Integrating Materials and Manufacturing Innovation材料与制造创新整合
Integrating Materials and Manufacturing Innovation(英文缩写 INTEGR MATER MANUF I),ISSN 2193-9764,eISSN 2193-9772,中文译名:材料与制造创新整合 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 3.551 | Q3 |
| 2022 | 3.300 | Q3 |
| 2023 | 2.400 | Q3 |
| 2024 | 2.500 | Q3 |
| 2025 | 3.000 | Q3 |
Integrating Materials and Manufacturing Innovation 最新收录文献
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1. MicroProcSim: A Software for Simulation of Microstructure Evolution.
PMID:日期:2025-06-23Understanding the large deformation behavior of materials under external forces is crucial for reliable engineering applications. The mechanical properties of materials depend on their underlying microstructures, which change over time during deformation. Experimental observation of these processes is time-consuming and influenced by various conditions. Therefore, we developed MicroProcSim, a physics-based simulation tool to replicate the deformation process of cubic microstructures. MicroProcSim can predict the evolution of texture, represented by the orientation distribution function (ODF), over time under various loads and strain rates. This software package can be run on both Windows and Linux operating systems. Unlike conventional crystal plasticity finite element software, MicroProcSim offers a distinct advantage by rapidly generating deformed textures, as it bypasses incorporating grain morphology. Furthermore, comparisons with existing experimental and computational studies on texture evolution have demonstrated that this software seamlessly replicates real-world material processing conditions through a simple modification of a single input matrix. The online version of this article (10.1007/s40192-025-00405-6) contains an Editor's Video Summary, which is available to authorized users.
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2. Online Measurement for Parameter Discovery in Fused Filament Fabrication.
PMID:日期:2024-04-03To describe a new method for the automatic generation of process parameters for fused filament fabrication (FFF) across varying machines and materials. We use an instrumented extruder to fit a function that maps nozzle pressures across varying flow rates and temperatures for a given machine and material configuration. We then develop a method to extract real parameters for flow rate and temperature using relative pressures and temperature offsets. Our method allows us to successfully find process parameters, using one set of input parameters, across all of the machine and material configurations that we tested, even in materials that we had never printed before. Rather than using direct parameters in FFF printing, which is time-consuming to tune and modify, it is possible to deploy machine-generated data that captures the fundamental phenomenology of FFF to automatically select parameters.
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3. A Methodology for the Rapid Qualification of Additively Manufactured Materials Based on Pore Defect Structures.
PMID:日期:2024-02-27Additive manufacturing (AM) can create net or near-net-shaped components while simultaneously building the material microstructure, therefore closely coupling forming the material and shaping the part in contrast to traditional manufacturing with distinction between the two processes. While there are well-heralded benefits to AM, the widespread adoption of AM in fatigue-limited applications is hindered by defects such as porosity resulting from off-nominal process conditions. The vast number of AM process parameters and conditions make it challenging to capture variability in porosity that drives fatigue design allowables during qualification. Furthermore, geometric features such as overhangs and thin walls influence local heat conductivity and thereby impact local defects and microstructure. Consequently, qualifying AM material within parts in terms of material properties is not always a straightforward task. This article presents an approach for rapid qualification of AM fatigue-limited parts and includes three main aspects: (1) seeding pore defects of specific size, distribution, and morphology into AM specimens, (2) combining non-destructive and destructive techniques for material characterization and mechanical fatigue testing, and (3) conducting microstructure-based simulations of fatigue behavior resulting from specific pore defect and microstructure combinations. The proposed approach enables simulated data to be generated to validate and/or augment experimental fatigue data sets with the intent to reduce the number of tests needed and promote a more rapid route to AM material qualification. Additionally, this work suggests a closer coupling between material qualification and part certification for determining material properties at distinct regions within an AM part.
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4. AM Bench 2022 Macroscale Tensile Challenge at Different Orientations (CHAL-AMB2022-04-MaTTO) and Summary of Predictions.
