Advances in Manufacturing ›› 2023, Vol. 11 ›› Issue (4): 708-751.doi: 10.1007/s40436-022-00417-x
• • 上一篇
Shailendra Chauhan, Rajeev Trehan, Ravi Pratap Singh
收稿日期:
2022-02-17
修回日期:
2022-04-09
发布日期:
2023-10-27
通讯作者:
Ravi Pratap Singh,E-mail:singhrp@nitj.ac.in
E-mail:singhrp@nitj.ac.in
作者简介:
Shailendra Chauhan is currently pursuing Ph.D. in the Department of Industrial & Production Engineering at Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India. He has received the M.E. in Mechanical Engineering (Manufacturing Technology) from the Department of Mechanical Engineering, NITTTR, Chandigarh, India. His broad areas of research are; manufacturing processes, milling operation, advanced materials, and their processing, optimization and modeling, etc.Shailendra Chauhan, Rajeev Trehan, Ravi Pratap Singh
Received:
2022-02-17
Revised:
2022-04-09
Published:
2023-10-27
Contact:
Ravi Pratap Singh,E-mail:singhrp@nitj.ac.in
E-mail:singhrp@nitj.ac.in
摘要: Over a century, metal cutting has been observed as a vital process in the domain of manufacturing. Among the numerous available metal-cutting processes, milling has been considered as one of the most employable processes to machine a variety of engineering materials productively. In the milling process, material removal occurs when the workpiece is fed against a rotating tool with multiple cutting edges. In order to maximize the profitability of metal cutting operations, it is essential that the various input and output variable relationships are analyzed and optimized. The experimental method of studying milling processes is costly and time demanding, particularly when a large variety of elements such as cutting tool shape, materials, cutting conditions, and so on, are included. Due to these issues, other alternatives emerged in the form of mathematical simulations that employ numerical methods. The finite element approaches have well-proven to be the most practical and commonly utilized numerical methods. The finite element model (FEM) can be used to determine the various physical interactions occurring during the machining process along with the prediction of various milling characteristics, such as cutting forces, cutting temperature, stresses, etc., with the help of milling inputs. In the present article, various research studies in the broad milling process domain practiced with numerous finite element approaches have been critically reviewed and reported. It further highlights the several experimental and finite element approaches-based research studies that attempted to analyze and optimize the overall performance of the different milling processes. In recent years, various investigators have explored numerous ways to enhance milling performance by probing the different factors that influence the quality attributes. Some of the studies have also been found to be focused on the economic impacts of milling and various process inputs that affect milling performance. Furthermore, various milling factors’ impact on the performance characteristics are presented and critically discussed. The issues related to the recent improvements in tool-work interaction modeling, experimental techniques for acquiring various milling performance measures, and the aspects of turn and micro-milling with finite element-based modeling have been further highlighted. Among the various available classifications in the milling process as employed in industries, face milling is more strongly established compared to other versions such as end milling, helical milling, gear milling, etc. The final section of this research article explores the various research aspects and outlines future research directions.
The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00417-x
Shailendra Chauhan, Rajeev Trehan, Ravi Pratap Singh. State of the art in finite element approaches for milling process: a review[J]. Advances in Manufacturing, 2023, 11(4): 708-751.
Shailendra Chauhan, Rajeev Trehan, Ravi Pratap Singh. State of the art in finite element approaches for milling process: a review[J]. Advances in Manufacturing, 2023, 11(4): 708-751.
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