Characteristic bands of oligosaccharide and polysaccharide.
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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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\r\n\tOptical images could be acquired either directly by using, e.g., a simple lens, or indirectly, as in the case of tomography, by using data acquisition hardware followed by a computational image reconstruction step. Computational optical imaging is a highly promising approach for direct or indirect imaging that involves the joint design of image acquisition hardware and digital processing algorithms to achieve imaging performance that would otherwise be unattainable by conventional systems.
\r\n\r\n\tDespite much advances in computational power, the processing (and storage) of acquired signals in applications such as real-time medical imaging, remote surveillance, and spectroscopy still pose a remarkable challenge. In addition, it may be too costly, or even physically impossible, to build optical hardware to acquire imaging data at the Nyquist sampling (measurement) rate that is required for high imaging performance, i.e., high resolution, large field-of-view, and high frame rates.
\r\n\r\n\tCompressed Sensing involves the digital construction of an image using a number of samples (measurements) that are significantly less than its dimension. By assuming that the unknown image is sparse in the domain where the measurements were acquired, one could use this sparsity constraint as prior information to obtain an approximate but accurate reconstruction of the image from relatively few samples.
\r\n\r\n\tThis book will be about the design and implementation of optical imaging systems, particularly, optical microscopy, optical coherence tomography, and hyperspectral imaging, using compressed sensing approaches. It will be particularly concerned with compressed sensing data acquisition approaches, and computational challenges involved in processing very large imaging data sets that traditionally result from 3D and/or real-time biomedical and industrial imaging applications.
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Ontario.",institutionString:"National Research Council Canada",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"National Research Council Canada",institutionURL:null,country:{name:"Canada"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"11",title:"Engineering",slug:"engineering"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"297737",firstName:"Mateo",lastName:"Pulko",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/297737/images/8492_n.png",email:"mateo.p@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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The NIR spectral region ranges from 800 to 2500 nm (12,500–4000 cm−1) with absorptions representing overtones and combinations mainly associated with –CH, –OH, –NH, and –SH functional groups [1]. NIR spectroscopy in combination with chemometric analyses can provide unique information in a wide field of applications from life sciences to environmental issues. It is more frequently used in the agricultural field [2–5], in particular, on the elucidation of nonstructural carbohydrates (NSCs) of plants. NSCs are products of the photosynthesis, providing substrates for growth and metabolism and can be stored by the plant playing a central role in the plant response to the environment [6, 7]. This type of carbohydrates is classified into monosaccharides (glucose and fructose), disaccharides (sucrose), polysaccharides (starch and fructans), oligosaccharides (raffinose), and sugar alcohols (inositol, sorbitol, and mannitol) [8, 9].
\nNIR spectroscopy is widely used to follow the chemical, physical, technological, or physiological processes that affect the structure and composition of carbohydrates found in many different organisms [10]. The success of this technique relies on the rapid and nondestructive analysis of the sample without the use of chemicals [11]. In addition, the data can be analyzed with chemometric methods. In this regard, partial least squares regression (PLSR) and principal component analysis (PCA) are two of the most recognized statistical methods that can be used to build NIR-chemometric models. PLSR is a well-established method for multivariate modeling and calibration [12]. Meanwhile, PCA analyzes data tables representing observations described by several dependent variables, which are, in general, intercorrelated [13].
\nThe objective of this chapter is to give a comprehensive overview of NIR spectroscopy for analyzing carbohydrates, such as glucose, fructose, sucrose, and fructans. In addition, we describe NIR spectroscopy and multivariate methods used to identify, classify, and quantify carbohydrates in plant tissues. Furthermore, we present the main applications of NIR-chemometrics on carbohydrate analyses.
