Example of structure of the suitability criteria table
\r\n\tHydrogen gas is the key energy source for hydrogen-based society. Ozone dissolved water is expected as the sterilization and cleaning agent that can comply with the new law enacted by the US Food and Drug Administration (FDA). The law “FDA Food Safety Modernization Act” requires sterilization and washing of foods to prevent food poisoning and has a strict provision that vegetables, meat, and fish must be washed with non-chlorine cleaning agents to make E. coli adhering to food down to “zero”. If ozone dissolved water could be successively applied in this field, electrochemistry would make a significant contribution to society.
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\r\n\tOxygen-enriched water is said to promote the growth of farmed fish. Hydrogen dissolved water is said to be able to efficiently remove minute dust on the silicon wafer when used in combination with ultrasonic irradiation.
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\r\n\t
\r\n\tThe purpose of this book is to show the latest water electrolysis technology and the future of society applying it.
In recent years, social robotics has become a popular research field. It aims to develop robots capable of communicating and interacting with humans in a personal and natural way. Social robots have the objective to provide assistance as a human would do it. Social robotics is a multidisciplinary field that brings together different areas of science and engineering, such as robotics, artificial intelligence, psychology and mechanics, among others (Breazeal, 2004). In this sense, an interdisciplinary group of the University of Almería is developing a social robot based on the Peoplebot platform (ActivMedia Robotics, 2003). It has been specifically designed and equipped for human-robot interaction. For that purpose, it includes all the basic components of sensorization and navigation for real environments. The ultimate goal is that this robot acts as a guide for visitors at our university (Chella et al., 2007). Since the robot can move on indoor/outdoor environments, we have designed and implemented a two-level decision making framework to decide the most appropriate localization strategy.
\n\t\t\tKnowledge modelling is a process of creating a model of knowledge or standard specifications about a kind of process or product. The resulting knowledge model must be interpretable by the computer; therefore, it must be expressed in some knowledge representation language or data structure that enables the knowledge to be interpreted by software and to be stored in a database or data exchange file. CommonKADS is a comprehensive methodology that covers the complete route from corporate knowledge management to knowledge analysis and engineering, all the way to knowledge-intensive systems design and implementation, in an integrated fashion (Schreiber et al., 1999).
\n\t\t\tThere are several studies on the knowledge representation and modelling for robotic systems. In some cases, semantic maps are used to add knowledge to the physical maps. These semantic maps integrate hierarchical spatial information and semantic knowledge that is used for robot task planning. Task planning is improved in two ways: extending the capabilities of the planner by reasoning about semantic information, and improving the planning efficiency in large domains (Galindo et al., 2008). Other studies use the CommonKADS methodology, or any of its extensions, to model the knowledge; some of the CommonKADS extensions that have been used in robotics are CommonKADS-RT, for real time systems, and CoMoMAS (Conceptual Modelling of Multi-Agent Systems), for multi-agent systems. The first one is based on CommonKADS with the addition of necessary elements to model real time restrictions and it is applied to the control of autonomous mobile robots (Henao et al., 2001). The second one extends CommonKADS towards Multi-Agent Systems development. A Nomad200 mobile robot is used to analyse two agent architectures, AIbot and CoNomad, by reverse engineering and to derive conceptual descriptions in terms of agent models (Glaser, 2002). Nowadays the knowledge engineering focuses mainly on domain knowledge, using reusable representations in the form of ontologies (Schreiber, 2008).
\n\t\t\tOne fundamental task to achieve our goal is robot navigation, which includes the subtasks of path planning, motion control, and localization. Generally, in the process of developing robots, robotics engineers select, at design time, a single method (algorithm) to solve each of these tasks. However, in the particular case of social robots (usually designed with a generic purpose, since its ultimate goal is to act as a human) it would be more interesting to provide several alternatives to solve a specific task and the criteria for selecting the best solution according to the current environment conditions. For instance, for the specific task of localization, the robot could decide to use a GPS-like solution, if it is moving on an open space, or dead-reckoning if it is in an indoor environment.
\n\t\t\tThe main contribution of this work is the development of an operational knowledge model for robot navigation. This model leads to a generic and flexible architecture, which can be used for any robot and any application, with a two-level decision mechanism. In the first level, the robotics engineer selects the methods to be implemented in the social robot. In the second level, robot applies dynamic selection to decide the proper method according to the environment conditions, taking into account a suitability criteria table. Dynamic selection of methods (DSM) lets to choose the best alternative to perform a task. It uses several suitability criteria, criterium weights, selection data and knowledge, and an aggregation function to make the decision (Bienvenido et al., 2001).
\n\t\t\tThe chapter is organized as follows. The second section presents the description of the robot system used in this work. In the third section, the methodology for the knowledge representation based on DSM is shown. Next, the fourth section shows the knowledge modelling for the localization subsystem needed to develop the generic multi-agent system for the social robot Peoplebot. The next section discusses the results of a physical experiment carried out to analyze the proposed methodology. The last section is devoted to conclusions and further works.
\n\t\tIn this work, the mobile robot called Peoplebot of ActivMedia Robotics Company has been used to test through physical experiments the proposed decision making approach. It is a mobile robot designed and equipped specifically for human-robot interaction research and applications. It includes all the basic components of sensorization and navigation in real environments, which are necessary for this interaction (see Fig. 1). It has two-wheel differential with a balancing caster and it feeds on three batteries that give an operational range of about ten hours. It also has installed a touch screen which displays a map of the University of Almería. Furthermore, for speech communication, it has two microphones to capture voice and two speakers. In this way, a user can interact with the robot either by manually selecting a target in the touch screen showing the environment map or by speaking directly to the robot.
\n\t\t\tPeoplebot robot: components (ActivMedia Robotics, 2003) and picture in action
Navigation architecture
For navigation purposes, a typical four-layer navigation architecture has been implemented (see Fig. 2). The top layer is devoted to path planning, that is, the generation of the reference trajectory between the current robot position and the target commanded by the user (touch screen or speech recognition modules). Then, a motion controller based on pure-pursuit (Coulter, 1992) is used to generate the actual wheel velocities. In order to ensure that the wheels move at the desired setpoints two low-level PID controllers were tuned. Finally, a layer devoted to localization is implemented. This localization layer is detailed subsequently.
\n\t\tThe knowledge model, about the localization for social robots described in this work, is based on some extensions of knowledge representation methodologies (like CommonKADS) and the DSM. Here, we introduce those approaches and a short summary of the localization algorithms implemented in the system.
