Performance of neuro-fuzzy systems
\r\n\tEngineering Geology studies can be conducted during different stages of the project, as it can be conducted during the planning process, or the environmental impact analysis process, or the structural design process, or during construction operations in public and private projects, in addition to the stages of economic engineering, and the type of studies after completing construction of the facility. Geological engineering includes the following areas: geological risk assessment, geotechnical engineering, material properties, land slippage and slope risk, erosion, floods, seismic studies, and water displacement.
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Essa Georges Lwisa and Prof. Hasan Arman",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/8157.jpg",keywords:"Rocks, Plate Tectonic, Geo Hazards, Ground Subsidence, Land Slide, Mass Fabric, Ground Mass Description, Engineering Geophysics, Seismic Methods, Grout Treatment, Bentonite Suspension, Water Reservoirs",numberOfDownloads:56,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 29th 2020",dateEndSecondStepPublish:"June 19th 2020",dateEndThirdStepPublish:"August 18th 2020",dateEndFourthStepPublish:"November 6th 2020",dateEndFifthStepPublish:"January 5th 2021",remainingDaysToSecondStep:"7 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Technical assessor in the Emirates National Accreditation System (ENAS), a trainer at H&A Professional Development Advisory, a member of the American Society of Testing and Materials (ASTM), Society of Petroleum Engineers (SPE), and Society of Core Analysis.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"272012",title:"M.Sc.",name:"Essa",middleName:"Georges",surname:"Lwisa",slug:"essa-lwisa",fullName:"Essa Lwisa",profilePictureURL:"https://mts.intechopen.com/storage/users/272012/images/system/272012.jpg",biography:"Essa Georges Lwisa is a petroleum engineer, an expert in core analysis, rock properties, formation evaluation, and enhanced oil recovery. He is also an Auditor for standards (ISO 45001:2018), (ISO 19011:2018), and (ISO/IEC 17025:2017)\nHe works at the United Arab Emirates University- Chemical and Petroleum Engineering department since 2009 as a core analysis lab engineer, before that he worked at Core Laboratories Intl. as a SCAL analyst. \nEssa is a technical assessor in Emirates National Accreditation System (ENAS), a trainer at H&A Professional Development Advisory, a member of the American Society of Testing and Materials (ASTM), Society of Petroleum Engineers (SPE), and Society of Core Analysis. \nHe has participated in many conferences and published 17 scientific papers in respected journals, and won several awards. Recently, Essa is working on new research about using polar salts in enhanced oil recovery and water treatment. 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From 1992 to 1993, he was a Postdoc at the University of Nevada, Reno, USA. Between 1993 and 2008, he was a faculty member at Sakarya University, Civil Engineering Department, Turkey as Assistant and Associate Professor. He became a Professor at the same university in 2006. Dr. Arman has been teaching several different courses in undergraduate and graduate levels related to geology, environment, engineering, and energy. His research interests include soil and rock mechanics, engineering and environmental geology, environmental degradation, water resources, global warming, climate change, renewable and sustainable energy sources. 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Estimating the amount of effort required for developing a software system is an important project management concern, because these estimation is a basic for budgeting and project planning, which are critical for software industry. However accurate software estimation is critical for project success. So many software models have been proposed for software effort estimation. Algorithmic models such as COCOMO, SLIM, Multiple Regression, Statistical models,… and non-algorithmic models such as Neural Network Models (NN), Fuzzy Logic Models, Case-Base Reasoning (CBR), Regression Trees,… are some of these models. Here we want to improve software accuracy by integrating the advantages of algorithmic and non-algorithmic models. Also, recent research has tended to focus on the use of function point (FP) in estimating the software development efforts, but a precise estimation should not only consider the FPs, which represent size of the software, but also should include various elements of the development environment which affected on effort estimation. Consequently, for software development effort estimation by Neuro-Fuzzy approach, we will use of all the significant factors on software effort. So the final results are very accurate and reliable when they are applied to a real dataset in a software project.
\n\t\t\tThe empirical validation uses the International Software Benchmarking Standards Group (ISBSG) Data Repository Version 10 to demonstrate the improvement of results. This dataset contains information on 4106 projects of which two thirds were developed between the years 2000 and 2007. The evaluation criteria were based mainly upon MMRE (Mean Magnitude Relative Error), MMER and PRED(20). The results show a slightly better predictive accuracy amongst Fuzzy Logic Models, Neural Network Models, Multiple Regression Models and Statistical Models.
\n\t\t\tThis chapter of book is organized into several sections as follows: In section 1, we briefly review fuzzy logic models and neural network models in software estimation domain. Section 2 begins with preparing the dataset and this is followed by description of our proposed model. The experimental results are examined in Section 3 in details, and finally Section 4 offers conclusions and recommendations for future research in this area.
\n\t\tSince fuzzy logic foundation by Zadeh in 1965, it has been the subject of important investigations [Idri & Abran, 2001]. Fuzzy logic enhances the user’s ability to interpret the model, allowing the user to view, evaluate, criticize and possibly adapt the model. Prediction can be explained through a series of rules [Gray & MacDonell, 1997],[Saliu et al., 2004]. After analyzing the fuzzy logic model, experts can check the model to avoid the adverse effects of unusual data, thereby increasing its robustness. Additionally, fuzzy logic models can be easily understood in comparison to regression models and the neural network, thus making it an effective communication tool for management [MacDonell et al., 1999],[Gray & MacDonell, 1999]. In comparison to fuzzy logic, case-based reasoning is similarly easy to interpret, but it requires a high volume of data [Su et al., 2007].
\n\t\t\t\tThe purpose in this section is not to discuss fuzzy logic in depth, but rather to present these parts of the subject that are necessary for understanding of this chapter and for comparing it with Neuro-Fuzzy model. Fuzzy logic offers a particularly convenient way to generate a keen mapping between input and output spaces thanks to fuzzy rules’ natural expression. The number of fuzzy rules for six input variables and three membership functions is calculated by 36, which equals 729. As a result, writing these rules is an arduous task, so based on the statistical model we use two input variables which are demonstrated later. Implementing a fuzzy system requires that the different categories of the different inputs be presented by fuzzy sets, which in turn is presented by membership functions. A natural membership function type that readily comes to mind is the triangular membership functions [Moataz et al., 2005].
\n\t\t\t\tA triangular MF is a three-point (parameters) function, defined by minimum (a), maximum (c) and modal (b) values, that is MF(a, b, c) where a ≤ b ≤ c. Their scalar parameters (a, b, c) are defined as follows:
\n\t\t\t\tBased on the Correlation (r) of the variables, fuzzy rules can be formulated. Correlation, the degree to which two sets of data are related, varies from -1.0 to 1.0. The Correlation Coefficient for the input variables is calculated from the equation below [Humphrey, 2002]:
\n\t\t\t\tAn acceptable correlation should have an absolute value higher than 0.5. The fuzzy inference process uses the Mamdani Approach for evaluating each variable complexity degree when linguistic terms, fuzzy sets, and fuzzy rules are defined. Specifically, we apply the minimum method to evaluate the ‘and’ operation, and consequently, we obtain one number that represents the antecedent result for that rule. The antecedent result, as a single number, creates the consequence using the minimum implication method. Overall, each rule is applied in the implication process and produces one result. The aggregation using the maximum method is processed to combine all consequences from all the rules and produces one fuzzy set as the output. Finally, the output fuzzy set is defuzzified to a crisp single number using the centroid calculation method [Xia et al., 2007]. This Two-Input-One-Output fuzzy logic system for Effort is depicted in Figure 1. Moreover, the results of this model are shown in Table 7 and Table 9.
\n\t\t\tThe Fuzzy Logic System for Effort Estimation
Artificial neural network are used in estimation due to its ability to learn from previous data. In addition, it has the ability to generalize from the training data set thus enabling it to produce acceptable result for previously unseen data [Su et al., 2007]. Artificial neural networks can model complex non-linear relationships and approximate any measurable function so it is very useful in problems where there is a complex relationship between inputs and outputs [Aggarwal et al., 2005] [Huang et al.,2007].
\n\t\t\t\tWhen looking at a neural network, it immediately comes to mind that activation functions are look like fuzzy membership function [Jantzen, 1998].
