Subjects -> MANUFACTURING AND TECHNOLOGY (Total: 363 journals)     - CERAMICS, GLASS AND POTTERY (31 journals)    - MACHINERY (34 journals)    - MANUFACTURING AND TECHNOLOGY (223 journals)    - METROLOGY AND STANDARDIZATION (6 journals)    - PACKAGING (19 journals)    - PAINTS AND PROTECTIVE COATINGS (4 journals)    - PLASTICS (42 journals)    - RUBBER (4 journals) MANUFACTURING AND TECHNOLOGY (223 journals)                  1 2

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 Integrating Materials and Manufacturing InnovationNumber of Followers: 7     Open Access journal ISSN (Print) 2193-9764 - ISSN (Online) 2193-9772 Published by SpringerOpen  [228 journals]
• A Machine Learning Strategy for Race-Tracking Detection During
Manufacturing of Composites by Liquid Moulding

• Abstract: Abstract This work presents a supervised machine learning (ML) model to detect race-tracking disturbances during the liquid moulding manufacturing of structural composites. Race-tracking is generated by unexpected resin channels at mould edges that may induce dry spots and porosity formation. The ML model uses the pressure signals recorded by a sensor network as input, providing a classification of the race-tracking event from a set of possible scenarios, and a subsequent variable regression for their position, size and strength. Such a model is based on the residual network (ResNet), a well-known artificial intelligence architecture that makes use of convolutional neural networks for image recognition. Training of the ML classifier and regressors was carried out with the aid of a synthetically generated simulation data set obtained throughout computational fluid dynamics simulations. The time evolution of the pressure sensors was used as grey-level images, or footprints, as inputs to the ResNet ML. The trained model was able to recognise the presence of race-tracking channels from the pressure data yielding good accuracy in terms of label prediction as well as position, size and strength. The model correlation was carried out with a set of injection experiments performed with a constant thickness closed mould containing induced race-tracking channels. The ability of ML models to provide an approximation to the inverse problem, relating the pressure sensor distortions to the cause of such events, is analysed and discussed.
PubDate: 2022-05-16

• Statistical Estimation of Strain Using Spatial Correlation Functions

• Abstract: Abstract Ex-situ estimation of strains from deformed micrographs is not possible as there are no persistent features which can be tracked. Two point spatial statistics enable the rigorous quantification of spatial patterns in heterogeneous media. In this paper, we propose a novel method for estimating strains directly from dissimilar micrographs using a continuum mechanics approach. Rather than operating directly on images from sequential frames, as is done in digital image correlation, we operate on different microstructure realizations. This is made possible by comparing the spatial autocorrelation maps of deformed and undeformed micrographs rather than direct comparison of images. A Bayesian framework is proposed for quantifying uncertainty. We first illustrate the efficacy of this method on speckle pattern images from digital image correlation experiments. Then, we demonstrate that the method is capable of operating on dissimilar micrographs using deformed synthetic binary microstructures. Finally, we present a case study on polycrystalline additively manufactured 316L deformed via tension. The proposed method works well and we discuss implications and limitations of the presented work.
PubDate: 2022-05-13

• Multi-fidelity Modeling for Uncertainty Quantification in Laser Powder Bed

• Abstract: Abstract Computer simulation of the additive manufacturing (AM) process involves multi-physics, multi-scale models. These sophisticated higher fidelity (HF) AM models, though more accurate, are computationally very expensive. On the other hand, AM process simulation using lower fidelity (LF) analytical models with simplified physics is fast but has significant prediction error. This paper presents a multi-fidelity (MF) modeling approach for constructing a prediction model for an AM process by fusing information from physics-based models of different fidelity and experimental data, thus maximizing the accuracy within the available computational resources. The LF model is corrected in two stages: first using the HF model simulation results and then the experimental data. A Bayesian calibration approach is used to estimate the correction factors and the MF model parameters to account for both process variability as well as model uncertainty. The proposed methodology is demonstrated by constructing a multi-fidelity model to predict the lack-of-fusion porosity in the laser powder bed fusion AM process, by combining an HF multi-physics computational model and an LF Rosenthal equation-based analytical solution. Further, an approach is developed to measure the effectiveness of the method by validating the prediction against experimental data.
PubDate: 2022-05-12

• Consistent Quantification of Precipitate Shapes and Sizes in Two and Three
Dimensions Using Central Moments