\n\t\t\tThe CommonKADS methodology was consolidated as a knowledge engineering technique to develop knowledge-based systems (KBS) in the early 90’s (Schreiber et al., 1994). This method provides two types of support for the production of KBS in an industrial approach: firstly, a lifecycle enabling a response to be made to technical and economic constraints (control of the production process, quality assurance of the system,...), and secondly a set of models which structures the development of the system, especially the tasks of analysis and the transformation of expert knowledge into a form exploitable by the machine (Schreiber et al., 1999). Our proposal supposes to work in the expertise or knowledge model, one of the six models in CommonKADS. The rest are organizational (it supports the analysis of an organization, in order to discover problems and opportunities for knowledge systems), task (it analyzes the global task layout, its inputs and outputs, preconditions and performance criteria, as well as needed resources and competences), agent (it describes the characteristics of agents, in particular their competences, authority to act, and constraints in this respect), communication (it models the communicative transactions between the agents involved in the same task, in a conceptual and implementation-independent way) and design models (it gives the technical system specification in terms of architecture, implementation platform, software modules, representational constructs, and computational mechanisms needed to implement the functions laid down in the knowledge and communication models). Fig. 3 presents the kernel set of models used in the CommonKADS methodology (Schreiber et al., 1994).
\n\t\t\t\tCommonKADS kernel set of models
The purpose of the knowledge model is to detail the types and structures of the knowledge used in performing a task. It provides an implementation-independent description of the role that different knowledge components play in problem solving, in a way that is understandable for humans. This makes the knowledge model an important vehicle for communication with experts and users about the problem solving aspects of a knowledge system, during both development and system execution (Schreiber et al., 1999). So, its final goal is to analyze the tasks (objectives), methods (possible solution mechanisms), inferences (algorithms or agents) and domain knowledge elements (context and working data) for the KBS to be developed. These four elements permit to represent the knowledge involved in our mobile robot system. So, we have decided to use this knowledge engineering methodology.
\n\t\t\t\tThe Task-Method Diagrams (TMD) (Schreiber et al., 1999) to model the solution mechanism of the general problem represented by the highest-level task (main objective) are used. TMD presents the relation between one task to be performed and the methods that are suitable to perform that task, followed by the decomposition of these methods in subtasks, transfer functions and inferences (final implemented algorithms). Fig. 4 shows an example of TMD tree, where the root node represents the main task (Problem). It can be solved using two alternative methods (Met 1 and Met 2). First of them is implemented by the inference Inf 1, a routine executed by an agent. Second method requires the achievement of three tasks (really are two transfer functions Tran. Fun. 1 and Tran. Fun. 2 –special type of task, so it is represented by the same symbol- and one task Task 1). Transfer functions are tasks whose resolution is responsible for an external agent (for instance, it could be used for manual tasks). There are two methods to solve Task 1; they are Met 3 and Met 4. Second one is implemented by the inference Inf 2, while Met 3 requires the performance of four tasks: Task 3, Task 4, Task 5 and Task 6; each one is solved by a correspondent method (Met 5, Met 6, Met 7 and Met 8, respectively). These four methods are implemented by the inferences Inf 3, Inf 4, Inf 5 and Inf 6.
\n\t\t\t\tCommonKADS proposes that the different elements (tasks, methods and inferences) of the TMD are modelled using schemas like CML or CML2 (Guirado et al., 2009). These schemas formalize all the knowledge associated to each one of these elements.
\n\t\t\t\tSimple TMD
A given task, at any level, can be performed by several alternative methods, and these can be only applied at specific conditions. DSM is based on a general decision module that, taking into account the suitability criteria defined for each alternative method and actual data, would activate the most appropriate method. These suitability criteria have assigned weights whose values are calculated through functions that depend on the current knowledge of the problem and modify the suitability criteria values of the alternative methods to solve a given task (Bienvenido et al., 2001). For example, Table 1 shows the structure of the suitability criteria for a set of alternative methods. There are criteria that must be completely fulfilled, and others are conveniently weighted to offer a condition that increase or not the suitability of a given method. This technique was previously used in greenhouses design (Bienvenido et al., 2001), and robot navigation (Guirado et al., 2009).
\n\t\t\t\tMethod | \n\t\t\t\t\t\t\tCriterion 1 | \n\t\t\t\t\t\t\tCriterion 2 | \n\t\t\t\t\t\t\tCriterion 3 | \n\t\t\t\t\t\t\tCriterion 4 | \n\t\t\t\t\t\t\tCriterion 5 | \n\t\t\t\t\t\t
Method 1 | \n\t\t\t\t\t\t\t4 | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t\tf1( ) | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\tg1( ) | \n\t\t\t\t\t\t
Method 2 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\tf2( ) | \n\t\t\t\t\t\t\t3 | \n\t\t\t\t\t\t\tg2( ) | \n\t\t\t\t\t\t
Method 3 | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\tf3( ) | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\tg3( ) | \n\t\t\t\t\t\t
Method 4 | \n\t\t\t\t\t\t\t5 | \n\t\t\t\t\t\t\t5 | \n\t\t\t\t\t\t\tf4( ) | \n\t\t\t\t\t\t\t1 | \n\t\t\t\t\t\t\tg4( ) | \n\t\t\t\t\t\t
Method 5 | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\tf5( ) | \n\t\t\t\t\t\t\t2 | \n\t\t\t\t\t\t\tg5( ) | \n\t\t\t\t\t\t
Example of structure of the suitability criteria table
In this example, criteria 3 and 5 are hard constraints or critical (C). Notice that corresponding functions fM() and gM() can only take the values 0 or 1 (depending on environment conditions), where a value of 0 means that the method is not applicable if this criterion is not met, and a value of 1 means that it can be used. The other criteria (C1, C2 and C4) can take values between 1 and 5 according to the suitability of the method. These criteria are called soft constraints or non-critical (N).
\n\t\t\t\tIn this case, the global suitability value S for the method M (M = {1, 2, 3, 4, 5}) is given by the following equation:
\n\t\t\t\tWhere CiM is the value of the criterion i for the method M, and Wi is the weight for the criterion i. These weights depend on the environment conditions and their sum must be equal to 1. For instance, assuming that W1 = 0.5, W2 = W4 = 0.25 and that the suitability criteria table is as shown in the table above (with f1() = f5() = 0, f2() = f3() = f4() = 1, g1() = g2() = g3() =1, and g4() = g5() = 0), then the selected method would be the number 3 (S1 = 0, S2 = 2.5, S3 = 3, S4 = 0, and S5 = 0). Notice that if there are two or more methods with the highest suitability value, the current method remains as selected, and if not, the method is selected randomly.
\n\t\t\tRobot localization is defined as the process in which a mobile robot determines its current position and orientation relative to an inertial reference frame. Localization techniques have to deal with the particular features of environment conditions, such as a noisy environment (vibrations when the robot moves, disturbance sources, etc.), changing lighting conditions, high degrees of slip, and other inconveniences and disturbances.
