Resource environment load prediction method for metal material machining based on process condition similarity matching

Frontiers in Materials

Materials

Zhipeng Xing¹  Haicong Dai²  Jiaji Xiong³  Yufeng Li³  Jiong Zhang³ 

[1] Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, China;CETC Information Science Academy, Beijing, China;Shanghai Aerospace Equipments Manufacturer Co., Ltd., Shanghai, China;State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing, China;

关键词: environment performance;    process condition;    resource environment load;    similarity matching;    machining;   

DOI  :  10.3389/fmats.2023.1129850

 received in 2022-12-22, accepted in 2023-01-20,  发布年份 2023

来源: Frontiers

【 摘 要 】

Introduction: Resource environment load data are important for analyzing and improving the environmental performance, which are affected by the process condition of metal material machining processes. However, the environmental performance assessment in previous research focused on the results under the specific process conditions. The resource environment load data need to be re-collected when the process conditions are changed for a credible assessment, which is time- consuming and tedious.Methods: This paper proposed a process condition- oriented prediction method of resource environment load data with limited samples. The significance of process condition elements on the resource environment load data was analyzed, and then the resource environment load was predicted according to the similarity between the process condition to be predicted and the existing process conditions.Results and Dicussion: The results show that the average prediction accuracy of this method exceeds 90%, and further the accuracy for predicting the environmental performances using the predicted data is more than 93% which would help process designers to choose the better process condition for machining the metal materials.

【 授权许可】

Unknown   
Copyright © 2023 Xing, Dai, Xiong, Zhang and Li.