• Abstract: Abstract Computational microstructure design aims to fully exploit the precipitate strengthening potential of an alloy system. The development of accurate models to describe the temporal evolution of precipitate shapes and sizes is of great technological relevance. The experimental investigation of the precipitate microstructure is mostly based on two-dimensional micrographic images. Quantitative modeling of the temporal evolution of these microstructures needs to be discussed in three-dimensional simulation setups. To consistently bridge the gap between 2D images and 3D simulation data, we employ the method of central moments. Based on this, the aspect ratio of plate-like particles is consistently defined in two and three dimensions. The accuracy and interoperability of the method is demonstrated through representative 2D and 3D pixel-based sample data containing particles with a predefined aspect ratio. The applicability of the presented approach in integrated computational materials engineering (ICME) is demonstrated by the example of γ″ microstructure coarsening in Ni-based superalloys at 730 °C. For the first time, γ″ precipitate shape information from experimental 2D images and 3D phase-field simulation data is directly compared. This coarsening data indicates deviations from the classical ripening behavior and reveals periods of increased precipitate coagulation.
PubDate: 2022-04-11

• Data-Driven Modeling of Mechanical Properties for 17-4 PH Stainless Steel

• Abstract: Abstract This study examines the link between microstructure and mechanical properties of additively manufactured metal parts by developing a predictive model that can estimate properties such as ultimate tensile strength, yield strength, and elongation at fracture based upon microstructural data for 17-4 PH stainless steel. The main benefit of the approach presented is the generalizability, as necessary testing is further reduced in comparison with similar methods that generate full process–structure–property linkages. Data were collected from the available literature on AM-built 17-4 PH stainless steel, in-house tensile testing and imaging, and testing conducted by an AM company. After standardizing the image size and grain boundary extraction via image processing, the features such as grain size distributions and aspect ratios were extracted. By using artificial neural networks, relationships were established between grain size and shape features and corresponding mechanical properties, and subsequently, properties were predicted for novel samples to which the network had not previously been exposed. The model produced correlation coefficients of R2 = 0.957 for ultimate tensile strength, R2 = 0.939 for yield strength, and R2 = 0.931 for fracture elongation. These results demonstrate the efficacy of predictive models that focus upon microstructure–property relationships and highlight an opportunity for further exploration as predictive modeling of metal additive manufacturing continues to improve.
PubDate: 2022-04-11

• Data-Driven Multi-Scale Modeling and Optimization for Elastic Properties
of Cubic Microstructures

• Abstract: Abstract The present work addresses gradient-based and machine learning (ML)-driven design optimization methods to enhance homogenized linear and nonlinear properties of cubic microstructures. The study computes the homogenized properties as a function of underlying microstructures by linking atomistic-scale and meso-scale models. Here, the microstructure is represented by the orientation distribution function that determines the volume densities of crystallographic orientations. The homogenized property matrix in meso-scale is computed using the single-crystal property values that are obtained by density functional theory calculations. The optimum microstructure designs are validated with the available data in the literature. The single-crystal designs, as expected, are found to provide the extreme values of the linear properties, while the optimum values of the nonlinear properties could be provided by single or polycrystalline microstructures. However, polycrystalline designs are advantageous over single crystals in terms of better manufacturability. With this in mind, an ML-based sampling algorithm is presented to identify top optimum polycrystal solutions for both linear and nonlinear properties without compromising the optimum property values. Moreover, an inverse optimization strategy is presented to design microstructures for prescribed values of homogenized properties, such as the stiffness constant ( $$C_{11}$$ ) and in-plane Young’s modulus ( $$E_{11}$$ ). The applications are presented for aluminum (Al), nickel (Ni), and silicon (Si) microstructures.
PubDate: 2022-04-06

• A Comparison of Statistically Equivalent and Realistic Microstructural
Representative Volume Elements for Crystal Plasticity Models