\n\t\t\t\tMethod | \n\t\t\t\t\t\t\tIndoor/Outdoor | \n\t\t\t\t\t\t\tComputing Time | \n\t\t\t\t\t\t\tLight Conditions | \n\t\t\t\t\t\t\tPrecision | \n\t\t\t\t\t\t\tCost | \n\t\t\t\t\t\t\tSensors | \n\t\t\t\t\t\t\tFault-tolerant | \n\t\t\t\t\t\t
Odometry | \n\t\t\t\t\t\t\tBoth, not advisable for slip conditions | \n\t\t\t\t\t\t\tFast | \n\t\t\t\t\t\t\tThere is no inconve-nience | \n\t\t\t\t\t\t\tError grows with distance | \n\t\t\t\t\t\t\tCheap | \n\t\t\t\t\t\t\tEncoders | \n\t\t\t\t\t\t\tIt only depends on encoders readings | \n\t\t\t\t\t\t
Dead-reckoning | \n\t\t\t\t\t\t\tBoth | \n\t\t\t\t\t\t\tFast | \n\t\t\t\t\t\t\tThere is no inconve-nience | \n\t\t\t\t\t\t\tError grows with distance, although it is reduced taking IMU data | \n\t\t\t\t\t\t\tMore expensive than odometry | \n\t\t\t\t\t\t\tEncoders and IMU | \n\t\t\t\t\t\t\tIt depends on encoders and IMU | \n\t\t\t\t\t\t
Beacons | \n\t\t\t\t\t\t\tMainly indoor | \n\t\t\t\t\t\t\tMiddle | \n\t\t\t\t\t\t\tBeacons must be observable from robot | \n\t\t\t\t\t\t\tAbsolute position (no error growth) | \n\t\t\t\t\t\t\tExpensive (installation of markers) | \n\t\t\t\t\t\t\tBeacons, landmarks, etc. | \n\t\t\t\t\t\t\tIt uses many beacons | \n\t\t\t\t\t\t
GPS-based | \n\t\t\t\t\t\t\tOnly outdoor | \n\t\t\t\t\t\t\tMiddle | \n\t\t\t\t\t\t\tThere is no inconve-nience | \n\t\t\t\t\t\t\tAbsolute position (no error growth) | \n\t\t\t\t\t\t\tHigh cost of accurate GPS | \n\t\t\t\t\t\t\tGPS, DGPS, RTK-GPS | \n\t\t\t\t\t\t\tIt depends on the number of available satellites | \n\t\t\t\t\t\t
Visual odometry | \n\t\t\t\t\t\t\tBoth, advisable for slip conditions | \n\t\t\t\t\t\t\tUsually high | \n\t\t\t\t\t\t\tIt depends on light conditions | \n\t\t\t\t\t\t\tError grows with distance, although it is reduced taking visual data | \n\t\t\t\t\t\t\tCheap | \n\t\t\t\t\t\t\tCamera(s) | \n\t\t\t\t\t\t\tIt depends on camera(s) | \n\t\t\t\t\t\t
Kalman-filter-based | \n\t\t\t\t\t\t\tBoth | \n\t\t\t\t\t\t\tUsually high | \n\t\t\t\t\t\t\tThere is no inconve-nience | \n\t\t\t\t\t\t\tSmall error (redundant sources) | \n\t\t\t\t\t\t\tExpensive (redundant sensors) | \n\t\t\t\t\t\t\tIt depends on fused sensors | \n\t\t\t\t\t\t\tYes, since it generally uses several redundant sources | \n\t\t\t\t\t\t
Main characteristics of the localization techniques
In this work, we have analyzed different localization methods, in order to evaluate the most appropriate ones according to the activity of the robot. In order to achieve this objective, we have firstly studied the typical localization methods for the mobile robotics community and we discuss the advantages and disadvantages of these methods to our specific case.
\n\t\t\t\tThe most popular solutions are wheel-based odometry and dead-reckoning (Borenstein & Feng, 1996). These techniques can be considered as relative or local localization. They are based on determining incrementally the position and orientation of a robot from an initial point. In order to provide this information, it uses various on-board sensors, such as encoders, gyroscopes, accelerometers, etc. The main advantage of wheel-based odometry is that it is a really straightforward method. The main drawback is, above all, an unbounded growth of the error along time and distance, particularly in off-road slip conditions (González, 2011).
\n\t\t\t\tWe have also analyzed global or absolute localization techniques, which determine the position of the robot with respect to a global reference frame (Durrant-Whyte & Leonard, 1991), for instance using beacons or landmarks. The most popular technique is GPS-like solutions such as Differential GPS (DGPS) and Real-Time Kinematics GPS (RTK-GPS). In this case, the error growth is mitigated and the robot position does not depend on time and initial position. The main problems in relation to GPS are a small accuracy of data (improved using DGPS and RTK-GPS) and the signal is lost in closed spaces (Lenain et al., 2004). Other solutions such as artificial landmarks or beacons require a costly installation of the markers on the area where the robot operates.
\n\t\t\t\tOn the other hand, there are some localization techniques based on visual information (images). One of the most extended approaches is visual odometry or Ego-motion estimation, which is defined as the incremental on-line estimation of robot motion from an image sequence (Nistér et al., 2006). It constitutes a straightforward-cheap method where a single camera can replace a typical expensive sensor suite, and it is especially useful for off-road applications, since visual information estimates the actual velocity of the robot, minimizing slip phenomena (Angelova et al., 2007).
\n\t\t\t\tFinally, probabilistic techniques based on estimating the localization of the mobile robot combining measurements from different data sources are becoming popular. The most extended technique is the Kalman filter (Thrun et al., 2005). The main advantage of these techniques is that each data source is weighted taken into account statistical information about reliability of the measuring devices and prior knowledge about the system. In this way, the deviation or error is statistically minimized.
\n\t\t\t\tSumming up, in Table 2 the considered localization methods for our social robot are presented. We also detail some key parameters to decide the most appropriate solution, depending on the task to be performed.
\n\t\t\tIn order to model the knowledge that the social robot needs to take decisions, we have analyzed the characteristics of the localization methods to decide the necessary parameters for the best selection in different environment conditions. Firstly, all available alternatives have been evaluated. Since it would be inefficient to implement all the methods in the robot, it is applied a first decision level in which the human experts select the methods that the social robot may need taking into account the scenarios to be found at the University. In this sense, we are considering a social mobile robot working at indoor and outdoor scenarios. The main purpose of this mobile robot is to guide to the people at our University, that means, the robot could guide a person inside a building (for instance, the library) or it could work outdoors between buildings.
\n\t\t\tWe propose a two-level multi-agent architecture for knowledge modelling of the localization strategy. Fig. 5 shows a schema for this architecture. Firstly, the expert selected the most proper methods for the kind of activities that the robot has to make (move at the campus of the University of Almería). These localization methods were: wheel-based odometry since it is a straightforward method to estimate the robot position. This approach is especially used for indoor environments (like inside the library). On the other hand, for outdoor motions, the visual odometry approach and a DGPS-like solution are used. Finally, it is also considered to use a Kalman filter fusing data from visual odometry and DGPS.
\n\t\t\tSchema for the proposed two-level multi-agent architecture
The first selection process (filter applied by the engineer) lets that the robot chooses only between useful and independent methods, according to the kind of activities to be accomplished by the mobile robot. In this way, redundant and useless localization methods will be avoided.