The term “near” in NIR relies on the position of the electromagnetic energy lying next to or near the visible energy range. Molecular vibrations in the middle infrared (MIR) range cover absorptions bands between 2500 and 25,000 nm (4000 and 400 cm−1) representing the most intense and simplest bands in the whole infrared range, whereas NIR bands arise in the interval between 800 and 2500 nm (12,500 and 4000 cm−1) covering absorptions corresponding to overtones and combinations of fundamental vibrations [14]. NIR spectroscopy is concerned with both electronic and vibrational transitions [1]. Bands due to electronic transitions are observed in the NIR region and in general are presented as weak bands. Moreover, bands arising from overtones and combination modes are so-called forbidden transitions. Starting from the diatomic molecule as the simplest vibrating system, described by the harmonic and anharmonic oscillator, the study of more complex substances is referred to as polyatomic molecules [14].
\nThe NIR region can be divided into three regions. Region I spans from 800 to 1200 nm (12,500–8500 cm−1), also known as the “the short-wave NIR region (SWNIR),” “near-NIR region (NNIR),” or “the Herschel region,” represents bands resulting from electronic transitions, overtones, and combinations modes. Region II ranges from 1200 to 1800 nm (8500–5500 cm−1) and covers first overtones of XH (X = C, O, N), stretching vibrations and various types of combination modes. Finally, Region III (1800–2500 nm or 5500–4000 cm−1) is a combination mode region. Many applications utilize Regions II and III [1].
\nAbsorptions due to different functional groups, especially –CH, –OH, and –NH, are displayed as molecular overtones and combination vibrations at specific wavebands [15, 16]. NIR spectral data are influenced by a particle size (e.g., ground or powder) and need to be properly calibrated [17]. In Table 1, the characteristic bands of oligosaccharide and polysaccharide are listed.
Carbohydrate type | Waveband | Wavenumber | Reference | |
---|---|---|---|---|
nm | cm−1 | |||
Glucose | OH stretch 1st overtone | 2340, 2255, 2150, 2085, 1902, 1730, 1590, 1520, 1385, 1195 | 4274, 4435, 4651, 4796, 5258, 5780, 6289, 6579, 7220, 8368 | [19] |
Glucose | OH stretch/OH bend | 1688 | 5924 | [20] |
Sucrose | OH stretch 1st overtone | 1433 | 6978 | [20] |
Sucrose/glucose/fructose | OH combination | 1928 | 5186 | [21] |
Sucrose/glucose/fructose | OH stretch/CO stretch combination | 2123–2200 | 4710–4717 | [22] |
Crystalline sucrose | OH stretch 1st overtone | 1443–1440 | 6930–6944 | [23, 24] |
Polysaccharides | CH stretch/CH deformation combination | 2328 | 4295 | [25] |
Polysaccharides | OH stretch/CO stretch combination | 2274, 2271–2270 | 4398, 4403–4405 | [22, 25] |
Polysaccharides | OH combination | 2090 | 4785 | [25] |
Polysaccharides | OH stretch/OH bend | 1920 | 5208 | [25] |
Polysaccharides | CH combination/CH 1st overtone | 2328, 2270, 2078, 1920, 1587–1583 | 4295, 4405, 4813, 5208, 6300–6317 | [19, 22] |
Polysaccharides | OH stretch 1st overtone | 1437–1389 | 6960–7200 | [26–29] |
Characteristic bands of oligosaccharide and polysaccharide.
NIRS has been used as a fingerprint technique for all kinds of samples (liquids, solids, and semisolids), independently of their nature, relatively simple substances or pure compounds, most times they show broad and overlapping bands, it is impossible to correctly assign the specifically vibrations, and cannot be used for structural determination of a sample [18].
NIR spectra are characterized for their complexity and difficulty to be interpreted. For these reasons, multivariate methods from chemometrics are required to understand NIR spectra.
\nChemometrics comprise the development and use of mathematical and statistical methods for applications in chemistry. As a discipline, the aim of chemometrics is to provide methods to extract relevant chemical information out of measured chemical data in order to represent and display this information.
\nFigure 1 shows a general scheme for multivariate techniques, including the two different chemometric groups that are frequently employed in the NIR spectra analysis: the qualitative (classification) methods and the quantitative (regression) methods. As a first step, before choosing any method, usually NIR spectra are preprocessed with mathematical treatments, such as baseline correction, normalizations, derivatives, and smoothing, in order to enhance the relevant information and reduce the influence of side information contained in the spectra. The classification methods are used to group or separate the samples according to their spectra. The regression methods correlate the spectrum to quantifiable properties of the samples.