PMID:日期:2024-01-16The additive manufacturing benchmarking challenge described in this work was aimed at the prediction of average stress-strain properties for tensile specimens that were excised from blocks of non-heat-treated IN625 manufactured by laser powder bed fusion. Two different laser scan strategies were considered: an X-only raster and an XY raster, which involved a 90° rotation in the scan direction between subsequent layers. To measure anisotropy, multiple tensile orientations with respect to the build direction were investigated (e.g., parallel, perpendicular, and intervals in between). Benchmark participants were provided grain structure information via electron backscatter diffraction measurements, as well as the stress-strain response for tensile specimens manufactured parallel to the build direction and produced by the XY scan strategy. Then, participants were asked to predict tensile properties, like the ultimate tensile strength, for the remaining specimens and orientations. Interestingly, the measured mechanical properties did not vary linearly as a function of tensile orientation. Moreover, specimens manufactured with the XY scan strategy exhibited greater yield strength than those corresponding to the X-only scan strategy, regardless of orientation. The benchmark data has been made publicly available for anyone that is interested [1]. For the modeling aspect of the challenge, five teams participated in this benchmark. While most of the models incorporated a crystal plasticity framework, one team chose to use a more semi-empirical approach, and to great success. However, no team excelled at all the predictions, and all teams were seemingly challenged with the predictions associated with the X-only scan strategy.
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5. Cross-Sectional Melt Pool Geometry of Laser Scanned Tracks and Pads on Nickel Alloy 718 for the 2022 Additive Manufacturing Benchmark Challenges.
PMID:日期:2024The Additive Manufacturing Benchmark Series (AM Bench) is a NIST-led organization that provides a continuing series of additive manufacturing benchmark measurements, challenge problems, and conferences with the primary goal of enabling modelers to test their simulations against rigorous, highly controlled additive manufacturing benchmark measurement data. To this end, single-track (1D) and pad (2D) scans on bare plate nickel alloy 718 were completed with thermography, cross-sectional grain orientation and local chemical composition maps, and cross-sectional melt pool size measurements. The laser power, scan speed, and laser spot size were varied for single tracks, and the scan direction was varied for pads. This article focuses on the cross-sectional melt pool size measurements and presents the predictions from challenge problems. Single-track depth correlated with volumetric energy density while width did not (within the studied parameters). The melt pool size for pad scans was greater than single tracks due to heat buildup. Pad scan melt pool depth was reduced when the laser scan direction and gas flow direction were parallel. The melt pool size in pad scans showed little to no trend against position within the pads. Uncertainty budgets for cross-sectional melt pool size from optical micrographs are provided for the purpose of model validation.
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6. Bi-directional Scan Pattern Effects on Residual Stresses and Distortion in As-built Nitinol Parts: A Trend Analysis Simulation Study.
PMID:日期:2023-01-30In this paper, a part-scale simulation study on the effects of bi-directional scanning patterns (BDSP) on residual stress and distortion formation in additively manufactured Nitinol (NiTi) parts is presented. The additive manufacturing technique of focus is powder bed fusion using a laser beam (PBF-LB), and simulation was performed using Ansys Additive Print software. The numerical approach adopted in the simulation was based on the isotropic inherent strain model, due to prohibitive material property requirements and computational limitations of full-fledged part-scale 3D thermomechanical finite element approaches. In this work, reconstructed 2D and 3D thermograms (heat maps) from in situ melt pool thermal radiation data, the predicted residual stresses, and distortions from the simulation study were correlated for PBF-LB processed NiTi samples using selected BDSPs. The distortion and residual stress distribution were found to vary greatly between BDSPs with no laser scan vector rotations per new layer, whereas negligible variations were observed for BDSPs with laser scan vector rotations per new layer. The striking similarities between the reconstructed thermograms of the first few layers and the simulated stress contours of the first lumped layer provide a practical understanding of the temperature gradient mechanism of residual stress formation in PBF-LB processed NiTi. This study provides a qualitative, yet practical insight towards understanding the trends of formation and evolution of residual stress and distortion, due to scanning patterns.