\n\t\t\tThe second decision level of this architecture considers a general scheduler module implemented in the social robot. This planner is permanently running. When the robot has to take a decision (selecting an alternative among several options to accomplish a particular task) it calls to the scheduler agent. This agent uses the context information, the suitability criteria table and a dynamic cost function (depending on the scenario) to select the most appropriate localization method.
\n\t\t\tSome of the main advantages of this architecture are that the robot can choose the most appropriate localization method according to the surrounding environment and new decisions can be incorporated simply including its suitability criteria table.
\n\t\t\t\n\t\t\t\tFig. 6 shows the lower-level TMD elements, simplified to four testing alternatives of localization. This is a branch of the most general navigation subsystem TMD (Guirado et al., 2009).
\n\t\t\tDSM is applied to choose the most efficient method using an aggregation function that integrates the suitability criteria and the weights to generate a suitability value for each method. In our particular case, the criteria for decision-making are Computing Time (CT), GPS-Signal Necessity (GN), Luminosity (L), Fault-Tolerance (FT) and Precision (P). These criteria are related to the method characterization done in the previous section. CT, L, FT and P are directly considered in the Table 2, while GN is related to the Indoor/Outdoor and Sensors method parameters. The economic Cost of implementation is used by the expert in the first decision level in order to choose the methods to be implemented in the robot, but it does not make sense to use it as a suitability criterion for selecting the best alternative method among those that are implemented in the robot.
\n\t\t\tRepresentation of a TMD for a pre-filtered localization system
CT is inversely proportional to the execution time of each method, favouring the faster method to calculate the exact position of the robot. We have considered this criterion because some instances need a fast response and it is necessary to use the fastest algorithm. CT is considered a non-critical (N) and static (S) criterion that means it is not used to discard any alternative method and its value is considered fixed for each method because the variations in testing are minimal.
\n\t\t\tGN indicates if a method needs a good GPS signal to be considered in the selection process. This criterion is critical (C) only for the DGPS-based method because the robot cannot apply it if the received signal to get the position is low (less than 4 satellite signals). The other methods do not use the GN criterion because they do not use the GPS data; so, it is convenient or non-critical (N) for those methods. The criterion is dynamic (D) for all the methods, taking values 0 or 1 for DGPS-based method, and values between 1 and 5 for the rest.
\n\t\t\tL represents the intensity of the light in the place where the robot is. If the luminosity is low, algorithms that require the use of conventional cameras for vision cannot be used. This is a dynamic (D) criterion since the robot must operate in places more or less illuminated with natural or artificial light. So, the value of this criterion is changing and its value is discretized between 1 and 5. As this criterion does not exclude any method in the selection process, it is considered non-critical (N). Notice that, in our case, luminosity is obtained analyzing the histogram of an image.
\n\t\t\tFT is a parameter that indicates if the robot system is able to continue operating, possibly at a reduced level, rather than failing completely, when the applied method fails. This criterion is static (S) for each method. Its values have been obtained from our experiences. As in the previous criterion, this is also considered non-critical (N).
\n\t\t\tP is related to the accuracy of the sensor data that each method uses. It has a dynamic (D) value because the environment conditions are changing. For instance, GPS signal quality is fine in an open area; therefore, the precision of DGPS-based method is high. This is another non-critical (N) criterion because it does not discard any method by itself.
\n\t\t\tAs previously explained, the human expert has chosen four localization methods in the first decision level. These alternatives are wheel-based odometry (O), DGPS-based (G), Kalman-filter-based (K) and visual odometry (V); each of them has assigned a set of suitability criteria.
\n\t\t\tThe cost function considers the criteria with their associated weights,
\n\t\t\tThe weights (Wi) are dynamic functions, so they can change depending on environment and performance requirements.
\n\t\t\tThe function for the critical criterion GN is defined as follow.
\n\t\t\tSo, it can only be equal to 0 for the DGPS-based method, and the GPS signal must also be insufficient.
\n\t\t\tThe description of the elements (tasks, methods and inferences) has been represented using the CML notation, as CommonKADS methodology proposes (Schreiber et al., 1999). Here is an example for the localization task:
\n\t\t\tEach selection criterion has two letters in front of his name. The first one is the severity of the criterion, where N indicates non-critical and C indicates critical, and the second one is if the criteria can change or not, using D for dynamic and S for static.
\n\t\tThe proposed methodology was tested through several physical experiments showing how the robot applies the knowledge model-based architecture using the suitability criteria values (depending on the environmental conditions) to select the appropriate method in every moment.
\n\t\t\tIn this section, we analyze the proposed methodology in a real scenario. Our real case has been that the mobile robot has guided a person at our University (see Fig. 7) from the bus stop (start) to the library (goal). Firstly, the visitor tells the robot to guide him to the library. In this case, the user used the touch screen. Then, the mobile robot calculated the optimal route according to several parameters (we are not detailing it here). The solution of this stage was the line marked in Fig. 7 (left). The mobile robot is moving at 0.5 m/s with a sampling time of 0.2 s. In order to avoid sudden transitions from one method to another, due to sensor noises and disturbances, we have tuned a filter, where a decision will not be taken until a method is not selected 10 consecutive times.
\n\t\t\tIn this case, the robot moves through four areas along the trajectory. The path labelled with “a” is a wide-open space. The path labelled with “b” is a narrow way with some trees. Finally, the path labelled with “c” is open space but close to buildings. Notice that the robot moved on a pavement terrain, which leads to slip phenomena, is not expected. The real trajectory followed by the robot is shown in Fig. 7 (right); note that the x-axis has a different scale from y-axis in the plot.
\n\t\t\tReal scenario (University map) and followed trajectory. The mobile robot has guided a person from bus stop (start) to the library (goal)
As previously explained, the GN criterion is critical for the DGPS-based method. This means that method is not selectable if GPS signal is insufficient (less than 4 satellites available). So, we represent in Fig. 8 the number of satellites detected by the GPS justifying the necessity to use other alternatives localization methods in some trajectory paths.
\n\t\t\tCT and FT are static criteria and so they have the same values in all situations, since they are related to independent characteristics of the environment (CTO=5, CTG=2, CTK=1, CTV=4, FTO=2, FTG=4, FTK=5 and FTV=3). Other criteria (GN, L and P) are dynamic, that means they can change depending on the environment conditions.
\n\t\t\tGPS signal during the robot travel
In the first area (“a”), the GN and L criteria was equal for all methods, since all of them could be used without problems in current conditions. In addition, the robot initially considered the same weights for all criteria (WCT = WL = WFT = WP = 0.25). Applying the cost function, robot obtained the following suitability values for each method:
\n\t\t\tAs expected, robot used the DGPS-based localization method, since it obtains the larger suitability value. Notice in Fig. 8 that there are more than three satellites available during this path.