\n\t\t\t\tOur neural network model uses an RBF network, which is easier to train than an MLP network. The RBF network is structured similarly to the MLP in that it is a multilayer, feed-forward network. However, unlike the MLP, the hidden units in the RBF are different from the units in the input and output layers. Specifically, they contain the RBF, a statistical transformation based on a Gaussian distribution from which the neural network’s name is derived [Heiat, 2002]. Since the data of our variables differs significantly, first, we normalized the data and then randomly divided them into two categories: 75% of projects are used for training and 25% of them are used for testing. The trajectory of the training phase is depicted in Figure 2. In particular, we used the Generalized Regression Neural Network Model in MATLAB 7.6, RBF network was created and the data set was applied to it; the results are shown in Table 7-\n\t\t\t\t\t9.
\n\t\t\tProgress of Training Phase
By comparison between artificial neural networks (ANN) and fuzzy inference systems (FIS), we find that neural network difficult to use prior rule knowledge, learning from scratch, they have complicated learning algorithms and they are black box structure and also they difficult extract knowledge while fuzzy inference systems can incorporate prior rule-base, they are interpretable by if-then rules, they have simple interpretation and implementation but they can’t learn linguistic knowledge and knowledge must be available. Therefore, it seems natural to consider building an integrated system combining the concepts of FIS and ANN modeling. A common way to integrate them is to represent them in a special architecture. Different integrated neuro-fuzzy models implement a Mamdani and Takagi Sugeno fuzzy inference systems, some of them are FALCON, ANFIS, NEFCON, NEFCLASS, NEFPROX, FUN, SONFIN, EFuNN, dmEFuNN and many others [Abraham, 2005].
\n\t\t\t\tDue to unavailability of source codes, we are unable to provide a comparison with all the models. In general Takagi-Sugeno fuzzy system has lower Root Mean Square Error (RMSE) than Mamdani-type fuzzy system but Mamdani fuzzy systems are much faster in compared to Takagi-Sugeno types, our purpose is accuracy so we didn’t consider mamdani-type fuzzy system such as FALCON, NEFCON, NEFCLASS, EFuNN. Since no formal neural network learning technique is used in FUN and it randomly changes parameters of membership functions and connections within the network structure, therefore we don’t consider it as a neuro-fuzzy system. About other models, Mackey & Glass [Mackey & Glass, 1977] provided a comparative performance of some neuro fuzzy systems for predicting the Mackey-Glass chaotic time series that represented in table 1.
\n\t\t\t\tSystem | \n\t\t\t\t\t\t\tEpochs | \n\t\t\t\t\t\t\tTest RMSE | \n\t\t\t\t\t\t
ANFIS | \n\t\t\t\t\t\t\t75 | \n\t\t\t\t\t\t\t0.0017 | \n\t\t\t\t\t\t
NEFPROX | \n\t\t\t\t\t\t\t216 | \n\t\t\t\t\t\t\t0.0332 | \n\t\t\t\t\t\t
EFuNN | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0.0140 | \n\t\t\t\t\t\t
dmEFuNN | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0.0042 | \n\t\t\t\t\t\t
SONFIN | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t0.0180 | \n\t\t\t\t\t\t
Performance of neuro-fuzzy systems
As shown in table ANFIS has the lowest RMSE in compared to NEFPROX (highest RMSE), SONFIN and dmEFuNN which used Takagi-Sugeno fuzzy system. So we use ANFIS as neuro-fuzzy model for predicting effort of software projects.
\n\t\t\tIn this study we used the latest publication of ISBSG (International Software Benchmarking Standards Group) data repository Release 10 that contains 4106 project’s information and two thirds of them were developed between the years 2000 and 2007. One hundred seven metrics were described for each project including data quality rating, project size, work effort, project elapsed time, development type, development techniques, language type, development platform, methodology, max team size,….
\n\t\t\t\tThe ISBSG data repository includes an important metric as Data Quality Rating which indicated that the reliability of the reported data. We excluded 141 projects with quality rating D which had little credibility. Project size is recorded with function points and homogeneity of standardized methodologies is very essential for measuring function size. Among different count approaches of function point NESMA is considered to produce equivalent results with IFPUG [NESMA 1996] and most of projects used these approaches for counting function points. So for giving more reliable results, projects with other counting approaches were excluded from the analysis. Also some projects had mistakenly information for example they had 0.5 or 0.95 for Average Team Size or Development Platform was recorded by ‘HH’ where not acceptable. Finally after cleaning data, 3322 projects remained for predicting effort’s projects.
\n\t\t\tOur study is based on statistical regression analysis, which is the most widely used approach for the estimation of software development effort. Now we briefly introduce the variables in data repository which will be used as the predicator for the regression analysis [Zhizhong et al.a, 2007]:
\n\t\t\t\tFunctional Size: It gives the size of the project which was measured in function points.
Average Team Size: It is the average number of people that worked on the project through the entire development process.
Language Type: It defines the language type used for the project such as 2GL, 3GL, 4GL and ApG. 2GL (two generation languages) are machine dependent assembly languages, 3GL are high-level programming languages like FORTRAN, C,etc. 4GL like SQL is more advanced than traditional high-level programming languages and ApG (Application Generator) is the program that allows programmers to build an application without writing the extensive code.
Development Type: Describes whether the software development was a new development, enhancement or Re-development.
Development Platform: Defines the primary development platform. Each project was developed for one of the platforms as midrange, mainframe, multi-platform, or personal computer.
Development Techniques: Specific techniques used during software development (e.g. Waterfall, Prototyping, Data Modeling, RAD, etc). A large number of projects make use of various combined techniques.
Case Tool Used: Indicates if the project used any CASE (Computer-Aided Software Engineering) tool or not.
How Methodology Acquired: Describes whether the development methodology was traditional, purchased, developed in-house, or a combination of purchased and developed.
It is important to point out that [Zhizhong et al b., 2007]:
\n\t\t\t\tWe did not take into account the factor primary programming language, since each particular programming language (Java, C, etc) belongs to one of the generation languages (2GL, 3GL, etc).
It is conceivable that senior software developers are more proficient and productive than junior developers. ISBSG data repository does not report this and assumes the developers are all well-qualified practitioners.
When considering the factor Development Techniques, there exist over 30 different techniques in the data repository and 766 projects even used various combinations of these techniques. Our study considered the ten key development techniques (Waterfall, Prototyping, Data Modeling, Process Modeling, JAD or Joint Application Development, Regression Testing, OO or Object Oriented Analysis & Design, Business Area Modeling, RAD or Rapid Application Development) and separated each of them as one single binary variable with two levels that indicates that whether this variable was used (1) or not (0), also other combinations were labeled by ‘Other’ as development factor technique.
The variables Effort, Size and Average Team Size are measured in ratio scales while all others are measured in nominal scales.
Here by fitting a model with Effort as the dependent variable and all the other variables as the predicators, we reduced our inputs for prediction, because for ANFIS with Genfis1 implementation is impossible to write all the rules and the complexity of model will be increased. So Regression Analysis helps us to use variables effectively. Table 2 gives the summary of the variables used for the regression analysis.
\n\t\t\t\tVariables | \n\t\t\t\t\t\t\tScale | \n\t\t\t\t\t\t\tDescription | \n\t\t\t\t\t\t|
Effort | \n\t\t\t\t\t\t\tRatio | \n\t\t\t\t\t\t\tSummary Work Effort | \n\t\t\t\t\t\t|
Size | \n\t\t\t\t\t\t\tRatio | \n\t\t\t\t\t\t\tFunctional Size | \n\t\t\t\t\t\t|
Average Team Size | \n\t\t\t\t\t\t\tRatio | \n\t\t\t\t\t\t\tAverage Team Size | \n\t\t\t\t\t\t|
Language Type | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\tLanguage Type | \n\t\t\t\t\t\t|
Development Type | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\tDevelopment Type | \n\t\t\t\t\t\t|
Development Platform | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\tDevelopment Platform | \n\t\t\t\t\t\t|
CASE Tool | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\tCASE Tools Used | \n\t\t\t\t\t\t|
Methodology | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\tHow Methodology Acquired | \n\t\t\t\t\t\t|
Development Techniques | \n\t\t\t\t\t\t\tWaterfall | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\t1=Waterfall , 0=Not | \n\t\t\t\t\t\t
Data | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\t1=Data Modelling , 0=Not | \n\t\t\t\t\t\t|
Process | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\t1=Process Modelling , 0=Not | \n\t\t\t\t\t\t|
JAD | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\t1=JAD , 0=Not | \n\t\t\t\t\t\t|
Regression | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\t1=Regression Testing , 0=Not | \n\t\t\t\t\t\t|
Prototyping | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\t1=Prototyping , 0=Not | \n\t\t\t\t\t\t|
Business | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\t1=Business Area Modelling , 0=Not | \n\t\t\t\t\t\t|
RAD | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\t1=RAD , 0=Not | \n\t\t\t\t\t\t|
O.O | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\t1=Object Oriented Analysis, 0=Not | \n\t\t\t\t\t\t|
Event | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\t1=Event Modelling , 0=Not | \n\t\t\t\t\t\t|
Other factors | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\t1=Uncommon Development Techniques, 0=Not | \n\t\t\t\t\t\t|
Missing | \n\t\t\t\t\t\t\tNominal | \n\t\t\t\t\t\t\t1=Missing , 0=Not | \n\t\t\t\t\t\t
Summary of the variables for Regression
The variable Missing was added as an indicator variable and indicate that the use of development techniques was recorded for particular project or not (1=recorded, 0=missing).