• Abstract: Abstract Two methods used to construct a microstructural representative volume element (RVE) were evaluated for their accuracy when used in a crystal plasticity-based finite element (CP-FE) model. The RVE-based CP-FE model has been shown to accurately predict the complete tensile stress–strain response of a Ti–6Al–4V alloy manufactured by laser powder bed fusion. Each method utilized a different image-based technique to create a three-dimensional (3D) RVE from electron backscatter diffraction (EBSD) images. The first method, referred to as the realistic RVE (R-RVE), reconstructed a physical 3D microstructure of the alloy from a series of parallel EBSD images obtained using serial-sectioning (or slicing). The second method captures key information from three orthogonal EBSD images to create a statistically equivalent microstructural RVE (SERVE). Based on the R-RVEs and SERVEs, the CP-FE model was then used to predict the complete tensile stress–strain response of the alloy, including the post-necking damage progression. The accuracy of the predicted stress–strain responses using the R-RVEs and SERVEs was assessed, including the effects of each microstructure descriptor. The results show that the R-RVE and the SERVE offer comparable accuracy for the CP-FE purposes of this study.
PubDate: 2022-03-28

• Investigation of the Fatigue Life Scatter for AA7075-T6 Using Crystal
Plasticity Finite Element Method in the High to Very High Cycle Fatigue
Regime

• Abstract: Abstract Fatigue indicator parameters (FIPs) serve as the measurement of the driving force for fatigue crack initiation (FCI). The extreme value distributions of FIPs for fatigue life scatter (i.e., fatigue crack initiated from surface or subsurface) in the high cycle fatigue (HCF) to very high cycle fatigue (VHCF) regime are computed of AA7075-T6. The FIPs are averaged in the volume of sub-band in the scale of sub-grain proposed based on the concept of slip system of AA7075-T6 where sub-bands are extracted from statistical volume elements (SVEs) using open-source called Dream-3D. SVEs are built via the experimental crystalline microstructure. The material response variables required in the computation of FIPs are obtained by embedding crystal plasticity theory to the crystal plasticity finite element model via open-source software PRISMS-Fatigue. To meet statistical requirements, 50 instantiations of SVEs are modeled for both traction-free and fully periodic boundary conditions, respectively, corresponding to the surface and subsurface cases under different strain amplitudes (i.e., 0.362%, 0.312%, 0.289%, and 0.254%). It is found that the FIPs are higher under high stress levels indicating that fatigue crack tends to initiate at high stress levels. In addition, the low driving force can be found in the simulations with fully periodic boundary conditions throughout the SVEs, whereas the higher FIPs under traction-free boundary conditions tend to form close to the free surface under cyclic loading conditions. Furthermore, the highest FIPs have a propensity to scatter along with the depth of SVEs with the traction-free surface under strain amplitude of 0.254%, which means the fatigue life in the VHCF regime has a higher scatter. The interactions between the high symmetry of FCC crystalline structure and free surface combining the loading amplitudes have a compound effect on the FIPs, especially at low stress amplitudes, which also has a greater impact on the ultra-high-cycle fatigue life. To this end, the VHCF and HCF experiments are performed under an ultrasonic fatigue experimental system with a frequency of 20 kHz with different stress levels corresponding to the strain amplitude in the simulations. Besides, the morphology fracture surface of presentative samples was observed via the scanning electron microscope (SEM). The comparison between the simulation and the experimental results indicates that the EVD of FIPs is efficient in preciously predicting the FCI and well explain the fatigue life scatter of the AA7075-T6 in the HCF to VHCF regime.
PubDate: 2022-03-21

• Self-Supervised Deep Hadamard Autoencoders for Treating Missing Data: A
Case Study in Manufacturing

• Abstract: Abstract Data collected from sensors are pivotal to the Industrial Internet of Things (IIoT) applications as they would not be usable if the data quality is bad. Missing values commonly appear in industrial datasets due to several reasons. In particular, unexpected changes in the process, failure of the sensors that are recording the measurements due to network connectivity or hardware issues, censored/anonymous features and human error are some of the reasons for missing values in these datasets. In the manufacturing context, it is expensive and time consuming to collect all the different types of data related to a part leading to siloed and incomplete datasets. Due to characteristics such as presence of high missingness ratios, varying patterns of missingness, class imbalance and small-sized datasets, approaches such as single imputation or dropping the rows with missing values as is done in complete case analysis would result in loss of information and can also introduce a bias in the analysis depending on the underlying mechanism of missingness. The approach used for treatment of missing values must be carefully chosen based on the characteristics of the data at hand as the downstream generalization performance of algorithms trained on the imputed data would directly depend on the quality of imputation. In this work, we present a novel approach to treating missing values in a real-world dataset. Our approach uses deep Hadamard autoencoders (HAE) in addition to the self-supervised learning paradigm and shows better imputation performance on the unobserved entries of the test set as compared to approaches such as single imputation using mean, or other methods such as standard autoencoders (SAE). We demonstrate the effectiveness of the proposed method using a case study on a high-pressure die casting dataset (HPDC).
PubDate: 2022-03-14