General scheme showing the commonly multivariate techniques employed by NIR spectroscopy.
The basic principles used for quantitative analysis are fundamentally invariable for all optical and spectral measurement methods. The principle behind any quantitative analysis is that the desired quantity, property, parameter, or compound can be determined from the signal obtained by an instrument, and this signal differs in a predictable manner for a given experimental system. The magnitude of the signal obtained can be correlated, directly or by mathematical algorithms, to the target characteristic properties of a sample. A common implementation of quantitative analysis is the determination of the concentration of a given analyte. For most applications, an attempt is made to linearize the relationship between the analyte and the instrument response, although this is not essential if a well-defined mathematical relationship can be established. This leads to the generation of a calibration from a characterized standard set (references) with the objective to construct a prediction model for a group of samples (Figure 2) [30].
Scheme for the construction of a quantitative model.
Many successful NIRS analysis have been performed using PLSR as a quantitative chemometric technique. Its usefulness derives from its potential to analyze data with numerous, noisy, collinear, and even incomplete variables. By establishing a linear relationship between two data matrices, the spectral data X and the reference values Y, through a linear multivariate model, the PLSR technique finds out the variables in the X matrix that will best define the Y matrix. In other words, it represents the NIR spectra in the space of wavelengths in order to display directions that will be linear combinations of wavelengths called factors that describe the studied property [31, 32].
Qualitative analyses are used for the classification of samples in accordance with their NIR spectra. Two general approaches can be used for qualitative classification: the unsupervised and the supervised methods. In the first approach, samples are classified lacking preceding knowledge, except the spectra. On the other hand, supervised methods require a prior knowledge of the sample, for instance, a category membership, generating a classification model with a training set of samples with well-established categories. The obtained model performance is evaluated by relating the classification predictions to the well-known categories of the validation samples [33].
\nPrincipal component analysis (PCA) is one of the most popular classification methods utilized in life sciences. PCA is used to visualize the most important information from a given data. One of the most significant advantages of PCA application is the reduction of the number of variables (scores), allowing the representation of a multivariate data table in a small dimensional area. Its purpose is to obtain significant information from the NIR spectra to express it as a set of new orthogonal variables called principal components (PC). The first principal component (PC1) defines the maximum variability scattered within the samples. A second principal component (PC2), uncorrelated and orthogonal to the first principal component, explains the maximum variability not described by the first component, this behavior continues with the next principal component (PC3), and so on [12]. Thereby, a display pattern of similarity of the variables as points in maps is created.
The near-infrared spectroscopy (NIRS) is a technique that allows the measurement of carbohydrates in a wide variety of samples. Nowadays, NIRS-chemometrics have proven their effectiveness for both qualitative and quantitative carbohydrate analysis. NIRS has several advantages such as allowing the sample remains intact after analysis and giving access to multiple chemical as well as physical properties at the same time [34].
\nNIR spectroscopy is generally chosen for its high-throughput screening, reduced sample preparation, low cost, and the nondestructive nature toward the analyzed sample [14]. However, establishing a suitable calibration demands a big effort and requires reference values for each sample, which makes it time-consuming and costly at the beginning [35].
\nIn the agrifood sector, the potential of NIRS have been widely investigated, this is a very powerful tool that provides meaningful information about internal and external properties of fruits, such as sugar content, total acidity, pH, soluble solid content, dry matter, firmness, and bruises, to mention some [36]. Moreover, NIRS can be applied to a wide variety of problems such as determination of particle size [38], determination of the best harvesting time [37], and investigation of geographical origin of foods such as apples, meat, and cheese [39].
\nHowever, and particularly to specific sugar content, NIRS in combination with PLSR models has been used in sorghum stalks [40] and sweet sorghum (cellulose, lignin, and hemicellulose) [41], fruit juices [42, 43], rice (amylose) [44], whey (lactose) [45], grasses (fructans) [46, 47], maize (nonstructural and water soluble carbohydrates) [48], intact apple fruit to determinate fructose, glucose, and sucrose [49], orange [50], apricot [51], sugar beet [52], cherries [53], and other fruits (Table 2). All these studies accorded that the performance of NIR spectroscopy is comparable to the reference chromatographic method, but the former is much faster and easier to carry out.