\n\t\t\t\tThe first step is automatic model selection based on Akaike’s information criterion (AIC). AIC is a measure of the goodness of fit of an estimated statistical model. Given the assumption of normally-distributed model errors, AIC is given as [Venables & Ripley, 2002]:
\n\t\t\t\tHere n is the number of observations, RSS is Residual Sum of Squares, and p is the number of parameters to be estimated. AIC has a penalty as a function of the number of estimated parameters because increasing the number of parameters improves goodness of fit (small RSS), so the preferred model is the one with the lowest AIC value. Based on this criterion, the preferred model with the lowest AIC value is introduced in Table 3.
\n\t\t\t\tIt is important to point out here that since the original data of Effort and Average Team Size also Effort and Size are extremely skewed, we take the natural log transformation (with base e) to make the data look normally distributed. In scatter plot between each two variables we can demonstrate that the relationship between them is close to linear. Accordingly we can apply linear model to investigate them.
\n\t\t\t\tRegression Terms | \n\t\t\t\t\t\t\tDf | \n\t\t\t\t\t\t\tSum of Square | \n\t\t\t\t\t\t\tAIC (if variable excluded) | \n\t\t\t\t\t\t
Log(Size) | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t140.6 | \n\t\t\t\t\t\t\t-161.7 | \n\t\t\t\t\t\t
Log(Team Size) | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t134.4 | \n\t\t\t\t\t\t\t-170.4 | \n\t\t\t\t\t\t
Language Type | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t\t22.2 | \n\t\t\t\t\t\t\t-357.5 | \n\t\t\t\t\t\t
Development Type | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\t14.2 | \n\t\t\t\t\t\t\t-371.1 | \n\t\t\t\t\t\t
Development Platform | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t\t13.8 | \n\t\t\t\t\t\t\t-373.8 | \n\t\t\t\t\t\t
Other | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1.9 | \n\t\t\t\t\t\t\t-393.7 | \n\t\t\t\t\t\t
RAD | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1.2 | \n\t\t\t\t\t\t\t-395.1 | \n\t\t\t\t\t\t
Regression Results Based on AIC
(The lowest value of AIC is -395.1)
\n\t\t\t\tAs regression based on AIC tends to overestimate the number of parameters when the sample size is large [Venables & Ripley, 2002], rely fully on the results produced by AIC is not suitable. So AIC should be combined with other statistical criterion such as ANOVA (ANalysis Of VAriance), here we used the ANOVA approach (based on Type І Sums of Squares) to test the significance of the variables. The variables added into the model in order and according to Table 3, the exclusion of the variable size results in the greatest increase of AIC value. Thus the project size factor is most significant to development effort likewise average team size is the second most important factor and etc. Based on Table 3 we can add the variable size to the regression model first, average team size, language type and so forth, then each time the regression was performed, the most insignificant variable was removed and then the model was refitted with the remained variables. By continuing this process we have the model with the final sets of significant terms where represented in Table 4 and significance level is based on p-value <0.05.
\n\t\t\t\tRegression Terms | \n\t\t\t\t\t\t\tDf | \n\t\t\t\t\t\t\tSum of Sq | \n\t\t\t\t\t\t\tF-Value | \n\t\t\t\t\t\t\tP-Value | \n\t\t\t\t\t\t
Log(Size) | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t497.8 | \n\t\t\t\t\t\t\t1026.2 | \n\t\t\t\t\t\t\t<10 -15 | \n\t\t\t\t\t\t
Log(Average Team Size) | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t173.7 | \n\t\t\t\t\t\t\t358.1 | \n\t\t\t\t\t\t\t<10 -15 | \n\t\t\t\t\t\t
Language Type | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t\t35.9 | \n\t\t\t\t\t\t\t24.7 | \n\t\t\t\t\t\t\t4.8 * 10 -15 | \n\t\t\t\t\t\t
Development Platform | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t\t16.3 | \n\t\t\t\t\t\t\t11.2 | \n\t\t\t\t\t\t\t3.8 * 10 -7 | \n\t\t\t\t\t\t
Development Type | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\t13.5 | \n\t\t\t\t\t\t\t13.9 | \n\t\t\t\t\t\t\t1.3*10 -6 | \n\t\t\t\t\t\t
RAD | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t2.7 | \n\t\t\t\t\t\t\t5.5 | \n\t\t\t\t\t\t\t0.019 | \n\t\t\t\t\t\t
Other | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t3.9 | \n\t\t\t\t\t\t\t8.1 | \n\t\t\t\t\t\t\t4.6*10 -3 | \n\t\t\t\t\t\t
Residuals | \n\t\t\t\t\t\t\t573 | \n\t\t\t\t\t\t\t277.9 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t |
ANOVA based on Type І Sums of Squares
(The significance level is based on P-level < 0.05)
\n\t\t\t\tBy comparing Table 2 and Table 3, we can see that the two methods produced similar significant factors for development effort, although the model based on AIC statistics overestimated additional two variables (OO and Missing) as significant. Considering that AIC tends to overestimate the number of parameters when the sample size is large, we accept the second model as most appropriate for our study. Summary of the regression results are shown in Table 5.
\n\t\t\t\tRegression Terms | \n\t\t\t\t\t\t\tCoefficients | \n\t\t\t\t\t\t\tStandard Error | \n\t\t\t\t\t\t\tP-Value | \n\t\t\t\t\t\t
Intercept | \n\t\t\t\t\t\t\t4.24 | \n\t\t\t\t\t\t\t0.30 | \n\t\t\t\t\t\t\t<10 -15 | \n\t\t\t\t\t\t
Log(Size) | \n\t\t\t\t\t\t\t0.56 | \n\t\t\t\t\t\t\t0.03 | \n\t\t\t\t\t\t\t<10 -15 | \n\t\t\t\t\t\t
Log(Average Team Size) | \n\t\t\t\t\t\t\t0.68 | \n\t\t\t\t\t\t\t0.04 | \n\t\t\t\t\t\t\t<10 -15 | \n\t\t\t\t\t\t
3GL’s Language | \n\t\t\t\t\t\t\t-0.40 | \n\t\t\t\t\t\t\t0.27 | \n\t\t\t\t\t\t\t0.136 | \n\t\t\t\t\t\t
4GL’s Language | \n\t\t\t\t\t\t\t-0.85 | \n\t\t\t\t\t\t\t0.27 | \n\t\t\t\t\t\t\t0.002 | \n\t\t\t\t\t\t
ApG’s Language | \n\t\t\t\t\t\t\t-0.71 | \n\t\t\t\t\t\t\t0.29 | \n\t\t\t\t\t\t\t0.014 | \n\t\t\t\t\t\t
Midrange Platform | \n\t\t\t\t\t\t\t-0.12 | \n\t\t\t\t\t\t\t0.08 | \n\t\t\t\t\t\t\t0.116 | \n\t\t\t\t\t\t
Multi-Platform | \n\t\t\t\t\t\t\t-0.15 | \n\t\t\t\t\t\t\t0.17 | \n\t\t\t\t\t\t\t0.379 | \n\t\t\t\t\t\t
PC Platform | \n\t\t\t\t\t\t\t-0.46 | \n\t\t\t\t\t\t\t0.08 | \n\t\t\t\t\t\t\t3.3*10 -8 | \n\t\t\t\t\t\t
New Development Type | \n\t\t\t\t\t\t\t0.29 | \n\t\t\t\t\t\t\t0.07 | \n\t\t\t\t\t\t\t1.6*10 -5 | \n\t\t\t\t\t\t
Re-development Type | \n\t\t\t\t\t\t\t0.56 | \n\t\t\t\t\t\t\t0.15 | \n\t\t\t\t\t\t\t2.4*10 -4 | \n\t\t\t\t\t\t
RAD | \n\t\t\t\t\t\t\t-0.23 | \n\t\t\t\t\t\t\t0.11 | \n\t\t\t\t\t\t\t0.027 | \n\t\t\t\t\t\t
Other | \n\t\t\t\t\t\t\t-0.27 | \n\t\t\t\t\t\t\t0.09 | \n\t\t\t\t\t\t\t0.005 | \n\t\t\t\t\t\t
Summary of the Regression results
It’s important to point that the default Language Type is 2GL, the default Development Platform is Mainframe, and the default Development Type is Enhancement. According to Table 5, the model is fitted as (the variable ‘Other’ is not useful and not included):
\n\t\t\t\ti=1, 2, 3, 4; j=1, 2, 3, 4; k=1, 2, 3
\n\t\t\t\tHere the function Ф is the indicator function with binary values of 1 or 0. A value of 1 means the relevant development technique in the parentheses is used, otherwise the value is 0. So the default techniques used are: 2GL for language type (α1=0), Mainframe for development platform (β1=0), and Enhancement for development type (γ1=0). The coefficients αi, βj, and γk can be obtained from Table5.