\n\t\t\tIn the second area (“b”), the GN and L criteria remained the same for all methods. Factors for P criterion changed for some methods with respect to the previous area. The GPS signal was frequently lost due to the trees and the error increased considerably (see Fig. 8). In addition, the user increased the velocity of the robot, which led to give a higher weight to TC criterion, keeping a constant value for the other (WCT = 0.4; WL = WFT = WP = 0.2). These were the obtained suitability values for each method:
\n\t\t\tThe selected method was visual odometry. DGPS method got a low suitability value due to “b” was a cover area (trees) and the GPS signal was temporary lost (see Fig. 8).
\n\t\t\tIn the third area (“c”), the GN and L criteria remained the same for all methods. Factors for the P criterion slightly changed from the previous area (GPS signal was slightly better since there were not trees, although still affected by the proximity to the buildings). The user reduced the velocity of the robot, and it led to reduce the weight of the TC criterion, keeping a constant value for the other (WCT = 0.1; WL = WFT = WP = 0.3). The obtained suitability values were:
\n\t\t\tThe Kalman-filter-based obtained the larger suitability value since “c” was an open area where DGPS and visual odometry work fine.
\n\t\t\tIn the last area (inside the library), the GN criterion was zero for the DGPS-based method, since the signal was completely lost; moreover, the L criterion decreased slightly for visual odometry method due to changing light conditions. When the robot goes inside the library, it considers the same weights for all criteria again (WCT = WL = WFT = WP = 0.25). The obtained suitability values were:
\n\t\t\tFinally, as expected, when the mobile robot guided to the person inside the library, wheel-based odometry method obtained the larger suitability value.
\n\t\t\t\n\t\t\t\tFig. 9 shows the average values during the experiment for the localization methods. This information has been used in the test of the proposed methodology.
\n\t\tThe main objective of this work is to take a further step in developing a generic and flexible decision mechanism to select the most proper localization algorithm for a social robot. We present the preliminary results for a single decision between four alternatives (selected by the human expert in the first decision level). More tests will be performed within the same operating environment in the future.
\n\t\t\tThe main advantages of the proposed architecture are to facilitate further addition of new algorithms that could be developed in the future and the capacity of deciding in real-time the most appropriate technique to be used in the current conditions.
\n\t\t\tFrom a practical point of view, and according to our physical experiments, the proposed methodology permits to successfully guide users at our university by choosing the best localization method taking into account the surrounding environment.
\n\t\t\tAverage suitability values for the localization methods in every path (“a”, “b”, “c”, “d”)
Here we have applied a direct DSM that means the best method is the one with the highest suitability value (or one of them if there is more than one), but we are considering to incorporate fuzzy logic to the cost function and to apply other types of membership functions to the DSM.
\n\t\t\tIn order to follow evaluating the proposed mechanisms of DSM in robotics, we are extending the use of these techniques to other social robot tasks. The final goal is to build an ontology in the domain of social robotic.
\n\t\tTitanium (Ti) is a lustrous metal with a silver color. This metal exists in two different physical crystalline state called body centered cubic (bcc) and hexagonal closed packing (hcp), shown in Figure 1 (a) and (b), respectively. Titanium has five natural isotopes, and these are 46Ti, 47Ti, 48Ti, 49Ti, 50Ti. The 48Ti is the most abundant (73.8%).
\n\nCrystalline state of titanium: (a) bcc, and (b) hcp [8].
Titanium has high strength of 430 MPa and low density of 4.5 g/cm3, compared to iron with strength of 200 MPa and density of 7.9 g/cm3. Accordingly, titanium has the highest strength-to-density ratio than all other metals. However, titanium is quite ductile especially in an oxygen-free environment. In addition, titanium has relatively high melting point (more than 1650°C or 3000°F), and is paramagnetic with fairly low electrical and thermal conductivity. Further, titanium has very low bio-toxicity and is therefore bio-compatible. Furthermore, titanium readily reacts with oxygen at 1200°C (2190°F) in air, and at 610°C (1130°F) in pure oxygen, forming titanium dioxide. At ambient temperature, titanium slowly reacts with water and air to form a passive oxide coating that protects the bulk metal from further oxidation, hence, it has excellent resistance to corrosion and attack by dilute sulfuric and hydrochloric acids, chloride solutions, and most organic acids. However, titanium reacts with pure nitrogen gas at 800°C (1470°F) to form titanium nitride [1, 2].
\nSome of the major areas where titanium is used include the aerospace industry, orthopedics, dental implants, medical equipment, power generation, nuclear waste storage, automotive components, and food and pharmaceutical manufacturing.
\nTitanium is the ninth-most abundant element in Earth‘s crust (0.63% by mass) and the seventh-most abundant metal. The fact that titanium has most useful properties makes it be preferred material of future engineering application. Moreover, the application of titanium can be extended when alloyed with other elements as described below.
\nAn alloy is a substance composed of two or more elements (metals or nonmetals) that are intimately mixed by fusion or electro-deposition. On this basis, titanium alloys are made by adding elements such as aluminum, vanadium, molybdenum, niobium, zirconium and many others to produce alloys such as Ti-6Al-4V and Ti-24Nb-4Zr-8Sn and several others [2]. These alloys have exceptional properties as illustrated below. Depending on their influence on the heat treating temperature and the alloying elements, the alloys of titanium can be classified into the following three types:
\nThese alloys contain a large amount of α-stabilizing alloying elements such as aluminum, oxygen, nitrogen or carbon. Aluminum is widely used as the alpha stabilizer for most commercial titanium alloys because it is capable strengthening the alloy at ambient and elevated temperatures up to about 550°C. This capability coupled with its low density makes aluminum to have additional advantage over other alloying elements such as copper and molybdenum. However, the amount of aluminum that can be added is limited because of the formation of a brittle titanium-aluminum compound when 8% or more by weight aluminum is added. Occasionally, oxygen is added to pure titanium to produce a range of grades having increasing strength as the oxygen level is raised. The limitation of the α alloys of titanium is non-heat treatable but these are generally very weldable. In addition, these alloys have low to medium strength, good notch toughness, reasonably good ductility and have excellent properties at cryogenic temperatures. These alloys can be strengthened further by the addition of tin or zirconium. These metals have appreciable solubility in both alpha and beta phases and as their addition does not markedly influence the transformation temperature they are normally classified as neutral additions. Just like aluminum, the benefit of hardening at ambient temperature is retained even at elevated temperatures when tin and zirconium are used as alloying elements.
\nThese alloys contain 4–6% of β-phase stabilizer elements such as molybdenum, vanadium, tungsten, tantalum, and silicon. The amount of these elements increases the amount of β-phase is the metal matrix. Consequently, these alloys are heat treatable, and are significantly strengthened by precipitation hardening. Solution treatment of these alloys causes increase of β-phase content mechanical strength while ductility decreases. The most popular example of the α-β titanium alloy is the Ti-6Al-4V with 6 and 4% by weight aluminum and vanadium, respectively. This alloy of titanium is about half of all titanium alloys produced. In these alloys, the aluminum is added as α-phase stabilizer and hardener due to its solution strength-ening effect. The vanadium stabilizes the ductile β-phase, providing hot workability of the alloy.
