支持向量机
自适应神经模糊推理系统
材料科学
基因表达程序设计
机器学习
人工神经网络
人工智能
镁
计算机科学
复合材料
模糊逻辑
模糊控制系统
冶金
作者
Maohua Li,Mohsen Mesbah,Alireza Fallahpour,Bahman Nasiri‐Tabrizi,Baoyu Liu
标识
DOI:10.1016/j.matlet.2021.130627
摘要
The relation between severe plastic deformation (SPD) and the mechanical behavior of the biodegradable magnesium (Mg) implants is not clearly understood yet. Thus, the present study aims to provide, for the first time, a framework for modeling the mechanical features of the ultrafine-grained (UFG) biodegradable Mg-based implant. First, an adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) were employed to determine relationships between SPD parameters, including the kind of metal forming process, the number of the pass, and temperature of the procedure based on the restricted training dataset. Second, gene expression programming (GEP) and genetic programming (GP) were then used to further verify the estimation capability of neural-based predictive machine learning techniques. Comparison of estimation results with real data confirmed that both ANFIS and SVM-based models had high accuracy for predicting the mechanical behavior of UFG Mg alloys for fracture fixation and orthopedic implants.
科研通智能强力驱动
Strongly Powered by AbleSci AI