\n\t\t\t\tBy using the obtained coefficient, we assign a value to each variable in our database and these values are corresponding to these coefficients which are shown in Table5.
\n\t\t\t\tOur purpose was to apply ANFIS to prepared ISBSG database. Before using ANFIS, we need to have an initial FIS (Fuzzy Inference System) that determines the number of rules and initial parameters, etc. This can be done in three different ways: by using five of the GUI tools, by using Genfis1 that generates grid partition of the input space, and by using Genfis2 that employs subtractive clustering. In other words, if we have a human expert, we can use GUI tools to convert human expertise into rough correctly fuzzy rules, which are then fine-tuned by ANFIS. If we don’t have human experts, then we have to use some heuristics embedded in Genfis1 or Genfis2 to find the initial FIS and then go through the same ANFIS tuning stage. The question is that which of Genfis1 or Genfis2 should be used to generate the FIS matrix for ANFIS, and the answer is when you have less than six inputs and a large size of training data, use Genfis1 and otherwise use Genfis2. GENFIS1 uses the grid partitioning and it generates rules by enumerating all possible combinations of membership functions of all inputs; this leads to an exponential explosion even when the number of inputs is moderately large. For instance, for a fuzzy inference system with 10 inputs, each with two membership functions, the grid partitioning leads to 1024 (=210) rules, which is inhibitive large for any practical learning methods. The "curse of dimensionality" refers to such situation where the number of fuzzy rules, when the grid partitioning is used, increases exponentially with the number of input variables. However, GENFIS1 and GENFIS2 differ in two aspects. First, GENFIS1 produces grid partitioning of the input space and thus is more likely to have the problem of the ``curse of dimensionality\'\' described above, while GENFIS2 uses SUBCLUST (subtractive clustering) to produces scattering partition. Secondly, GENFIS1 produce a fuzzy inference system where each rule has zero coefficients in its output equation, while GENFIS2 applies the backslash ("\\") command in MATLAB to identify the coefficients. Therefore the fuzzy inference system generated by GENFIS1 always needs subsequent optimization by ANFIS command, while the one generated by GENFIS2 can sometimes have a good input-output mapping precision already. Any way since we have six inputs, Genfis2 and then ANFIS is used for our implementation. Also we divided our inputs in two categories and then Genfis1 was used for implementation because we want to compare our results with Fuzzy Model and this model is impossible to implement with six inputs because of its’ exponential rules. The other way for preparing FIS for ANFIS is using Genfis3 and its’ difference with Genfis2 is that Genfis3 use Fuzzy C-Means Clustering for Clustering inputs data and since our results are almost the same, we have arbitrarily used Genfis2.
\n\t\t\t\tFor implementation with two inputs, as we say we should divide our six inputs in two categories:
\n\t\t\t\tInputs which have the Ratio Scale such as Log(Size) and Log(Average Team Size) given as:
\n\t\t\t\tInputs which have the Nominal Scale such as Language Type, Development Platform, Development Type and RAD
\n\t\t\t\tThe ANFIS structure with six and two inputs are shown in Figure 3 and Figure 4 respectively.
\n\t\t\t\tANFIS Structure for Six Inputs
two-inputs Type ІІІ ANFIS with 9 Rules
These inputs and structures are for estimating effort of software projects, but for the elapsed time of software project studies shows that two inputs of log(Effort) and log(Average Team Size) are sufficient for estimating. So by using Genfis1, the subspace of ANFIS Structure is as shown in Figure 5.
\n\t\t\t\tcorresponding fuzzy subspaces with two inputs for time estimation
ANFIS uses a hybrid learning algorithm to identify parameters of Sugeno-type fuzzy inference systems. It applies a combination of the least-squares method and the back-propagation gradient descent method for training FIS membership function parameters to emulate a given training data set. More specifically, in the forward pass of the hybrid learning algorithm, functional signals go forward till layer 4 and the consequent parameters are identified by the least squares estimate. In the backward pass, the error rates propagate backward and the premise parameters are updated by the gradient descent. Hybrid learning rule can speed up the learning process and has less error than gradient descent method. Table 6 summarizes the activities in each pass.
\n\t\t\t\tBackward Pass | \n\t\t\t\t\t\t\tForward Pass | \n\t\t\t\t\t\t\t- | \n\t\t\t\t\t\t
gradient descent | \n\t\t\t\t\t\t\tfixed | \n\t\t\t\t\t\t\tPremise Parameters | \n\t\t\t\t\t\t
Fixed | \n\t\t\t\t\t\t\tLeast Squares Estimate | \n\t\t\t\t\t\t\tConsequent Parameters | \n\t\t\t\t\t\t
Error rates | \n\t\t\t\t\t\t\tNode outputs | \n\t\t\t\t\t\t\tSignals | \n\t\t\t\t\t\t
Two passes in the hybrid learning procedure for ANFIS
In Figure 6 we demonstrate the membership functions of FIS for time estimation, and Figure 7 shows an output of ANFIS for Time Estimation.
\n\t\t\t\tmembership function of FIS for Time Estimation after ANFIS’s Training
ANFIS output for Time Estimation
We employ the following criteria to assess and compare the performance of effort estimation models. A common criterion for the evaluation of effort estimation models is the relative error (RE) or the magnitude of relative error (MRE), which is defined as [Huang et al., 2007]:
\n\t\t\t\tThe RE and MRE values are calculated for each project i whose effort is predicted. For N multiple projects, we can also use the mean magnitude of relative error (MMRE) [Huang et al., 2007]:
\n\t\t\t\tIntuitively, MER seems preferable to MRE since MER measures the error relative to the estimate. Here we used this. The MER is defined as follows [Lopez-Martin et al., 2008]:
\n\t\t\t\tThe MER value is calculated for each observation i whose effort is predicted. The aggregation of MER over multiple observations (N) can be achieved through the mean MER (MMER) as follows [Lopez-Martin et al., 2008]:
\n\t\t\t\tAnother criterion that is commonly used is the prediction at level p:
\n\t\t\t\tWhere k is the number of projects where MRE is less than or equal to p. here we used Pred(25).
\n\t\t\t\tIn general, the accuracy of an estimation technique is Proportional to Pred(p) and inversely proportional to MMRE and MMER. Any way we used all of these criterions for evaluation of software techniques.
