Li Tao
Associate Professor Supervisor of Doctorate Candidates Supervisor of Master's Candidates
Gender:Female
Alma Mater:Harbin Institute of Technology
Degree:Doctoral Degree
School/Department:School of Mechanical Engineering
Discipline:Mechanical Design and Theory. Intelligent Manufacturing. Mechanical Manufacture and Automation. Mechanical Engineering. Intelligent Manufacturing Technology
Business Address:8027 room, School of Mechanical Engineering building
E-Mail:
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Date:2019-10-19
Indexed by:Journal Article
Date of Publication:2019-08-01
Journal:JOURNAL OF CLEANER PRODUCTION
Included Journals:EI、SCIE
Volume:227
Page Number:58-69
ISSN:0959-6526
Key Words:Parameter optimization; Additive manufacturing; Gene expression programming; Tabu search; Multi-objective optimization
Abstract:The soaring global additive manufacturing (AM) market implies considerable potentials of energy and material savings. However, very few researches have addressed the energy and material efficiency issue in AM process through processing parameters optimization. In this study, we developed a predictive model of specific energy consumption (SEC) and metallic powder usage rate in laser cladding process. Three approaches were adopted to perform the modeling, namely, basic gene expression programming (GEP), response surface methodology (RSM), and integrated Tabu search and GEP (TS-GEP). Comparison amongst these methods revealed that TS-GEP demonstrated the highest fitting performance in terms of the root mean square deviation (RMSD) and coefficient of determination (R-2). The experimental validation showed that TS-GEP enabled high robustness and precision of the modeling even though the accuracy of prediction was slightly lower than that of RSM in some cases. Analysis of variance was conducted to examine the contribution of the processing parameters. Results presented that the dominating factor was powder feed rate followed by laser power, Z-increment, and scanning speed irrespective of the interactive effects. With the predictive models, the Pareto front was determined by non-dominated sorting genetic algorithm II (NSGA-II) to provide the optimal set of processing parameters for the maximization of energy and metallic powder efficiency. This study would facilitate appropriate parameter selection of laser cladding process and assist the sustainable manufacturing in AM domain. (C) 2019 Elsevier Ltd. All rights reserved.
Ph.D, associate professor, doctoral supervisor, graduated from Harbin Institute of technology, mainly engaged in product sustainability evaluation methods, laser repair technology, mechanical equipment energy consumption analysis and evaluation, enterprise information technology development and application. Reviewers of many domestic and foreign journals in related fields, presided over or participated in more than 20 projects of national key research and development plan, national 973 program, national Natural Science Foundation of China and enterprises, and published more than 100 papers. Ph.D Li has trained or assisted in the training of more than 50 doctoral and master degree students.
