Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algor  

作　　者：Parth Khandelwal Harshit Indranil Manna 

机构地区：[1]Metallurgical&Materials Engineering Department,Indian Institute of Technology,Kharagpur,West Bengal,721302,India [2]Computer Science&Engineering Department,Indian Institute of Technology,Kharagpur,West Bengal,721302,India [3]Vice Chancellor Office,Birla Institute of Technology(BIT)Mesra,Ranchi,Jharkhand,835215,India

出　　处：《Computers, Materials & Continua》2024年第4期1727-1755,共29页计算机、材料和连续体（英文）

摘　　要：Metallic alloys for a given application are usually designed to achieve the desired properties by devising experimentsbased on experience, thermodynamic and kinetic principles, and various modeling and simulation exercises.However, the influence of process parameters and material properties is often non-linear and non-colligative. Inrecent years, machine learning (ML) has emerged as a promising tool to dealwith the complex interrelation betweencomposition, properties, and process parameters to facilitate accelerated discovery and development of new alloysand functionalities. In this study, we adopt an ML-based approach, coupled with genetic algorithm (GA) principles,to design novel copper alloys for achieving seemingly contradictory targets of high strength and high electricalconductivity. Initially, we establish a correlation between the alloy composition (binary to multi-component) andthe target properties, namely, electrical conductivity and mechanical strength. Catboost, an ML model coupledwith GA, was used for this task. The accuracy of the model was above 93.5%. Next, for obtaining the optimizedcompositions the outputs fromthe initial model were refined by combining the concepts of data augmentation andPareto front. Finally, the ultimate objective of predicting the target composition that would deliver the desired rangeof properties was achieved by developing an advancedMLmodel through data segregation and data augmentation.To examine the reliability of this model, results were rigorously compared and verified using several independentdata reported in the literature. This comparison substantiates that the results predicted by our model regarding thevariation of conductivity and evolution ofmicrostructure and mechanical properties with composition are in goodagreement with the reports published in the literature.

关 键 词：Machine learning genetic algorithm SOLID-SOLUTION precipitation strengthening pareto front data augmentation 

分 类 号：TG146[一般工业技术—材料科学与工程] TP181[金属学及工艺—金属材料]