\n\t\t\t\tAlso the other criterion is coefficient of determination (R2). Coefficient of determination is used to assess the quality of the estimation models and expressed by R2. The coefficient R2 is calculated by [Gu et al., 2006]:
\n\t\t\t\tHere, \n\t\t\t\t\t
A software tool (MATLAB 7.6) was used to simulate fuzzy logic system, neural network model and neuro-fuzzy model. Three categories of results are as below:
\n\t\t\t\tFirst category: Effort Estimation with two inputs data as we discussed above. The results are gathered in Table 7 which showed that Neuro-Fuzzy model has 96% data with less than 20% error. As shown in Figure 8 just four data had more than 25% error and most of them had less than 7% error.
\n\t\t\t\tSince we just have two inputs, we implement ANFIS by Genfis1.
\n\t\t\t\tMMER | \n\t\t\t\t\t\t\tMMRE | \n\t\t\t\t\t\t\tAverage Error | \n\t\t\t\t\t\t\tPred (20) | \n\t\t\t\t\t\t\tEstimation Models | \n\t\t\t\t\t\t
0.05 | \n\t\t\t\t\t\t\t0.05 | \n\t\t\t\t\t\t\t0.40 | \n\t\t\t\t\t\t\t0.96 | \n\t\t\t\t\t\t\tNeuro -Fuzzy Model | \n\t\t\t\t\t\t
0.13 | \n\t\t\t\t\t\t\t0.12 | \n\t\t\t\t\t\t\t0.91 | \n\t\t\t\t\t\t\t0.89 | \n\t\t\t\t\t\t\tFuzzy Logic Model | \n\t\t\t\t\t\t
0.07 | \n\t\t\t\t\t\t\t0.04 | \n\t\t\t\t\t\t\t0.39 | \n\t\t\t\t\t\t\t0.88 | \n\t\t\t\t\t\t\tNeural Network Model | \n\t\t\t\t\t\t
0.14 | \n\t\t\t\t\t\t\t0.12 | \n\t\t\t\t\t\t\t0.90 | \n\t\t\t\t\t\t\t0.78 | \n\t\t\t\t\t\t\tMultiple Regression Model | \n\t\t\t\t\t\t
Implementation Results for Effort Estimation with two inputs
MMRE Results for ANFIS with two inputs for Effort Estimation
Second Category: due to the number of inputs we implement ANFIS by Genfis2 here. As we mentioned before the Fuzzy Model is impossible to implement in this category due to large number of inputs, so we have nothing in that row. Here we also had the best results for Neuro-fuzzy Model, these results were shown in Table 8 and were demonstrated in Figure 9.
\n\t\t\t\tMMER | \n\t\t\t\t\t\t\tMMRE | \n\t\t\t\t\t\t\tAverage Error | \n\t\t\t\t\t\t\tPred (20) | \n\t\t\t\t\t\t\tEstimation Models | \n\t\t\t\t\t\t
0.05 | \n\t\t\t\t\t\t\t0.05 | \n\t\t\t\t\t\t\t0.38 | \n\t\t\t\t\t\t\t0.95 | \n\t\t\t\t\t\t\tNeuro -Fuzzy Model | \n\t\t\t\t\t\t
It’s impossible to implement, Due to very large rule set. | \n\t\t\t\t\t\t\tFuzzy Model | \n\t\t\t\t\t\t|||
0.11 | \n\t\t\t\t\t\t\t0.11 | \n\t\t\t\t\t\t\t0.73 | \n\t\t\t\t\t\t\t0.89 | \n\t\t\t\t\t\t\tNeural Network Model | \n\t\t\t\t\t\t
0.07 | \n\t\t\t\t\t\t\t0.07 | \n\t\t\t\t\t\t\t0.50 | \n\t\t\t\t\t\t\t0.95 | \n\t\t\t\t\t\t\tMultiple Regression Model | \n\t\t\t\t\t\t
0.07 | \n\t\t\t\t\t\t\t0.08 | \n\t\t\t\t\t\t\t0.51 | \n\t\t\t\t\t\t\t0.94 | \n\t\t\t\t\t\t\tStatistical Model | \n\t\t\t\t\t\t
Implementation Results for Effort Estimation with six inputs
MMRE Results for ANFIS with six inputs for Effort Estimation
As shown in Figure 7, most of estimations had less than 5% error and this emphasized that the performance of this model is better than the others.
\n\t\t\t\tThird category: Time estimation with two inputs: log (Effort) and log(Average Team Size). The obtained results are organized in Table 9.
\n\t\t\t\tMMER | \n\t\t\t\t\t\t\tMMRE | \n\t\t\t\t\t\t\tAverage Error | \n\t\t\t\t\t\t\tPred (20) | \n\t\t\t\t\t\t\tEstimation Models | \n\t\t\t\t\t\t
0.2456 | \n\t\t\t\t\t\t\t0.2594 | \n\t\t\t\t\t\t\t0.4161 | \n\t\t\t\t\t\t\t0.5103 | \n\t\t\t\t\t\t\tNeuro -Fuzzy Model | \n\t\t\t\t\t\t
0.3291 | \n\t\t\t\t\t\t\t0.3435 | \n\t\t\t\t\t\t\t0.5561 | \n\t\t\t\t\t\t\t0.3913 | \n\t\t\t\t\t\t\tFuzzy Logic Model | \n\t\t\t\t\t\t
0.3032 | \n\t\t\t\t\t\t\t0.3266 | \n\t\t\t\t\t\t\t0.5295 | \n\t\t\t\t\t\t\t0.4119 | \n\t\t\t\t\t\t\tNeural Network Model | \n\t\t\t\t\t\t
0.2640 | \n\t\t\t\t\t\t\t38.02 | \n\t\t\t\t\t\t\t0.4225 | \n\t\t\t\t\t\t\t0.5149 | \n\t\t\t\t\t\t\tMultiple Regression Model | \n\t\t\t\t\t\t
Implementation Results for Time Estimation
\n\t\t\t\t\tFigure 10 demonstrated that most of results had less than 3% error and it’s pointed that this model is very accurate for prediction.
\n\t\t\t\tMMRE Results of ANFIS for Time Estimation
The value of coefficient of determination (R2) for ANFIS is equal to 0.9828 which indicated that more than 98 % of the variance in dependent variable can be explained by this model thus that’s confidenceable.
\n\t\t\t\tThe comparison plots of these models for Time and Effort estimation are shown in Figure 11 and 12 respectively.
\n\t\t\t\tComparison plot for Time Estimation
Comparison plot for Effort Estimation
As software development has become an essential investment for many organizations, software estimation is gaining an ever-increasing importance in effective software project management, quality management, planning, and budgeting.
\n\t\t\tThe primary purpose of this study was to propose a precise method of estimation that takes account of and places emphasis on the various software development elements. We compared this neuro-fuzzy based software development estimation model with four other models such as neural network models, fuzzy logic models, multiple regression models, and statistical models.
\n\t\t\tThe main benefit of this model is its good interpretability by using the fuzzy rules. Another great advantage of this research is that they could put together expert knowledge (fuzzy rules), project data and the traditional algorithmic model into one general framework that may have a wide range of applicability in software effort and time estimation. Also recent researches have tended to focus on the use of function points (FPs) in estimating the software development efforts and FPA (Function Point Analysis) assumes that the FP is the only factor which influences software development effort, however, a precise estimation should not only consider the FPs, which represent the size of the software, but should also include various elements of the development environment for its estimation. The factors significant to software development effort are project size, average number of developers that worked on the development, type of development, development language, development platform, and the use of rapid application development which are used for estimation although FP as a software size metric is an important topic in the software prediction domain.
\n\t\t\tAs a result of comparison, the effort and time estimation model, which is based on the neuro-fuzzy techniques, showed superior results in predictability than the other models mentioned in this study.
\n\t\t\tThis study worked on the latest release of ISBSG data repository which is very large database recording 4106 software projects developed worldwide. Also for comparison of software development techniques we used three evaluation criteria: MMRE (Mean Magnitude Relative Error), MMER and Pred(20).
\n\t\t\tThe proposed model has 98% coefficient of determination (R2) which emphasize on the best performance of our proposed approach.
\n\t\t\tSome limitations in this domain are:
\n\t\t\tEstimation of time and effort in earlier phase of software development is very difficult and it depends on lower level of estimation such as Size Estimation which is done by using External Inputs (EI), External Outputs (EO), External Queries (EQ), Internal Logical Files (ILF), and External Interface Files (EIF).
Many existing research papers have proposed various effort estimation techniques and they still do not have an agreement which technique is the best across different cases.