\nThe α-β titanium alloys have high tensile strength, high fatigue strength, high corrosion resistance, good hot formability and high creep resistance [3].
\nTherefore, these alloys are used for manufacturing steam turbine blades, gas and chemical pumps, airframes and jet engine parts, pressure vessels, blades and discs of aircraft turbines, aircraft hydraulic tubing, rocket motor cases, cryogenic parts, and marine components [4].
\nThese alloys exhibit the body centered cubic crystalline form shown in Figure 1 (a). The β stabilizing elements used in these alloy are one or more of the following: molybdenum, vanadium, niobium, tantalum, zirconium, manganese, iron, chromium, cobalt, nickel, and copper. Besides strengthening the beta phase, these β stabilizers lower the resistance to deformation which tends to improve alloy fabricability during both hot and cold working operations. In addition, this β stabilizer to titanium compositions also confers a heat treatment capability which permits significant strengthening during the heat treatment process [4].
\n\nAs a result, the β titanium alloys have large strength to modulus of elasticity ratios that is almost twice those of 18–8 austenitic stainless steel. In addition, these β titanium alloys contain completely biocompatible elements that impart exceptional biochemical properties such as superior properties such as exceptionally high strength-to-weight ratio, low elastic modulus, super-elasticity low elastic modulus, larger elastic deflections, and low toxicity [1, 3].
\nThe above properties make them to be bio-compatible and are excellent prospective materials for manufacturing of bio-implants. Therefore, nowadays these alloys are largely utilized in the orthodontic field since the 1980s, replacing the stainless steel for certain uses, as stainless steel had dominated orthodontics since the 1960s [2].
\nBecause of alloying the titanium achieve improved properties that make it to be preferred material of choice for application in aerospace, medical, marine and instrumentation. The extent of improvement to the properties of titanium alloys and ultimately the choice of area of application is influenced by the methods of production and processing as discussed in the subsequent sections.
\nThe base metal required for production of titanium alloys is pure titanium. Pure titanium is produced using several methods including the Kroll process. This process produces the majority of titanium primary metals used globally by industry today. In this process, the titanium is extracted from its ore rutile—TiO2 or titanium concentrates. These materials are put in a fluidized-bed reactor along with chlorine gas and carbon and heated to 900°C and the subsequent chemical reaction results in the creation of impure titanium tetrachloride (TiCl4) and carbon monoxide. The resultant titanium tetrachloride is fed into vertical distillation tanks where it is heated to remove the impurities by separation using processes such as fractional distillation and precipitation. These processes remove metal chlorides including those of iron, silicon, zirconium, vanadium and magnesium. Thereafter, the purified liquid titanium tetrachloride is transferred to a reactor vessel in which magnesium is added and the container is heated to slightly above 1000°C. At this stage, the argon is pumped into the container to remove the air and prevent the contamination of the titanium with oxygen or nitrogen. During this process, the magnesium reacts with the chlorine to produce liquid magnesium chloride thereby leaving the pure titanium solid. This process is schematically presented in Figure 2.
\nKroll process for production of titanium: (a) chlorination, (b) fractional distillation [5].
The resultant titanium solid is removed from the reactor by boring and then treated with water and hydrochloric acid to remove excess magnesium and magnesium chloride leaving porous titanium sponge, which is jackhammered, crushed, and pressed, followed by melting in a vacuum electric arc furnace using expendable carbon electrode. The melted ingot is allowed to solidify in a vacuum atmosphere. This solid is often remelted to remove inclusions and to homogenize its constituents. These melting steps add to the cost of producing titanium, and this cost is usually about six times that of stainless steel. Usually the titanium solid undergo further treatment to produce titanium powder required in alloying process. The basic methods used to produce titanium powder are summarized below.
\nThe first method is called the Armstrong process, shown in Figure 3, in which the powder is made as the product of extractive processes that produce primary metal powder. This process is capable of producing commercially pure titanium (Ti) powder by the reduction of titanium tetrachloride (TiCl4) and other metal halides using sodium (Na). This process produces powder particles with a unique properties and low bulk density. To improve powder properties such as the particle size distribution and the tap density, additional post processing activities such as dry and wet ball milling are applied. The narrowed particle size distributions are necessary for typical powder metallurgical processes. In addition, the resultant powder’s morphology produced by the Armstrong process provide for excellent compressibility and compaction properties that result in dense compacts with increased green strength than those produced by the irregular powders. For this reason, the powders can even be consolidated by traditional powder metallurgy techniques such as uniaxial compaction and cold isostatic pressing. Figure 4 illustration the scanning electron microscope images of the titanium powders of the Armstrong process. As seen in the figure, the powder has an irregular morphology made of granular agglomerates of smaller particles.
\nIllustration of the Armstrong process [5].
SEM micrographs of CP-Ti produced by Armstrong process [5].
The hydride-dehydride (HDH) process, illustrated in Figure 5, is used to produce titanium powder using titanium sponge, titanium, mill products, or titanium scrap as the raw material. The hydrogenation process is achieved using a batch furnace that is usually operated in vacuum and/or hydrogen atmospheric conditions. The conditions necessary for hydrogenation of titanium are pressure of one atmospheric and temperatures of utmost 800°C. This process results in forming of titanium hydride and alloy hydrides that are usually brittle in nature. These metal hydrides are milled and screened to produce fine powders. The powder is resized using a variety of powder-crushing and milling techniques may be used including: a jaw crusher, ball milling, or jet milling. After the titanium hydride powders are crushed and classified, they are placed back in the batch furnace to dehydrogenate and remove the interstitial hydrogen under vacuum or argon atmosphere and produce metal powder. These powders are irregular and angular in morphology and can also be magnetically screened and acid washed to remove any ferromagnetic contamination. Finer particle sizes can be obtained, but rarely used because oxygen content increases rapidly when the powder is finer than −325 mesh. Powder finer than −325 mesh also possess more safety challenges [5]. The powder can be passivated upon completion of both the hydrogenating and dehydrogenating cycles to minimize exothermic heat generated when exposed to air.
\nHydride-dehydride process for obtaining of titanium powders [6].
The hydride-dehydride process is relatively inexpensive because the hydrogenation and dehydrogenation processes contribute small amount of cost to that of input material. The additional benefit of this process is the fact that the purity of the powder can be very high, as long as the raw material’s impurities are reduced. The oxygen content of final powder has a strong dependence on the input material, the handling processes and the specific surface area of the powder. Therefore, the main disadvantages of hydride-dehydride powder include: the powder morphology is irregular, and the process is not suitable for making virgin alloyed powders or modification of alloy compositions if the raw material is from scrap alloys (Figure 6) [5].
\nSEM micrographs of CP-Ti produced by HDH [5].