• Computational Alloy Design for Process-Related Uncertainties in Powder
Metallurgy

• Abstract: Abstract An integrated computational materials engineering approach to the design of alloys for supersolidus liquid phase sintering has been developed. The method aims to minimize the sensitivity of the alloys to variabilities in material (e.g., composition) and process parameters (e.g., temperature) during sintering while also maximizing mechanical properties. This is achieved by developing a fast acting and high throughput design models that can quantify the processability and the resulting mechanical properties. A highly processable alloy is defined as one that is tolerant to both composition and process conditions such that changes in either do not materially affect the alloy properties. The design models are validated using experimental data from the literature and the computational design approach is demonstrated by designing unique high-speed steels with enhanced processability for powder metallurgy.
PubDate: 2022-03-10

• Crystal Elasticity Simulations of Polycrystalline Material Using Rank-One
Approximation

• Abstract: Abstract This study focuses on investigating alternative computationally efficient techniques for numerically estimating the mesoscale (grain and sub-grain scales) stress and strain in volume elements within an elastic constitutive framework. The underlying principle here lies in developing approximations for the localization tensor that relates the stress and strain fields at the component level to the mesoscale, using low rank approximations. The study proposes two methods to build low rank approximations of localization tensor using different mathematical principles. Numerical results are presented to discuss the relative merits of low rank approximation vis-a-vis full scale simulations across various metals.
PubDate: 2022-02-24

• A Numerical Approach to Model Microstructure Evolution for NiTi Shape
Memory Alloy in Laser Powder Bed Fusion Additive Manufacturing

• Abstract: Abstract Computer simulation and prediction of the microstructure of the additively manufactured parts via laser powder bed fusion is an essential part of the computational-based design of material and components. In this work, the microstructural evolution of NiTi alloys was simulated through a multiscale multiphysics modeling approach. The model consists of two computational modules for thermal simulation and microstructural evolution. The thermal history and the melt-pool dimensions obtained from the thermal simulation were in good agreement with the experimental micrograph of the samples. The Cellular Automata method was adopted for the microstructural simulation due to the lower computational cost compared to other techniques. The introduced Cellular Automata model generated an acceptable prediction of the microstructure. The simulation results indicated columnar austenite competitive grain growth in agreement with the observations on the fabricated samples. The validated model will serve as a base for future studies toward model-informed material and part design.
PubDate: 2022-02-15

• Advanced Acquisition Strategies for Lab-Based Diffraction Contrast
Tomography

• Abstract: Abstract Three new advanced acquisition strategies for lab-based diffraction contrast tomography are presented. They are named Helical Phyllotaxis, Helical Phyllotaxis Raster, and Helical Phyllotaxis HART and enable grain mapping of longer, larger, high-aspect ratio samples. The implementation of these advanced acquisition strategies combines a golden angle rotation with vertical and horizontal translations to perform a seamless data collection that has a uniform sample illumination both angularly and spatially. Reconstruction of the corresponding data is equally seamless, simultaneously using all data to reconstruct the full illuminated volume without the need for registration or stitching of data subsets or sample subvolumes. Examples of performing Helical Phyllotaxis and Helical Phyllotaxis Raster scans on a selection of samples, which have either been mapped previously or come from the same batch of samples, show a substantial reduction in data collection time and/or a significant improvement in grain statistics. The Helical Phyllotaxis HART (high-aspect ratio tomography for plate-like samples) strategy enables investigations of a hitherto inaccessible class of sample geometries comprising industrially relevant materials like rolled metal sheets and electrical steels. The advanced acquisition strategies take lab-based non-destructive 3D grain mapping to the next level of throughput, grain statistics and versatility and hold great promise for integrated computational materials science and engineering applications. While the throughput warrants 4D studies of materials microstructural evolution, a representative sample volume is a prerequisite for successful model predictions of the evolution, and the versatility enables studies of samples or components under more realistic in situ or in operando conditions.
PubDate: 2022-02-03