Also we don’t have any dynamic learning algorithm for our model to adopt itself with any situation and completed our database in each estimation time. By adding the process maturity in effort estimation models as an input factor, we can improve the accuracy of estimation models.
This limitation gives us motivation to continue this research in our future work.
\n\t\t3D printing is an additive manufacturing (AM) process that enables the manufacturing of components with complex geometries in a layer-by-layer fashion. 3D printing became popular after the first machine was introduced to the market in 1986 by Hull [1]. Charles Hull created the first stereolithography (SLA) manufacturing method which he used for the rapid design and manufacturing of small prototype plastic parts. Stereolithography uses light to activate polymers within a resin (photopolymerization) to create 3D, complex shapes [2, 3]. This SLA system was commercialized in 1987 by the company 3D Systems. Since this breakthrough invention, there has been great effort in producing machines that can process a variety of plastics. Some of the machines currently in the market are fused deposition modeling (FDM) [4, 5] and direct ink write (DIW) for extrusion-based processes [6, 7]. Powder bed fusion (PBF) and laser sintering (SLS) are used for processes requiring a laser to cure or fuse polymeric materials [8]. Inkjet printers also use light to photopolymerize ink drops into complex shapes [9]. Extensive reviews on these processing and 3D printing technologies have been published elsewhere [4, 5, 10, 11, 12, 13, 14]. This chapter focuses on applications that use AM for the 3D printing of polymeric materials.
\nSince the 1980s, 3D printing has become very popular as a result of the rapid manufacturing of components with architectures designed to meet specific applications. AM allows for the manufacturing of a variety of shapes in a layer-by-layer fashion, often without the need of post-processing such as machining. As a general scheme, AM starts with the design of a virtual object using CAD (computer-aided design) software that generates a STL (stereolithography, named after Charles Hull’s SLA process) file format [15]. A slicer program interprets the STL file and converts it into g-code (e.g. Slic3r, 3DPrinterOS, MakerBot Print, and others). The computer controls the stage and dispenser of the 3D printer allowing prototypes to be manufactured. Rapid prototyping allows one to refine product ideas while saving significant time and money because it allows for iterations prior to creating a final product. Optimization via an iterative process involves touching and feeling the prototype, in real time, in order to finalize the shape and geometry, leading to a final product. Characterization methods during iterations and on the final design include optical microscopy, SEM, and mechanical tests. Others methods, such as bio-compatibility (cell-adhesion and proliferation) and electrical performance are performed depending on the application. Figure 1 demonstrates a general scheme for the AM process. Despite the many advances in AM, the technology still has many challenges that need to be addressed. These challenges are related to the speed of the processes (which in many cases is slower than injection molding processes and machining), cost of the machines, and limited feedstock. However, advantages outweigh the challenges due to the fact that AM allows for compositional flexibility, complex macro and microstructures, and easy modeling and optimization. As a result, industries including biomedical engineering, transportation, and the military have adopted AM as the main manufacturing method for the printing of prototypes and final parts [16, 17].
\nGeneral scheme for the use of additive manufacturing processes, from the choice of material to the final product. The 3D printing of parts involves the use of a computer-assisted design software that generates a STL file format that is then sliced and formatted into gcode. The computer controls the stage and dispenser to generate materials with specific architectures, e.g. faced-centered tetragonal cushion using direct ink writing (a) and diamond structure using FDM (b).
Careful attention is imperative when choosing a material to print a given part. While there are a variety of commercially available polymers, not one polymer is inclusive and will give one the properties needed for a specific application. Furthermore, a single AM technique is not capable of printing any one individual polymer available in the market. The selection of material depends on the application and the customers’ needs. Figure 2 lists the decision criteria for the selection of a material. One must take into consideration the environment at which the part will be exposed and the properties required (e.g. temperature, mechanical load, humidity, chemical exposure, radiation, UV light), the processability, 3D printing method, and availability.
\nMaterial selection chart for product design and manufacturing.
Polymers have become consumer goods, for they are used to manufacture bottles, toys, tools, bags, phones, computers, tools, cushions, electronics and transportation components [18]. Thus, it makes sense that efforts have focused on developing materials that can be 3D printed, which allows for rapid manufacturing [2, 3, 4, 17]. Table 1 lists commercially available polymers used in some of the AM processes. Polycarbonate (PC), acrylonitrile butadiene styrene (ABS), poly ether ester ketone (PEEK), polyetherimide (ULTEM) and Nylon are common polymers used in processes requiring thermoplastics, or plastics that are processed by heating to a semi-liquid state and close to the melting point. Upon extrusion, the printed layers fuse and solidify. AM techniques that use thermoplastics are Fused-Deposition Modeling (FDM), Jetting (InkJet), and Selective Laser Sintering (SLS). SLA and Direct Ink Writing (DIW) use thermosetting polymers in their liquid state, or polymers that become solids after curing. A chemical reaction occurs prior to the melting point, resulting in a solid-state material. In SLA and DIW, polymers are formulated to meet specific properties, most importantly rheological. For example, each layer should be self-supporting and should allow for the printing of multiple layers while retaining the designed geometry [14, 19, 20, 21]. Rheologically, this corresponds to a resin that has a yield stress at high oscillatory stresses, such that the resin is solid-like at rest (low stress) and liquid like during flow (high stress) [7]. One of the main challenges in the polymer 3D printing industry is the limited feedstock available for purchase. Polymers listed in Table 1 cannot be used in all applications. Particularly, polymers in the pure state lack mechanical strength for load-bearing applications. The addition of fillers, such as silica [22, 23] and carbon fibers [24, 25], is often used to generate materials with high mechanical strength. Furthermore, the incorporation of additives enhances materials properties by adding functionality to the parts that include getter [20], UV and radiation resistance [26, 27, 28], and anti-fouling properties [29, 30, 31].
\nAM technology | \nProcess | \nPhysical state of starting material | \nFeedstock | \n
---|---|---|---|
FDM | \nMelting-solidifying | \nSolid | \nPC, ABS, PLA, ULTEM, Nylon, Carbon-filled Nylon, ASA | \n
SLA | \nPhotocuring | \nLiquid | \nThermosetting- acrylates and epoxy | \n
SLS | \nMelting-solidifying | \nSolid | \nPCL, PLA | \n
Jetting | \nPhotocuring | \nSolid | \nABS, ASA, PCL, PLA, Vero | \n
Direct Writing | \nExtrusion-heat/UV curing | \nliquid | \nThermosetting- any material with adequate viscosity | \n
List of polymers used for 3D printing applications.
The biomedical market represents 11% of the total AM market share today, and will be a strong driver for AM development and growth [32]. Since the early 2000s, there has been increased interest in using 3D printing to fabricate hard tissues (bones, teeth, cartilage) and soft tissues (organs, skin, and others) [2, 3, 4, 16, 33]. The manufacturing of prostheses and scaffolds with complex geometries is especially important for regenerative medicine, where a porous scaffold is implanted into the patient to serve as a template for tissue to regenerate while the implant degrades slowly in the body. Other implants need to stay in place for the lifetime of the patient. 3D printing allows for the rapid manufacturing of customized prosthetics and implants with controlled architectures. The structure can be designed through the translation of x-ray, MRI, and CT images into STL file formats. The STL file can be processed by software and a design can be generated based on the patient’s specific needs. Metals are commonly used to generate prosthetics for bone reconstruction. ABS and PLA are the most suitable non-biodegradable polymers used for the manufacturing of scaffolds. However, materials used in medicine must enable cell adhesion, growth, and differentiation. Current feedstock for biomaterials is limited to collagen, gelatin, fibrin, and chitosan, which are similar to natural tissue, have high affinity to cells and are highly hydrated. The main challenge with these soft natural polymers is their low mechanical strength [33]. In biomedical engineering, the main focus has been on the development of biopolymeric materials for tissue and scaffold generations with improved flexibility, strength, and patient compatibility in order to prevent implant rejection and toxicity. Some polymeric mixtures include living cells isolated from the patient and grown in the laboratory. These types of polymers are often hydrogels suitable for ink jet 3D printing technologies. Table 2 shows various polymers used for biomedical applications. Some examples of biomedical devices developed using 3D printing are implants, prosthetics, dental, orthodontics, hearing aids, and drug release tissues.