Sample | Carbohydrate | Analysis | Reference |
---|---|---|---|
Grain sorghum stalks | Sucrose, glucose | PLSR | [41] |
Fruit juices | Glucose, fructose, sucrose | PLSR, PCA | [43, 44] |
Rice | Amylose | mPLSs | [45] |
Whey | Lactose | PLS | [46] |
Grasses | Fructan | PLSR | [47, 48] |
Apple fruit | Glucose, fructose, sucrose | PLS | [50] |
Sugar beet | Sucrose | SEPs | [53, 55] |
Cherries | Total carbohydrates | PLSR | [54] |
Oranges | Glucose, fructose, sucrose | PLSR | [56, 57] |
Kiwifruit | Glucose, fructose, sucrose | PLS | [58] |
Potato | Glucose, fructose | PLSR | [59] |
Wine | Glucose | PLSR, PCR | [60] |
Cakes | Sucrose | MLR | [61] |
Syrup | Glucose, fructose, sucrose | PLSR | [62] |
Nules Clementine | Glucose, fructose, sucrose total carbohydrates | PLS, PCR | [63] |
Chinese yams | Total carbohydrates | PLS, PCA, LS-SVM | [64] |
Samples analyzed by NIR in a carbohydrate study.
mPLSs, various modified partial least square; PLS, partial least square; SEPs, standard errors of prediction; MLR, multiple linear regressions; and LS-SVM, least squares-support vector machine.
On the other hand, NIRS has been applied on food quality evaluation; it is often used to check if fruits or vegetables are green or rotten to detect surface defects. NIRS is also employed to check sugar concentrations, for instance, not only in apples [64], oranges [55, 56], mango [65], kiwifruits [57], sugar beet [54], peaches [66], jujube [67], onion [68], potato tubers [58], Nules Clementine [62], passion fruit [69], but also in fruit juices [43], wine [59], or cakes [60] (Table 2). Additionally, it has been used in breadstuff, dairy products, meat, vegetables, and fish products and in processed food to provide information about overtones and their combinations [70]. Moreover, studies have been performed to demonstrate that NIRS-chemometric analyses are of greater predictive value than mid-infrared data. In Chinese yams, Zhuang et al. [63] analyzed with NIR and MIR spectroscopy, the authors concluded that reasonable results were obtained using both spectral data sets and methods, but that NIR-chemometric data derived better prediction models.
\nIn respect to specific absorption peaks, sugar analyses have been carried out in fruit juices establishing that NIRS can deal with the distortions due to water clusters [20–22, 42].
\nNIR techniques have also been applied to measure biomass composition, especially on the presence of structural carbohydrates. The National Renewable Energy Laboratory (NREL) reported sorghum composition prediction models for glycan, xylan, lignin, starch, extractives, and ash [71].
\nNIR spectroscopy is not only useful in laboratory measurements sites but also applicable to online and field studies. The study of 116 syrup samples to compare a portable spectrometer and a benchtop device showed that the reduced wavelength range and reduced resolution of the portable device is sufficient to receive calibrations with R2 ≥ 0.96 for standard syrups with comparable standard error of prediction (SEP) values of 1.30 g/100 g versus 1.19 g/100 g, 0.94 g/100 g versus 0.99 g/100 g, and 2.04 g/100 g versus 2.46 g/100 g for glucose, fructose, and sucrose, respectively, to the handheld device [61]. The developed method is suitable to be implemented for quality control in the producing industry as well as in grocery stores.
\nA relevant novel application of the predictive models, particularly of the direct NIR prediction on diverse parameters on fruit quality was demonstrated. In Ref. [50], the authors compared two commercial portable spectrometers (Vis/NIR spectrometer versus OTF-NIR) for four orange varieties quality: soluble solids content, acidity, titratable acidity, maturity index, flesh firmness, juice volume, fruit weight, rind weight, juice volume to fruit weight ratio, fruit color index, and juice color index, and they found relevant the prediction of maturity index. The Lab spec spectrometer showed better predictive performance than the laminar instrument.
