Machine Learning Based Sintered Density Prediction of Bronze Processed by Powder Metallurgy Route

Kamal, Tafzeelul; Gouthama,; Upadhyaya, Anish

doi:10.1007/s12540-022-01338-x

Cited by 5 publications

(3 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typical uses involve process optimization, flaw detection, and microstructure characterization, as well as attribute estimation of the end output [27][28][29]. Because of the widespread incorporation of Al 2 O 3 , TiC, SiC, and B4C particles into the metallic matrix, ML has already been extensively applied in MMCs [24,30]. The mechanical properties of AMCs with various additions, including Al/Al 2 O 3 [31][32][33], AA2219/Al 2 O 3 /TiC [34], A356/Al 2 O 3 [35], A356/B4C [36], AA6061/Al 2 O 3 /SiC [37], and Al-Si-Mg/Al 2 O 3 /SiC [38,39], have all been predicted using ML.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Powder Metallurgy Parameters to Enhance the Mechanical Properties of Al-4Cu/xAl2O3 Composites Using Machine Learning and Response Surface Approaches

et al. 2023

View full text Add to dashboard Cite

This study comprehensively investigates the impact of various parameters on aluminum matrix composites (AMCs) fabricated using the powder metallurgy (PM) technique. An Al-Cu matrix composite (2xxx series) was employed in the current study, and Al2O3 was used as a reinforcement. The performance evaluation of the Al-4Cu/Al2O3 composite involved analyzing the influence of the Al2O3 weight percent (wt. %), the height-to-diameter ratio (H/D) of the compacted samples, and the compaction pressure. Different concentrations of the Al2O3 reinforcement, namely 0%, 2.5%, 5.0%, 7.5%, and 10% by weight, were utilized, while the compaction process was conducted for one hour under varying pressures of 500, 600, 700, 800, and 900 MPa. The compacted Al-4Cu/Al2O3 composites were in the form of cylindrical discs with a fixed diameter of 20 mm and varying H/D ratios of 0.75, 1.0, 1.25, 1.5, and 2.0. Moreover, the machine learning (ML), design of experiment (DOE), response surface methodology (RSM), genetic algorithm (GA), and hybrid DOE-GA methodologies were utilized to thoroughly investigate the physical properties, such as the relative density (RD), as well as the mechanical properties, including the hardness distribution, fracture strain, yield strength, and compression strength. Subsequently, different statistical analysis approaches, including analysis of variance (ANOVA), 3D response surface plots, and ML approaches, were employed to predict the output responses and optimize the input variables. The optimal combination of variables that demonstrated significant improvements in the RD, fracture strain, hardness distribution, yield strength, and compression strength of the Al-4Cu/Al2O3 composite was determined using the RSM, GA, and hybrid DOE-GA approaches. Furthermore, the ML and RSM models were validated, and their accuracy was evaluated and compared, revealing close agreement with the experimental results.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimizing the Powder Metallurgy Parameters to Enhance the Mechanical Properties of Al-4Cu/xAl2O3 Composites Using Machine Learning and Response Surface Approaches

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Few research papers have demonstrated the suitability of ML models in the quality assessment of sintered components. These models have been employed for the estimation of mechanical and fatigue properties [22], [24], or density estimation of sintered bronze [23]. Here, we focus on the prediction of different QCs, i.e., mass and lengths, for more complex, i.e., multilevel, workpieces.…”

Section: Introductionmentioning

confidence: 99%

“…Here, we focus on the prediction of different QCs, i.e., mass and lengths, for more complex, i.e., multilevel, workpieces. More importantly, while in [22], [24], and [23] the input features used by the ML models are mainly limited to the composition of the alloy components and the static summary of the production process, here we design and utilize features that fully characterize the dynamics of the production process of sintered workpieces, regardless of the components' properties, to predict their QCs. Moreover, while previous works employ ML models based on artificial neural networks (ANNs), which require long training times and expensive optimization procedures to tune their hyper-parameters, here we show that QCs can be effectively predicted by harnessing simpler and more quickly trainable ML models, with lower computational time complexity.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Mass and Lengths of Sintered Workpieces Using Machine Learning Models

Massimo,

Ganthaler,

Buriro

et al. 2023

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

Process–Material–Performance Trade-off Exploration of Materials Sintering with Machine Learning Models

Kakanuru,

Terway,

Jha

et al. 2024

Integr Mater Manuf Innov

View full text Add to dashboard Cite

Process-induced porosity, defects, and residual stresses lead to mechanical performance degradation in fiber-reinforced composite and other heterogeneous structures. Physical and chemical processes create complex process–material–performance relationships. Predicting porosity and residual stresses in this context requires computationally burdensome forward simulations and obtaining optimal process settings and calibrating properties of new materials requires solving inverse problems with predictions from the forward simulations. In this paper, we parameterized the process–material–performance space and created a dataset based on physics models that are valid for sintering ceramic powders. The dataset was used to train several machine learning models that captured the process–material–performance relationships. The trained ML models were applied in process optimization, calibration of properties for new material systems, and estimating performance for a given process and material. Support vector regression (SVR), convolutional neural networks (CNNs), and a Gaussian mixture model (GMM) called REPAIRS were selected, and their prediction accuracy was determined. While the SVR and CNN models require training several models, we show that the GMM model captures the process–material–performance relationships with a single machine-learned model and partial system completion methods. The paper describes root-mean-square error and mean absolute percentage errors of the inferences from the models on a validation dataset.

show abstract

Machine Learning Based Sintered Density Prediction of Bronze Processed by Powder Metallurgy Route

Cited by 5 publications

References 51 publications

Optimizing the Powder Metallurgy Parameters to Enhance the Mechanical Properties of Al-4Cu/xAl2O3 Composites Using Machine Learning and Response Surface Approaches

Optimizing the Powder Metallurgy Parameters to Enhance the Mechanical Properties of Al-4Cu/xAl2O3 Composites Using Machine Learning and Response Surface Approaches

Estimation of Mass and Lengths of Sintered Workpieces Using Machine Learning Models

Process–Material–Performance Trade-off Exploration of Materials Sintering with Machine Learning Models

Contact Info

Product

Resources

About