• XtalMesh Toolkit: High-Fidelity Mesh Generation of Polycrystals

• Abstract: Abstract A method for generating high-fidelity, boundary conforming tetrahedral mesh of three-dimensional (3D) polycrystalline microstructures is presented. With growing interest into subgrain scale micromechanics of materials, crystal plasticity finite element (CPFE) models must adapt not only their respective constitutive laws, but also their model geometry to the finer scale, namely the representation of grains and grain boundary junctions. Additionally, with the increasing availability of microstructure datasets obtained via 3D tomography experiments, it is possible to characterize the 3D topology of grains. From these advancements in experiment emerge both an opportunity and challenge for researchers to develop model microstructures, specifically finite element meshes, which best preserve grain topology for the accurate representation of boundary conditions in polycrystalline materials. To accomplish this, an open-source code called XtalMesh was created and is presented here. XtalMesh works by smoothing input voxel microstructure data using a feature-aware Laplacian smoothing algorithm that preserves complex grain topology and leverages state-of-the-art tetrahedralization code fTetWild to generate volume mesh. In this work, the workflow and associated algorithms of XtalMesh are described in detail using a synthetically generated example microstructure. For demonstration, we present a case study involving mesh generation of an experimentally obtained microstructure of nickel-based superalloy Inconel 718 (IN718).
PubDate: 2022-02-01

• Imputation Method Based on Collaborative Filtering and Clustering for the
Missing Data of the Squeeze Casting Process Parameters

• Abstract: Abstract The development of a highly efficient methodology for establishing squeeze casting process parameters from past data is essential. However, designing squeeze casting process parameters based on past data is difficult when there are many missing values. Conventional missing data approaches are fraught with additional computational challenges when applied to high-dimensional multivariable missing data, especially material process data with correlation. As the relationship between material composition and process parameters has similar characteristics with that between users and information of interest, this paper proposes a method for missing data imputation based on a clustering-based collaborative filtering (ClubCF) algorithm to address this challenge. Data samples with and without missing values were divided into two groups. K-means clustering based on a canopy algorithm was applied to the data samples without missing values to obtain k subclass data, whose values were then selected to fill data samples with missing values via a collaborative filtering theory based on Pearson similarity user filling. The missing squeeze casting process parameters data of aluminum alloys were used to evaluate the method, and more comparative experiments were carried out to understand their performance and features. Two different indicators, including the mean absolute error and the standard deviation, were utilized to quantify the imputation performance, which was compared with those of three conventional methods (mean interpolation, regression interpolation, and the expectation maximization algorithm). The results indicate that the proposed approach is effective and outperforms conventional methods in processing high-dimensional correlated data.
PubDate: 2022-01-27

• 3D Grain Shape Generation in Polycrystals Using Generative Adversarial
Networks

• Abstract: Abstract This paper presents a generative adversarial network (GAN) capable of producing realistic microstructure morphology features and demonstrates its capabilities on a dataset of crystalline titanium grain shapes. Alongside this, we present an approach to train deep learning networks to understand material-specific descriptor features, such as grain shapes, based on existing conceptual relationships with established learning spaces, such as functional object shapes. A style-based GAN with Wasserstein loss, called M-GAN, was first trained to recognize distributions of morphology features from function objects in the ShapeNet dataset and was then applied to grain morphologies from a 3D crystallographic dataset of Ti–6Al–4V. Evaluation of feature recognition on objects showed comparable or better performance than state-of-the-art voxel-based network approaches. When applied to experimental data, M-GAN generated realistic grain morphologies comparable to those seen in Ti–6Al–4V. A quantitative comparison of moment invariant distributions showed that the generated grains were similar in shape and structure to the ground truth, but scale invariance learned from object recognition led to difficulty in distinguishing between the physical features of small grains and spatial resolution artifacts. The physical implications of M-GAN’s learning capabilities are discussed, as well as the extensibility of this approach to other material characteristics related to grain morphology.
PubDate: 2022-01-21