\nIn another study, a Lab spec Pro portable spectrophotometer to conduct an online classification of beef tenderness was also successful [72].
\nIn sugar-flour mixtures, NIR spectroscopy displayed proper results on the characteristic absorption bands of sugars, which are 1200 nm (8333 cm−1), 1437 nm (6959 cm−1), 2074 nm (4822 cm−1), and 2320 nm (4310 cm−1). However, it was not possible to distinguish various sorts of sugars, for instance, make a difference between the sucrose of the powdered sugar and the numerous carbohydrates present in the flour. Nevertheless, the identification of specific signatures of sugars can be very useful for rapid detection in the industrial sector [73].
\nHoney represents another class of samples that have proven the effectiveness of a NIR analysis [74]. In a study on Galicia honeys with protected geographical indication (PGI), the samples were processed by different chemometric methods to develop an authentication system specific to this type of honey. In this work, fifteen Galicia certificated PGI honeys were differentiated from other fifteen commercial available honeys by PCA, demonstrating that a single and fast chemometric method could be used to indicate the genuineness of Galicia PGI samples. Figure 3A shows the NIR spectra of all the analyzed samples and Figure 3B, illustrates the discrimination of Galicia PGI honeys from the other samples by the PCA plot.
(A) NIR spectra of honey samples. (B) Score plot of the honey samples in the space defined by the first two principal components. Adapted from Ref. [74].
Similarity, the potential use of NIR-PCA analysis to monitor sugar adulteration in onion powders was assessed through a detailed examination of the feasibility of quantification of cornstarch as an adulterating ingredient in onion powders [75]. Spectral analysis of 18 concentrations of starch in 180 onion powders, ranging from 0 to 35%, was conducted. The NIR spectra of the pure and adulterated onion powders (Figure 4A) reveal differences in the absorption intensities between 1920 (5208 cm−1) and 1980 nm (5051 cm−1). The absorption bands in these regions correspond to the O–H stretch and O–H band combination and the H–O–H deformation combination, which represents the starch content. The spectral fluctuations from 1400 (7143 cm−1) to 1600 nm (6250 cm−1) correspond to the first overtone of the hydroxyl group. The precise position of these bands is very sensitive to hydrogen bonding in the starch molecule, causing a difference between genuine and adulterated samples. The application of PCA (Figure 4B) resulted in a data grouping of each of the different concentrations used, working as a discriminative screening tool of authentic and adulated samples.
(A) Original NIR spectra of pure onion and starch onion mixtures at different concentrations. (B) Principal component score plot for the first three PCs for discrimination among different adulteration concentrations in onion powder. Adapted from Ref. [75].
Applications of NIRS have been developed also in the nutrition and health fields. NIR and MIR spectroscopy measurements and multivariate calibration methods based on partial least square regression have been used in a determination of fat, proteins, carbohydrates, and energy values in baby food, infant fast food, and canteen menus, with a simple, fast, and good predictive capabilities [70]. Another great diagnostic application is the measurement of blood glucose [1].
\nFinally, another notable capacity of NIRS was the prediction of carbohydrates concentrations, and distribution, leading to high ratio of performance to deviation (RPD) values, reducing the use of chemicals and working time, confirming that this makes a suitable technique of industry applications [61].
The potential of NIR spectroscopy in combination with chemometrics on carbohydrate analysis has been fully demonstrated. NIR is a powerful technique to study carbohydrates composition, type, and levels. This method can be used qualitatively and quantitative to detect, identify, and qualify carbohydrates. These unique capabilities enable the employment of NIR-chemometric in numerous applications: from state-of-the-art scientific experiments to on-line industrial processing control.
Thermomechanical processing in hot rolling mill in general involves rolling of slabs or blooms to thinner sections which are classified as plate or coils by inducing reduction in thickness by rolling at controlled elevated temperatures to achieve desired mechanical properties.