\nMaterial | \n3D printing techniques | \nComments | \n
---|---|---|
PLA, PCLA, PLGA | \nFDM | \nScaffolds. Biodegradable. Can add fillers, e.g. HA, for improved cell adhesion and mechanical properties | \n
Collagen, alginate, PEG, fibrin, chitosan | \nInkjet, extrusion | \nBiodegradable scaffolds. Can add fillers and cells for improved cell adhesion and mechanical properties | \n
PCL, methacrylate copolymers | \nSLS | \nBiodegradable scaffolds. Improved mechanical properties | \n
Polymers and processes used for the additive manufacturing of biomedical devices.
Polymers used for tissue and organ fabrication need to have various functions in order to (1) allow for cell attachment and migration, (2) transfer growth factors and waste products, (3) maintain its shape while cells are growing and (4) maintain adequate mechanical properties. Wu et al. [34] reported the generation of a biopolymeric material based on chitosan dissolved in an acid mixture of acetic acid, lactic acid, and citric acid. This biomaterial was 3D printed using an ink-writing technique, then dried under vacuum and neutralized to remove any acid residue. The structure of the scaffold was characterized using confocal laser scanning microscopy and the images showed wrinkles attributed to the volume change. Tensile mechanical tests show that the printed material exhibits a strain to failure of 400% under tensile load and a 7.5 MPa ultimate strength when in its neutralized form. Furthermore, the 3D printed material allows for excellent cell adhesion, growth, and proliferation, as demonstrated using the Live-Dead staining method, fluorescence microscopy, and SEM.
\nLuo et al. [35] reported the 3D printing of a bioceramic hollow struts-packed scaffold using an extrusion typ. 3D printer and a shell/core nozzle. The ink contained Ca7Si2P2O16, alginate and Pluronic F-127. After printing, the ink was dried overnight and sintered for 3 hours at 1400°C to remove the alginate and F-127 materials. The morphology was analyzed using an optical microscope. The micropores and the microstructure of the pores were characterized using SEM. The fabricated scaffolds (16/23 shell/core size) were subjected to mechanical testing and exhibited a compressive strength of 5 MPa, comparable to cancellous bone (2–12 MPa), and a modulus of 160 MPa. The scaffold had high porosity (65–85%), adjusted with the core/shell size nozzles. The high porosity and surface area (up to 6500 mm2/g) allowed for cell adhesion and proliferation on the outer and inner surface of the scaffold, as determined by SEM. Finally, the in-vivo bone formation study in a rabbit demonstrated that the bioceramic implant allows for good cell integration and bone formation was detected with micro-CT.
\nLewis’ team at Harvard University 3D printed a tympanic membrane scaffold composed of PDMS, PLA, and PCL based materials using a DIW technique [36]. The team demonstrated that it is possible to design and fabricate materials with similar properties when compared to human specimens. The high frequency displacement and acoustics were organized by concentric rings for each 3D printed graft, and it was very dependent on the patterns and mechanical properties, characterized via digital opto-electronic holography, laser Doppler vibrometry, and dynamic mechanical analysis. In a different study, the team 3D printed cellular materials with vascular networks for flow [37]. The 3D printed structure was fabricated using an ink composed of Pluronic F-127, GelMA (gelatin methacrylate to allow for UV curing) and fibroblast cell culture. After curing, the Pluronic F-127 was removed by cooling to 4°C, yielding open channels that represent the vascular networks. Lewis’ team demonstrated that blood and other cellular liquids can flow through the channels with minimal death of cells.
\nPatients with skin burns and thick wound injuries often suffer from long term recovery and extensive and expensive treatments. The autologous split-thickness skin graft (ASSG) is the technique most often used to treat large wounds [38]. A skin tissue is place in the injured area and assists with the wound closure and healing. This technique relies on the removal of a piece of skin from a different part of the patient’s body and reapplying it on the place of injury. The drawback with ASSG is that it is limited by the size of donor sites and also creates another place of injury [38]. 3D printing of biomaterials would alleviate the problems related to ASSG. Skin cells are cultured in a laboratory and mixed with biocompatible polymers for bioprinting. In 2012, Koch Singh et al. [39] reported the 3D printing of skin using a laser-based inkjet printing method. The inks were composed of blood plasma/alginate solution and fibroblast/keratinocytes/collagen biomaterials. Collagen is the main component of the extracellular matrix (ECM) in skin. The team proved that the laser-based printing method does not harm the cells by performing proliferation of the cells in histologic sections 10 days after printing. Ki-67 staining, which includes the protein present in cells during their active cell cycle phases, shows that proliferating cells can be found in all regions, verifying vitality. In addition, a build-up of basal lamina, cell adhesion and proliferation- sign of tissue generation was observed.
\nThe dental industry is taking advantage of 3D printing technologies for restoratives, implants, and orthodontics purposes. Currently, professionals in the dental field have access to 3D printers and it is possible to print designs in a clinical environment. A CT scan is used to generate a defined shape based on the patient’s morphology and quickly fabricate and replace a missing tooth [40]. 3D printing is used for the manufacturing of aligners, braces, dental implants, and crowns [40]. Biocompatible materials are used for the fabrication of dental parts using 3D printing, e.g. polylactic acid, polycaprolactone and polyglycolide, and acrylates [3]. It is possible to fabricate dental implants with antibacterial properties by the incorporation of additives, such as quaternary ammonium salts [41, 42, 43]. At the age of 23, Amos Dudley fabricated his own orthodontic aligners while he was a student at New Jersey Institute of Technology [44]. He used equipment available at the institute to scan and print models of his teeth. A non-toxic plastic was used to mold and eventually generate 12 clear aligners. Amos had access to a Stratasys Dimension 1200 3D printer and used a mixture of alginate powder and PermaStone as the resin to print the aligners, which were tested by fitting them on his teeth. While it was not a trivial problem to solve, Amos proved the ability of 3D printing orthodontic materials for teeth alignment.
\nAM has been widely used in the biomedical industry and will continue to impact work in the future. Some challenges will persist, such as regulatory issues, limited materials, and inconsistent quality [45]. AM biomedical products require FDA approval, which can be time consuming and difficult to obtain [46]. Biocompatibility will require the development of new techniques and materials to produce high quality, high performing AM materials [47]. Furthermore, mechanical properties of AM materials need to be well assessed such that final properties can have reliable and reproducible behaviors. Further development for on-demand and patient-specific applications will be exciting work in this field. For example, designing patient-specific implants following a CT-scan will result in quick results [48]. Complex parts with specific mechanical properties and biocompatibility can be constructed on demand and with multifunctional components if needed. AM Research and development may help to improve bio-printed scaffolds and tissues for clinical applications to reduce cost for tissue engineering [49]. Manufacturing AM artificial organs, which includes multifunctionality (i.e. bionic ear [50]), will revolutionize the field of 3D printing for biomedical applications.
\nOne of the most promising fields in the future of AM is the aerospace industry. According to Wohlers’ report, this industry account for almost 20% of the total AM market today [32]. Aerospace applications typically require light weight and high strength materials. The importance of AM relies on the reduced cost, increased flexibility of design, and increase in a variety of products to meet customer needs. Additive manufacturing is an important technology that enables the design and manufacturing of complex structured products with improved mechanical strength and lower weight, at a lower cost and reduced lead-time. The aerospace industry has replaced the conventional manufacturing methods of molding and machining with 3D printing technology for small scale production. At a small production scale, AM offers effectively low-cost design and assembly [17].
\nThe aerospace industry implemented the use of AM approximately 20 years ago [51]. The main use for 3D printing has been focused on prototyping, modeling and producing jigs, fixtures and tools [17]. Furthermore, AM is used to build replacement parts on-demand when required. The ability to build on-demand spare components reduces costs for the production of parts that may never be used due to them becoming obsolete to new technology, which also saves warehouse storage space. For example, BAE Systems is currently 3D printing window breather pipes used in jetliners [52]. These pipes cost 40% less than pipes manufactured using injection molding processes and are manufactured on an as-needed basis.