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7. Heterogeneous Deformation of As-Built Nickel Alloy 625 with Checkered Columnar Grains at Different Strain Rates (AMB2022-04).
PMID:日期:2023The Additive Manufacturing Benchmark Test Series (AM Bench) provides comprehensive measurement data for additive manufacturing (AM) simulations and modeling. As part of the primary AM Bench 2022 measurements, tensile testing at different strain rates and microstructural characterization on AM nickel alloy 625 (UNS N06625) in the as-built condition are performed. Tensile specimens are built with an X-Y alternating scan strategy on a commercial powder bed fusion machine and machined in accordance with ASTM E8 subsize specimen geometry. Each specimen is tested at a nominal strain rate of 10 s or 10 s on a servohydraulic material testing machine. Stereo digital image correlation (DIC) is used to report a virtual extensometer strain up to fracture and to show heterogeneous deformation with local crosshatch strain banding throughout the gauge section at both strain rates. Anisotropic deformation is also measured where the principal strains deviate from the isotropic strain path. Microstructural analysis using scanning electron microscopy (SEM) imaging and electron backscatter diffraction (EBSD) are performed to help illuminate the microstructural role in the anisotropy and heterogeneous deformation. Finally, nanoindentation followed by EBSD of the indents are performed to investigate the difference in the indentation response of different grain orientations and grain locations with respect to the laser track.
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8. Additive Manufacturing Benchmark 2022 Subcontinuum Mesoscale Tensile Challenge (CHAL-AMB2022-04-MeTT) and Summary of Predictions.
PMID:日期:2023This additive manufacturing benchmarking challenge asked the modelling community to predict the stress-strain behavior and fracture location and pathway of an individual meso-scale (gauge dimensions of approximately 200 μm thickness, 200 μm width, 1mm length) tension specimen that was excised from a wafer of nickel allow IN625 manufactured by laser powder bed fusion (L-PBF). The data used for the challenge questions and answers are provided in a public dataset (https://data.nist.gov/od/id/mds2-2587). Testing models against the data is still possible, although a good-faith blinded prediction should be attempted before reading this article, as the results are contained herein. The uniaxial tension test was pin loaded, conducted at quasi-static strain rates under displacement control, and strain was measured via non-contact methods (digital image correlation). The predictions are challenging since the number of grains contained in the thickness of the specimen are sub-continuum. In addition, pores can be heterogeneously distributed by the L-PBF process, as opposed to intentionally seeded defects. The challenge provided information on chemical composition, grain and subgrain structure (surface-based measurements via electron backscatter diffraction and scanning electron microscopy) and pore structure (volume-based measurements via X-Ray computed tomography) along the entire gauge length for the tension specimen. During the challenge, prediction responses were collected from six different groups. Prediction accuracy compared to the measurements varied, with elastic modulus and strain at ultimate tensile strength consistently over-predicted, while most other values were a mix of over- and under-predicted. Overall, no one model performed best at all predictions. Failure-related properties proved quite challenging to predict, likely in part due to the data provided as well as the inherent difficulty in predicting fracture. Future directions and areas of improvement are discussed in the context of improving model maturity and measurement uncertainty.
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9. Part Deflection Measurements of AM-Bench IN718 3D Build Artifacts.
PMID:日期:2023One of the primary barriers for adoption of additive manufacturing (AM) had been the uncertainty in the performance of AM parts due to residual stresses/strains. The rapid melting and solidification which occurs during AM processes result in high residual stresses/strains that produce significant part distortion. While efforts to mitigate residual stresses, such as post-process heat treatment, can reduce these effects, they nullify the benefits of the as-built component microstructure. Therefore, the ability to predict as-built component residual stresses and component deflection is crucial. AM-Bench seeks to provide modelers with high-fidelity data in well-characterized AM components to aid in model development and calibration. The measurements reported here are part of the 3D builds of nickel-based superalloy IN718 test objects for the CHAL-AMB2022-01-PD modeling challenges. The part deflection measurements were performed using a coordinate measurement machine after the part was partially separated from the build plate.
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10. Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design.
PMID:日期:2022-11-08There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure-property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.