As shown in Figure 1, typical thermomechanical hot rolling process can be divided into five basic processes.
Typical setup of hot strip rolling mill.
The first step involves heating up of slabs to remove dendritic segregation and facilitate solutionizing of microalloying element which is intended to precipitate during hot rolling, contributing to the strength of the material.
Before inducing final reductions at finish rolling mill, slab is first introduced to be rolled in the roughing stands where thickness of slab is reduced from 200 to 300 mm to about 50 mm in several passes, usually four or five. In the roughing process, the width increases in each pass and is controlled by vertical edge rollers. Since the temperatures are high, recrystallization takes place during this process.
Finishing mill is generally a tandem rolling mill consisting of 5–7 rolling stands. The finishing temperature is dependent upon the rolling speed. The interpass heating, or cooling is also controlled during rolling.
After finish rolling, the hot rolled coil is subjected to cooling on runout table where water is sprayed on the top and bottom of the steel at a steady flow rate to induce phase and microstructure control leading to increased strengths.
The hot rolling process can be divided approaches based upon requirement of properties. They are chiefly identified as conventional controlled rolling (CCR) and recrystallization controlled rolling (RCR) (Figure 2). The recrystallization rolling requires rolling at high temperatures that leads to recrystallization and control of grain size. The process is designed to have mechanisms that inhibit grain growth after recrystallization. The conventional controlled rolling approach requires rolling in no-recrystallization zone, leading to unrecrystallized grains which ultimately lead to finer sizes after phase transformation (Figure 2).
Recrystallization controlled rolling and conventional controlled rolling.
The conventional controlled rolling shall be presented in detail as RCR has been mainly used for higher gauge rolling, i.e., plate mills or mills with lower rolling load capacities.
In general, in hot rolling method of conventional controlled rolling (CCR), methods of achieving strength would largely be dependent upon the grain size control of austenitic phase and eventually the phase transformation which is controlled by means of addition of microalloying elements in steel chemistry alongside or singly with application thermomechanical treatment practices during rolling.
The basic underlying principle of CCR is obtaining steel which possess both high toughness and strength through grain refinement. The successive methods to achieve finer grains are carried out as explained below:
Repeated deformation in roughing mill at recrystallization temperature range. Here, the austenitic grains are refined due to recrystallization.
Finish rolling which induces the successive heavy reduction in the non-recrystallization zone, i.e., rolling just above the non-recrystallization temperature TNR. During finish rolling gamma (austenite) grains are forced to elongate in rolling direction, creating annealing twin deformation bands to cause alpha (ferrite) to form with a very fine size. Thus, by inducing numerous nucleation sites for alpha grains, its size is restricted.
Accelerated cooling that additionally promotes refinement of the ferrite grain size and restricts formation of pearlite.
Addition of microalloying elements also helps in enhancing strength by restricting movement of grain boundaries by formation of precipitates and restricting transformation of gamma phase.
Static recrystallization is likely favored phenomenon in steels during roughing passes and for plain carbon steels it continues between finishing passes as well. The static recrystallization is favored by low alloying levels and high temperatures, strains, and strain rates.
The recovery is suppressed during finish interpasses, but as dislocation density is increasing on account of work hardening at finish rolling, dynamic recrystallization (Figure 3) is initiated after surpassing a critical strain value εC. Dynamic recrystallization is markedly identified by necklace-type grain structure. After dynamic recrystallization during rolling pass, the recrystallized nuclei continue to grow after the deformation ends, leading to a phenomenon called metadynamic recrystallization.
Stress-strain distribution with onset of recrystallization during rolling pass.
In controlled rolling process CCR, the addition of microalloying elements is deliberate to prevent static recrystallization. However, at low rolling temperatures, increased strain rates and lower interpass times coupled with lower precipitation, dynamic recrystallization is favored. As shown in Figure 3, the strain accumulates to peak strain and then decreases which differs on basis of type of steel.