Conventional sintering, shown in Figure 7, is one of the widely applied powder metallurgy (PM) based method for manufacturing titanium alloys. In this method, the feedstock titanium powder is mixed thoroughly with alloying elements mentioned in Section 2 using a suitable powder blender, followed by compaction of the mixture under high pressure, and finally sintered. The sintering operation is carried out at high temperature and pressure treatment process that causes the powder particles to bond to each other with minor change to the particle shape, which also allows porosity formation in the product when the temperature is well regulated. This method can produce high performance and low cost titanium alloy parts. The titanium alloy parts produced by powder metallurgy have several advantages such as comparable mechanical properties, near-net-shape, low cost, full dense material, minimal inner defect, nearly homogenous microstructure, good particle-to-particle bonding, and low internal stress compared with those titanium parts produced by other conventional processes [7].
\nPowder metallurgy process [7].
Self-propagating high temperature synthesis (SHS), shown in Figure 8, is another PM based process used to produce titanium alloys. The steps in this process include: mixing of reagents, cold compaction, and finally ignition to initiate a spontaneous self-sustaining exothermic reaction to create the titanium alloy [7].
\nSHS process [7].
Although the above PM processes are mature technologies for fabrication of bone implants they have difficulties of fabricating porous coatings on surfaces that are delicate or with complex geometries. In addition, these processes tend to produce brittle products because of cracks and oxides formed inside the materials. Further, the high costs and poor workability associated with these PM processes restrict their application in commercial production of bone implants. Consequently, new methods, based on additive manufacturing principles were developed [7].
\nThe definitions of advanced methods of production is the use of technological method to improve the quality of the products and/or processes, with the relevant technology being described as “advanced,” “innovative,“ or “cutting edge.” These technologies evolved from conventional processes some of which have been developed to achieve various components of titanium base alloys and aluminides. Atomisation processes are among the most widely used cutting edge methods for production of titanium alloys [5].
\nAtomisation processes are used to make alloyed titanium powders. In these processes, the feedstock material is generally titanium, and the alloy powders produced are further processed typically to manufacture components using processes such as hot isostatic pressing (hip). As mentioned previously, it is generally believed that alloyed powders are not suitable for cold compaction using conventional uniaxial die pressing methods. Moreover, the inherent strength of the alloyed powders is too high, making it difficult to deform the particles in order to achieve desired green density. The atomisation processes produce relatively spherically shaped titanium alloy powders that are most suitable for additive manufacturing using techniques such as selective laser melting or electron beam melting. These spherical powders are also required for manufacturing titanium components using metal injection molding techniques. Typically, additive manufacturing and metal injection molding processes require particle sizes of powders to be in the range of 100 μm to ensure good flowability of the powder during operations. However, the challenge of the atomisation processes usually is that powders produced tend to have a wide particle size distribution, from a few to hundreds of micrometers. Examples of atomisation processes are gas atomisation and plasma atomisation processes described below [5].
\nIn the gas atomisation process, shown in Figure 9, the metal is usually melted using gas and the molten metal is atomised using an inert gas jets. The resultant fine metal droplets are then cooled down during their fall in the atomisation tower. The metal powders obtained by gas-atomization offer a perfectly spherical shape combined with a high cleanliness level. However, even though gas atomisation is, generally, a mature technology, its application need to be widened after addressing a few issues worth noting such as considerable interactions between droplets while they cool during flight in the cooling chamber, causing the formation of satellite particles. Also, due to the erosion of atomising nozzle by the liquid metal, the possibility for contamination by ceramic particles is high. Usually, there may also be argon gas entrapment in the powder that creates unwanted voids [5].
\nSchematic diagrams of gas atomisation process [5].
Plasma atomisation, shown in Figure 10, uses a titanium wire alloy as the feed material which is a significant cost contributing factor. The titanium alloy wire, fed via a spool, is melted in a plasma torch, and a high velocity plasma flow breaks up the liquid into droplets which cool rapidly, with a typical cooling rate in the range of 100–1000°C/s. Plasma atomisation produces powders with particle sizes ranging from 25 to 250 μm. In general, the yield of particles under 45 μm using the plasma wire atomisation technique is significantly higher than that of conventional gas atomisation processes [5].
\nSchematic diagrams of plasma atomisation process [5].
The future methods for production of titanium alloys depend on the demand of these products and to what extend nature will be able to provide them. The demand for titanium alloys shall also influence the number and type of technological breakthroughs, the extent of automation, robotics’ application, the number of discoveries for new titanium alloys, their methods of manufacturing, and new areas of application. Automation is an important aspect of the industry’s future and already a large percentage of the manufacturing processes are fully automated. In addition, automation enables a high level of accuracy and productivity beyond human ability—even in hazardous environments. And while automation eliminates some of the most tedious manufacturing jobs, it is also creating new jobs for a re-trained workforce. The new generation of robotics is not only much easier to program, but also easier to use due to extra capabilities such as voice and image recognition during operations, they are capable of doing precisely what you ask them to do. The discovery of new titanium alloys, or innovative uses of existing ones, is essential for making progress in many of the technological challenges we face. This discovery can result in new synthesis methods of new alloy compounds and design of super alloys, theoretical modeling and even the computational prediction of titanium alloys. This discovery requires that new methods of manufacturing are developed. In light of this, “additive manufacturing” is being developed and this is viewed as a groundbreaking development in manufacturing advancement that offers manufacturers powerful solutions for making any number of products cost-effectively and with little waste. Examples of additive manufacturing technologies are cold spray, 3-D printing, electron beam melting, and selective laser melting. To fabricate alloy surfaces using these technologies, alloying elements are mixed thoroughly in the feedstock powder and the fabrication processes proceed as described in the following paragraphs [7, 8].
\nCold spray (CS) process, schematically shown in Figures 11 and 12 can deposit metals or metal alloys or composite powders on a metallic or dielectric substrate using a high velocity (300–1200 m/s) jet of small (5–50 μm) particles injected in a stream of preheated and compressed gas passing through a specially designed nozzle. The main components of a generic CS system include the source of compressed gas, gas heater, powder feeder, spray nozzle assembly, and sensors for gas pressure and temperature. The source of compressed gas acquires the gas from an external reservoir, compresses it to desired pressure and delivers it into the gas heater. Then, the gas heater preheats the compressed gas in order to increase its enthalpy energy. The preheated gas is delivered into the spray nozzle assembly whose convergent/divergent geometry not only converts the enthalpy energy of the gas into kinetic energy but also mixes the metal powders with the gas proportionately. The powder feeder meters and injects the powder in the spray nozzle assembly. The sensors for the gas pressure and temperature are responsible for regulating the preset pressure and temperature of the gas stream. The powder injection point in the spray nozzle assembly, the gas pressure, and gas temperature distinguish the low pressure-CS system (LP-CS) from the high pressure CS (HP-CS). In the LP-CS system, the feedstock powder is injected in the downstream side of the convergent section of the nozzle assembly, while in the HP-CS system; the powder is injected in the upstream side of the convergent/diverging section of the nozzle assembly as illustrated in Figures 11 and 12. Several other parameters which contribute towards the distinguishing of the CS systems are summarized in Table 1 [8].