\nRecently, NASA designed a rover, named Desert RATS, that can support humans in a pressurized cabin in space [53]. The rover is intended to transport humans to Mars. It contains 70 3D printed parts that include flame-retardant vents and housings, camera mounts, large pod doors, front bumpers, complex electronics, and others. The materials used for the 3D printing of the part used in the rover were ABS, PCABS and PC, and were printed using a FDM Stratasys 3D printer. Piper Aircraft manufactures tools using PC that can withstand hydroforming pressures of 3000 to 6000 psi. Aurora Flight Science additively manufactured wings that weigh one third of the fully dense metal components [54]. Some wings have integrated electronics. Lepron generated 200 different designs for use in piloted helicopters [17]. It is foreseen that aerospace companies will replace small components with 3D printed parts, thus reducing the weight of the machines. Some examples are arm rests, seat belts, food trays, and many others [17].
\nCompanies have adopted AM for fast production without making substantial changes to their products [17]. This modification is mostly due to the fast-changing market and low cost of generating such small builds. Several challenges would have to be overcome to facilitate the growth of AM. Some of these challenges include: (1) current speed of AM machines is slow for bulk production; (2) few polymeric material options; and (3) current machines do not allow for the manufacturing of large components [17, 55]. In the future, it is expected that companies will pursue a completely different business model by performing product customization for end-product while maintaining the on-demand part supply. Future work will focus on the development of multifunctional structures with complex geometries, which allows for novel solutions for complicated problems. AM techniques, such as using functionally graded materials, can be used in order to tailor the mechanical and/or thermal response of components [56]. Furthermore, on-demand manufacturing will reduce costs and eliminates potential damage caused by storage [45].
\nElectronic devices require suitable mechanical, geometrical, and optical functionalities to allow for miniaturization, low energy consumption, and smart capabilities [57]. The production of prototypes and end-products has to rapidly change due to the fast-changing technology. The conventional method for manufacturing electronic devices is using subtractive methods that involve masking and etching of sacrificial materials [58]. AM allows for the reduction of material waste, energy consumption and processing time and steps. 3D printing is being used to substitute steps for mounting and assembling electronic devices [59]. The additive process deposits material in a controlled layer-by-layer process allowing the manufacturing of complex geometries and dimensions. In addition, it enables 3D orientation of important components to improve performance. With miniaturization, AM allows for the manufacturing of small parts that would otherwise be difficult to obtain. AM has found application for thin films [60], inductors [61], solar cells [62], and others. The most common 3D printing techniques for electronics are inkjet and direct writing of conductive inks.
\nJennifer Lewis and colleagues fully 3D printed a quantum-dot (QD) light-emitting diode (LED) system, including green and orange-red light emitters embedded in a silicone matrix [63]. The printed device exhibits a performance of 10–100-fold below the best processed QD-LEP but could potentially be optimized with the addition of an electron-transport layer. A copper nanoparticle stabilized with polyvinyl pyrrolidine was mixed with 2-(2-butoxyethoxy)ethanol to prepare ink for inkjet printing [64]. The ink was printed onto a polyimide subtracted and sintered at 200°C. The prepared electronic device resulted in low electrical resistivity (≥ 3.6 μΩcm, or ≥ 2.2 times the resistivity of bulk copper). Bionic ears were printed using an inkjet printer [50]. The inks were composed of cell-cultured alginate and chondrocytes hydrogel matrix and a conductive polymer consisting of silicone and silver nanoparticles. The 3D printed ears exhibit enhanced auditory sensing for radio-frequency reception allowing the ear to listen to stereo music. This result demonstrates that bioengineering and electronics can be merged, resulting in advanced technologies. Students from Northwest Nazaren University and Caldwell High School designed the 3D printed CubeSat [65]. The CubeSat was launched aboard Delta II rocket as part of a NASA mission in 2013. It carries miniaturized electronics and sensors and is intended to collect real-time data on the effects of the harsh environments of space (oxygen, UV, radiation, temperature and collisions) on the polymeric materials- ABS, PLA, Nylon, and PEI/PC ULTEM.
\nFuture research and development in the electronics field will take advantage of low cost methods, flexibility in design, and fast speed of 3D printers for designing and prototyping new products. For example, printing circuit boards will offer superior accuracy and flexibility, with potential cost savings, environmental impacts, faster production times, and increased design versatility. Furthermore, adaptive 3D printing, which takes advantage of a closed-loop method that combines real-time feedback control and DIW of functional materials to construct devices on dynamic surfaces, is an exciting field of research [66]. This method of 3D printing may lead to new forms of smart manufacturing technologies for directly printed wearable devices. New possibilities will emerge in the wearable device industry, in biological and biomedical research, and in the study and treatment of advanced medical treatments.
\nUnsurprisingly, the amount of plastic pollution on the planet is alarming [67]. Plastics have dominated our marketplace due to their utility and versatility and make up at least 10% by mass of our waste streams. Plastics are designed to be durable and to withstand harsh environmental conditions. Therefore, the amount of plastic waste is only expected to increase in the future. Currently, 91% of plastic is not being recycled. The negative impact plastics have on our ecosystem is well recognized and researchers are using this as a business model and opportunity [68, 69]. Considerable efforts are being placed on recycling and reusing plastic waste. Prof. Sahajwalla at the University of New South Wales Sydney and her team work on turning plastic waste into usable polymers, including 3D printing polymers [70]. The company Reflow is collecting polyethylene terephthalate (PET) waste bottles and turning them into filaments suitable for 3D FDM printers [71]. A company in Belgium, Yuma, is using recycled plastics for the 3D printing of sunglasses [72]. The U.S. Army Research Laboratory and the U.S. Marine Corps are working together to repurpose plastic waste by printing items from recycled plastic useful for soldiers [73]. This process allows for a decrease in transportation costs and manufacturing of parts on demand. This large effort is expected to have a positive impact on both the environment and communities by turning polymer 3D printing into income for waste collectors and removing waste from the streams.
\nIndustries are moving toward the implementation of 3D printing as a manufacturing process because it facilitates the design of complex structures and rapid production of prototypes. AM utilizes a computer-aided design software that allows for the design of architectures with defined porosity and structures at a microscopic level. Because of the easy production of 3D printed prototypes, modeling based on a specific application can be performed to further improve the design of the end product and potentially reduce failure risks. The 3D printing of polymers and polymer composites has significantly progressed over the last 40 years and is expected to increase in the near future. Thermoplastic materials are readily commercially available for use in FDM, SLS, and inkjet processes. Materials like PC, ABS, PLA, ULTEM, and PCLA are commonly used for the manufacturing of tools, prototypes, and items used in the aerospace industry. However, these polymers are not one-size-fits-all types of polymers and are not necessary a good choice for all applications. Thus, research efforts are focused on developing materials that are capable of meeting specific applications. For examples, polymers blended with cultured cells can be used for scaffolds and implants on biological systems. Cells can be obtained from the patient and cultivated in the laboratory, thus producing a material that is less likely to be rejected by the patient. Fillers and additives can be used to generate multifunctional materials with improved mechanical properties. Fillers, such as CNTs and graphene, can be incorporated into the polymer to produce a material that is electrically conductive.
\nDespite all of the advances in the design and development of new polymeric materials for AM applications, challenges still remain. The availability of polymeric inks suitable for extreme applications, such as low temperature environments, high load pressures, and radiation resistance, is very limited. The development of new materials is necessary to increase the usefulness of polymer 3D printing technologies. Ideally, some of these composites are recyclable and/or biodegradable to reduce the negative impact plastics have on our environment.
\nWe thank the US Department of Energy’s National Nuclear Security Administration contract DE-AC-52-06NA25396 for providing financial support.
\nThe authors declare no conflict of interest.
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\n\nOur reputation – Everything we publish goes through a two-stage peer review process. We’re proud to count Nobel laureates among our esteemed authors. We meet European Commission standards for funding, and the research we’ve published has been funded by the Bill and Melinda Gates Foundation and the Wellcome Trust, among others. IntechOpen is a member of all relevant trade associations (including the STM Association and the Association of Learned and Professional Society Publishers) and has a selection of books indexed in Web of Science's Book Citation Index.
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\n\n"In developing countries until now, advancement in science has been very limited, because insufficient economic resources are dedicated to science and education. These limitations are more marked when the scientists are women. In order to develop science in the poorest countries and decrease the gender gap that exists in scientific fields, Open Access networks like IntechOpen are essential. Free access to scientific research could contribute to ameliorating difficult life conditions and breaking down barriers." Marquidia Pacheco, National Institute for Nuclear Research (ININ), Mexico
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