There has been an established relationship [1] between the maximum peak stress
typical values of n′ = 4.5 and A = 0.12.
while Sun and Hawbolt [2] have reported peak Z dependent on initial grain size
The maximum peak strain
The dynamic recrystallization threshold strain
In order to obtain good dimensional tolerance and optimum mechanical properties after rolling, optimum rolling parameters need to be established. The usual method is to achieve this to calculate the dynamic mean flow stresses (MFS) at each rolling stand of roughing mill and more importantly at finish rolling mill. The MFS is chiefly dependent upon the alloying elements, rolling reduction, and temperature at each rolling pass and based upon these factors several models have been proposed [3].
A most widely used method, simplified rolling load versus inverse temperature, is plotted to determine the rolling conditions and has been depicted in Figure 4. When the mean flow stress, which is directly related to mill rolling load value, is plotted against the inverse absolute temperature, a slope kink signifying end of static recrystallization is observed. If rolling is accomplished below this temperature represented as
Plot of mean flow stress versus inverse of absolute temperature.
The onset of recrystallization during finish rolling is controlled largely by rolling interpass time. It has been reported [4] that for interpass intervals significantly longer than 1 second, static recrystallization takes place, whereas those involving interpass times of 15–100 ms, dynamic or metadynamic recrystallization is favored. Another important factor to include is consideration of strain-induced precipitation, which inhibits recrystallization phenomenon.
When high strength low-alloyed steels are finish rolled, an additive buildup of strain causes an increase in MFS consecutively after each pass. When a critical strain value is surpassed, dynamic recrystallization is initiated, and a small drop in load caused by metadynamic recrystallization is observed during end of rolling. In carbon-manganese grades, this may be associated with the austenite to ferrite transformation.
As thermomechanical processing involves microstructural evolution in terms of static and dynamic recrystallization that takes place during the rolling process, the mean flow stress shall also depend upon these considerations also.
A model equation for mean flow stress prediction for carbon-manganese grades by Misaka [5] has been proposed as below:
Various researchers (Siciliano et al. [3], Sun and Hawbolt [2]) have worked upon refinement of the equation to include effect of recrystallization that would affect the value of MFS.
Recrystallization criteria are a function of initial grain size d0, strain
If
where Z is Zener-Holloman parameter defined by
To calculate type of crystallization that occurs at a particular pass, the strain values need to be compared against critical strain value.
If εa (strain at a pass) is less than the critical strain εc, static recrystallization is favored, leading to grain size governed by the equation:
Time for 50% completion of recrystallization (static) is
Grain growth during interpass time
If εa (strain at a pass) is greater than the critical strain εc, dynamic recrystallization is favored, leading to grain size governed by equation:
Time for 50% completion of recrystallization (dynamic) is
Grain growth during interpass time
Accumulated strain
where fractional softening X is governed by recrystallization:
The value of q shall depend upon the type of recrystallization.
For static recrystallization (SRX), q = 1.0.
For metadynamic recrystallization (MDRX), q = 1.5.
The modified mean flow stress modeling equation incorporating effect of manganese addition shall be
where
where
and where
High-strength steel requires tensile properties as main requirement, whereas the requirements such as weldability and ductility are also of chief importance. Therefore, carbon which is the chief source of strength should not exceed very high values, and hence high-strength steel requires addition of alloying elements.
The addition of microalloying elements can be divided into two categories [6]:
Microalloying elements: niobium, vanadium, titanium, aluminum, and boron.
Substitutional elements: silicon, manganese, molybdenum, copper, nickel, and chromium.
Microalloying elements such as niobium, titanium, and vanadium are principally carbide-forming elements. Although the addition of these elements in steel raises its Ar3 temperature, they retard austenite transformation to ferrite by restricting carbon diffusion. Strengthening by addition of one or all of niobium, vanadium, or titanium has shown a remarkable increase in strength of steel. The strengthening phenomenon is caused by fine precipitation of nitrides, carbides, or carbonitrides which are coherent with ferrite matrix but induce strengthening by impeding dislocation movement.
One of the most significant effect of adding individually or simultaneous addition of V, Nb, and Ti is to decrease recrystallization temperature. Which contributes in generating a finer size of gamma (austenite) grains during finish rolling.