\nLow pressure CS process configuration [8].
High pressure CS process configuration [8].
Operation parameters for CS systems [8].
3-D printing is an additive manufacturing method that applies the principle of adding material to create structures using computer aided design (CAD), part modeling, and layer-by-layer deposition of feedstock material. This cutting-edge technology is also called stereolithography, and is illustrated in Figure 13 [8].
\n3D-printing process [8].
In this technology, the pattern is transferred from a digital 3D model, stored in the CAD file, to the object using a laser beam scanned through a reactive liquid polymer which hardened to create a thin layer of the solid. In this manner, the structure is fabricated on the desired surface. This method was proved in the laboratory setup is still being integrated in commercial set-up because 3-D printing is the most widely recognized version of additive manufacturing. For this reason, the inventors and engineers for this process have for years used machines costing anywhere from a few thousand dollars to hundreds of thousands for rapid prototyping of new products. It can be noted that all of the additive-manufacturing processes follow this same basic layer-by-layer deposition principle but with slightly different ways such as using powdered or liquid polymers, metals, metal-alloys or other materials to produce a desired product [8].
\nElectron beam melting (EBM), shown in Figure 14, is one of the additive manufacturing processes which fabricated titanium coatings by melting and deposition of metal powders, layer-by-layer, using a magnetically directed electron beam. Though this method was proved to be successful, it has high set-up costs due to the requirement of high vacuum atmosphere [7].
\nElectron beam melting method [1].
Selective laser melting (SLM), shown in Figure 15 is the second additive manufacturing method for titanium alloy coatings which completely melt the powder using a high-power laser beam. Similarly, this method is costly because it requires advanced high rate cooling systems. Moreover, the fluctuations of temperatures during processing negatively affect the quality of the products [1].
\nSelective laser melting method [1].
This chapter described the titanium as a metal that exists naturally with two crystalline forms. The chapter highlighted the properties of titanium metal that influence its application. The fact that titanium has advantageously unique properties that can be improved by alloying with other elements makes it to be preferred engineering material for future application in such areas as biomedical implants, aerospace, marine structures, and many others. The chapter discussed the traditional, current and future methods necessary to produce structures using titanium and titanium alloys. Further, the chapter suggested “additive manufacturing methods” as advanced methods for future manufacturing because they offer powerful solutions for making any type and number of products cost-effectively and with little waste. The examples of these methods are cold spray, 3-D printing, electron beam melting, and selective laser melting. Finally, the various processes used during fabrication of alloys using these methods were also presented.
\nAuthors are listed below with their open access chapters linked via author name:
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\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nJocelyn Chanussot (chapter to be published soon...)
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\\n\\nAbdul Latif Ahmad 2016-18
\\n\\nKhalil Amine 2017, 2018
\\n\\nEwan Birney 2015-18
\\n\\nFrede Blaabjerg 2015-18
\\n\\nGang Chen 2016-18
\\n\\nJunhong Chen 2017, 2018
\\n\\nZhigang Chen 2016, 2018
\\n\\nMyung-Haing Cho 2016, 2018
\\n\\nMark Connors 2015-18
\\n\\nCyrus Cooper 2017, 2018
\\n\\nLiming Dai 2015-18
\\n\\nWeihua Deng 2017, 2018
\\n\\nVincenzo Fogliano 2017, 2018
\\n\\nRon de Graaf 2014-18
\\n\\nHarald Haas 2017, 2018
\\n\\nFrancisco Herrera 2017, 2018
\\n\\nJaakko Kangasjärvi 2015-18
\\n\\nHamid Reza Karimi 2016-18
\\n\\nJunji Kido 2014-18
\\n\\nJose Luiszamorano 2015-18
\\n\\nYiqi Luo 2016-18
\\n\\nJoachim Maier 2014-18
\\n\\nAndrea Natale 2017, 2018
\\n\\nAlberto Mantovani 2014-18
\\n\\nMarjan Mernik 2017, 2018
\\n\\nSandra Orchard 2014, 2016-18
\\n\\nMohamed Oukka 2016-18
\\n\\nBiswajeet Pradhan 2016-18
\\n\\nDirk Raes 2017, 2018
\\n\\nUlrike Ravens-Sieberer 2016-18
\\n\\nYexiang Tong 2017, 2018
\\n\\nJim Van Os 2015-18
\\n\\nLong Wang 2017, 2018
\\n\\nFei Wei 2016-18
\\n\\nIoannis Xenarios 2017, 2018
\\n\\nQi Xie 2016-18
\\n\\nXin-She Yang 2017, 2018
\\n\\nYulong Yin 2015, 2017, 2018
\\n"}]'},components:[{type:"htmlEditorComponent",content:'New for 2018 (alphabetically by surname).
\n\n\n\n\n\n\n\n\n\nJocelyn Chanussot (chapter to be published soon...)
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nYuekun Lai
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPrevious years (alphabetically by surname)
\n\nAbdul Latif Ahmad 2016-18
\n\nKhalil Amine 2017, 2018
\n\nEwan Birney 2015-18
\n\nFrede Blaabjerg 2015-18
\n\nGang Chen 2016-18
\n\nJunhong Chen 2017, 2018
\n\nZhigang Chen 2016, 2018
\n\nMyung-Haing Cho 2016, 2018
\n\nMark Connors 2015-18
\n\nCyrus Cooper 2017, 2018
\n\nLiming Dai 2015-18
\n\nWeihua Deng 2017, 2018
\n\nVincenzo Fogliano 2017, 2018
\n\nRon de Graaf 2014-18
\n\nHarald Haas 2017, 2018
\n\nFrancisco Herrera 2017, 2018
\n\nJaakko Kangasjärvi 2015-18
\n\nHamid Reza Karimi 2016-18
\n\nJunji Kido 2014-18
\n\nJose Luiszamorano 2015-18
\n\nYiqi Luo 2016-18
\n\nJoachim Maier 2014-18
\n\nAndrea Natale 2017, 2018
\n\nAlberto Mantovani 2014-18
\n\nMarjan Mernik 2017, 2018
\n\nSandra Orchard 2014, 2016-18
\n\nMohamed Oukka 2016-18
\n\nBiswajeet Pradhan 2016-18
\n\nDirk Raes 2017, 2018
\n\nUlrike Ravens-Sieberer 2016-18
\n\nYexiang Tong 2017, 2018
\n\nJim Van Os 2015-18
\n\nLong Wang 2017, 2018
\n\nFei Wei 2016-18
\n\nIoannis Xenarios 2017, 2018
\n\nQi Xie 2016-18
\n\nXin-She Yang 2017, 2018
\n\nYulong Yin 2015, 2017, 2018
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USA, CRC Press Taylor & Francis, Asia Pacific, Trans Tech Publications Ltd., Switzerland, and Materials Science Forum, USA. 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