Evidence map›Paper›PMID 36585590›Full record

ArticleEnvironmental science and pollution research international2022

Multi-objective parameter optimization of CNC plane milling for sustainable manufacturing.

Shun Jia, Shang Wang, Na Zhang, Wei Cai, Yang Liu, Jian Hao, Zhongwei Zhang, Yang Yang, Yang Sui

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Environmental science and pollution research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Shun JiaDepartment of Industrial Engineering, Shandong University of Science and Technology, Qingdao, 266590, China. jiashun@sdust.edu.cn.
Shang WangDepartment of Industrial Engineering, Shandong University of Science and Technology, Qingdao, 266590, China.
Na ZhangDepartment of Industrial Engineering, Shandong University of Science and Technology, Qingdao, 266590, China.
Wei CaiCollege of Engineering and Technology, Southwest University, Chongqing, 400715, China.
Yang LiuDepartment of Industrial Engineering, Shandong University of Science and Technology, Qingdao, 266590, China.
Jian HaoDepartment of Industrial Engineering, Shandong University of Science and Technology, Qingdao, 266590, China.
Zhongwei ZhangSchool of Mechanical & Electrical Engineering, Henan University of Technology, Zhengzhou, 450001, China.
Yang YangDepartment of Industrial Engineering, Shandong University of Science and Technology, Qingdao, 266590, China.
Yang SuiDepartment of Industrial Engineering, Shandong University of Science and Technology, Qingdao, 266590, China.

Funding

National Natural Science Foundation of China 71971130National Natural Science Foundation of China 72271146Project of Shandong Province Higher Educational Science and Technology Program J17KA167Project of Shandong Province Higher Educational Youth Innovation Science and Technology Plan Team 2021KJ060SDUST Research Fund 2018YQJH103
6 · The paper itself

Abstract

Energy modeling and cutting parameter optimization of the machining process have been recognized as powerful and effective ways to save energy. However, in the actual machining process, technologists often use empirical methods to determine the final cutting parameters. Due to the lack of theoretical support and optimization tools, this method is difficult to fully consider the constraints of machine tool capability, cutting tool performance, and workpiece material, which affects the overall performance of the machine tool to give full play. To address this problem, a multi-objective parameter optimization method of computer numerical control (CNC) plane milling for sustainable manufacturing was proposed in this paper. More specifically, three tasks were carried out: (1) an accurate milling energy model considering transient processes such as spindle acceleration was established; (2) a multi-objective parameter optimization model of CNC plane milling was established with cutting parameters as optimization variables and considering various complex constraints; (3) by drawing 3D surface maps, the internal relationship between the cutting parameters and the optimization index was presented in detail and intuitively. Finally, a case study was carried out in the XHK-714F vertical machining center. The results showed that the processing efficiency is improved by 21.0%, the energy consumption is reduced by 15.3%, and the surface roughness is reduced by 5.5% through the optimization of cutting parameters, which verified the effectiveness and feasibility of the proposed model and method.

Indexed as

Energy modelingEnergy-saving strategyParameter optimizationPlane millingPower modelingSustainable manufacturing

Identifiers

PMID36585590
PMCPMC9803406

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.