The two principal mechanisms that inhibit recrystallization and eventually grain growth are particle pinning and solute drag.
The grain boundary movement can be accounted on strain-induced precipitation of micro carbides on gamma grain boundaries that limit the gamma grain size. Addition of titanium or niobium helps in suppressing gamma grain growth by means of nitride or carbonitride precipitates which are majorly present at grain boundaries and inhibit their movement.
In the case of vanadium addition, addition of nitrogen can be helpful in increasing the strength and toughness. The vanadium nitride precipitates are useful in imparting strength to the steel. The addition of nitrogen however attributes to poor weldability. Likewise, the strengthening may be achieved by adding niobium, but a higher niobium content is bound to give poor weldability. Hence the conventional methods require simultaneous addition of V and Nb.
The improvement of toughness can be achieved through addition of manganese that leads to decrease in Ar3 temperature. Due to decrease in Ar3 coupled with low coiling temperatures, the alpha (ferrite) grains are refined, thus increasing the strength. Additionally, the fine precipitate size is contributed by niobium carbonitrides and vanadium nitrides.
In general, austenitic grains starts to recrystallize at temperatures above 1050°C. Since an initial finer gamma grain size is helpful in creating a final finer size of alpha, lower reheating temperatures are effective. Also, the microalloying elements also add to refinement of the austenitic grain size by means of undissolved carbides and nitrides that restrict initial austenitic grain size.
Eventually, a lower slab reheating temperature by contributing fine austenitic grain size and lower temperature rolling at roughing mill will induce even finer grain size by reduction at lower temperatures.
Due to repeated reduction in roughing mill, both recrystallization and precipitation are competing phenomena. However, at higher temperatures recrystallization will be the first phenomenon. As a basic principle of controlled rolling demands that precipitation should occur during finish rolling, it has been recommended to have higher roughing temperatures along with short rougher interpass intervals.
The gamma grain is refined by repeated static recrystallization caused during roughing mill action. Increasing rolling strain has marked effects on facilitating static recrystallization caused by higher dislocation density and increased nucleation sites caused by fine size of austenite which in turn leads to softening of material. However, there is a limiting value of grain refinement.
As has been elucidated above, no-recrystallization temperature (TNR) is important in design of controlled rolling process. This temperature determines where strain is multiplied for austenite grains, leading to strain-induced precipitation of carbonitrides as well as enhanced sites for a fine-grain size ferrite to be nucleated at the sites. Hot rolling being a dynamic process, no-recrystallization temperature depends upon deformation parameters. The influencing factors for TNR are composition of the steel, strain values applied in each pass, the strain rate, and the rolling interpass time [7, 8].
During finish rolling the value of TNR tends to dynamically lower down as the strain value or the reduction increases. This phenomenon is attributable on account of static recrystallization caused by increased recrystallization sites owing to finer grains and higher dislocation density induced during each rolling pass.
The strain rate value is also a determining factor for the onset of dynamic recovery and facilitates static recrystallization which eventually decreases the TNR.
During controlled rolling, the interpass time during each rolling reduction also plays an important role as the prime requirement is to roll below TNR temperatures. The precipitation kinetics are accelerated due to strains induced when rolling below TNR. A lower interpass time is preferable as higher interpass will lead to coarsening of precipitate sizes as well as increased tendency of recrystallization detrimental to final strength value of steel.
The runout table and the coiler in general act like post heat treatment unit which makes possible to achieve phase transformation through control of cooling to generate coils with varied properties and microstructures.
Accelerated cooling after hot rolling leads to further refinement of grains and phase control, leading to enhancement of properties. The phenomenon for strengthening of microstructure is phase transformations in terms of microstructures avoiding pearlitic transformations, precipitation strengthening through carbides, and nitride precipitates which along with controlled cooling rates lead to the refinement of grain size in the resulting microstructure. The accelerated cooling may be classified into two techniques—continuous accelerated cooling and interrupted accelerated cooling.
The final mechanical properties after accelerated cooling are majorly influenced by the alloying content and hot rolling parameters.
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