• An Easy, Simple, and Accessible Web-based Machine Learning Platform,
SimPL-ML

• Abstract: Abstract Most machine learning (ML) platforms used in materials science provide prediction models built using a computational database. However, to provide more practical and accurate material property predictions, it is advantageous to build a prediction model with user’s own data. Here, we present a web-based ML platform, SimPL-ML (https://www.simpl-ml.org) that enables the user to build an ML prediction model through a simple process using their own data. Our platform, SimPL-ML, comprises four main parts: a dataset editor for dataset preprocessing, a model generator to perform actual model training, a predictor to provide a predicted target value for an arbitrary input, and a band-gap predictor (as an example case study) to predict the band gap of inorganic materials through several optimized band gap prediction models. In addition to its core functions, SimPL-ML provides additional functions such as atomic feature generation and hyper-parameter optimization for efficient ML research. We expect our platform to facilitate more accurate and efficient materials research through ML.
PubDate: 2022-01-21

• Data Mining and Visualization of High-Dimensional ICME Data for Additive
Manufacturing

• Abstract: Abstract Integrated computational materials engineering (ICME) methods combining CALPHAD with process-based simulations can produce rich, high-dimensional data for alloy and process design. In ICME methods for metallurgical applications, the visualization and interpretation of such high-dimensional data has previously been through heat maps represented in 2 or 3 dimensions. While such an approach is ideal when one variable is varied at a time, in the case of high-dimensional data with multiple variables varied simultaneously, as is the case in additive manufacturing, interpreting the trends through two- or three-dimensional heat maps becomes challenging. Here, we propose a strategy of mixed visual data mining and quantitative analysis for high-dimensional metallurgical and process data using high-throughput thermodynamic calculations. Two case studies show the application of the proposed approach. The first case study investigated the effects of feedstock chemistry on the $$\delta$$ ferrite formation in 316L stainless steel powders used for binder jet additive manufacturing. The second case study linked Scheil–Gulliver calculations to a process model for dissimilar joining of aluminum alloys 5356 and 6111 during laser hot-wire additive manufacturing. Both cases contained thousands of calculated data points, showcasing the utility of visual data analysis through parallel coordinate plotting, Pearson correlation coefficient matrices, and scatter matrices compared to traditional process maps. These visualization techniques can be extended to many additive manufacturing problems to capture process–structure–property relationships for additively manufactured components.
PubDate: 2022-01-21

• CrabNet for Explainable Deep Learning in Materials Science: Bridging the

• Abstract: Abstract Despite recent breakthroughs in deep learning for materials informatics, there exists a disparity between their popularity in academic research and their limited adoption in the industry. A significant contributor to this “interpretability-adoption gap” is the prevalence of black-box models and the lack of built-in methods for model interpretation. While established methods for evaluating model performance exist, an intuitive understanding of the modeling and decision-making processes in models is nonetheless desired in many cases. In this work, we demonstrate several ways of incorporating model interpretability to the structure-agnostic Compositionally Restricted Attention-Based network, CrabNet. We show that CrabNet learns meaningful, material property-specific element representations based solely on the data with no additional supervision. These element representations can then be used to explore element identity, similarity, behavior, and interactions within different chemical environments. Chemical compounds can also be uniquely represented and examined to reveal clear structures and trends within the chemical space. Additionally, visualizations of the attention mechanism can be used in conjunction to further understand the modeling process, identify potential modeling or dataset errors, and hint at further chemical insights leading to a better understanding of the phenomena governing material properties. We feel confident that the interpretability methods introduced in this work for CrabNet will be of keen interest to materials informatics researchers as well as industrial practitioners alike.
PubDate: 2022-01-17

• Temperature-Dependent Material Property Databases for Marine
Steels—Part 2: HSLA-65

• Abstract: Abstract Integrated Computational Materials Engineering (ICME)-based tools and techniques have been identified as the best path forward for distortion mitigation in thin-plate steel construction at shipyards. ICME tools require temperature-dependent material properties—including specific heat, thermal conductivity, coefficient of thermal expansion, elastic modulus, yield strength, flow stress, and microstructural evolution—to achieve accurate computational results for distortion and residual stress. However, the required temperature-dependent material property databases of US Navy-relevant steels are not available in the literature. Therefore, a comprehensive testing plan for some of the most common marine steels used in the construction of US Naval vessels was completed. This testing plan included DH36, HSLA-65, HSLA-80, HSLA-100, HY-80, and HY-100 steel with a nominal thickness of 4.76 mm (3/16-in.). This report is the second part of a seven-part series detailing the pedigreed steel data. The first six reports will report the material properties for each of the individual steel grades, whereas the final report will compare and contrast the measured steel properties across all six steels. This report will focus specifically on the data associated with HSLA-65 steel.
PubDate: 2022-01-14

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