| FAN Xiao-jian,TIAN Jian-yan,YANG Ying-bo,JIAN Long,YANG Sheng-qiang.Fusion Decision Model of Tumbling Chip Abrasives Based on Improved D-S Evidence Theory[J],50(4):393-401 |
| Fusion Decision Model of Tumbling Chip Abrasives Based on Improved D-S Evidence Theory |
| Received:August 10, 2020 Revised:November 24, 2020 |
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| DOI：10.16490/j.cnki.issn.1001-3660.2021.04.042 |
| KeyWord:tumbling chip abrasives intelligent optimization fusion decision D-S evidence theory frame of discernment basic probability assignment |
| Author | Institution |
| FAN Xiao-jian |
School of Electrical and Power Engineering, Taiyuan , China |
| TIAN Jian-yan |
School of Electrical and Power Engineering, Taiyuan , China |
| YANG Ying-bo |
School of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan , China |
| JIAN Long |
School of Electrical and Power Engineering, Taiyuan , China |
| YANG Sheng-qiang |
School of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan , China |
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| Abstract: |
| As the primary solid medium in the barrel finishing process, the tumbling chip abrasives has a great influence on the processing effect. In order to effectively utilize the case knowledge and expert experience of the database of barrel finishing process, and improve the accuracy of the optimization of tumbling chip abrasives when the new part are processed, the research group have established the optimization model of tumbling chip abrasives based on case-based reasoning, expert reasoning and transfer learning respectively. However, optimization result only based on three independent methods had low reliability,for the new part to be processed, there will be conflicts among the three optimization results, so that it is necessary to make a fusion decision for the three optimization results. Therefore, a fusion decision model of tumbling chip abrasives based on improved D-S evidence theory is proposed. Firstly, the optimization results of case-based reasoning, expert reasoning and transfer learning are used as three kinds of evidence. According to the similarity results calculated by the three optimization methods, the decision frame of discernment of tumbling chip abrasives is constructed, and a reasonable method is used to determine the basic probability assignment. Secondly, aiming at the problem that the fusion results are contrary to the actual situation when the evidences are highly conflicting in the traditional D-S evidence theory, the method of distributing the basic probability assignment according to the proportion of conflict information is used to improve the synthesize formula. Then, the improved synthesize formula is used to fuse the three kinds of evidence. Finally, the simulation is carried out by using the real data of factory processing in the database. Based on existing case results, a large number of simulation results show that the improved fusion decision model can solve the conflicts between the optimization results of different optimization methods as well as the disadvantages of the original synthesis formula. The results of fusion decision have higher accuracy than those of the other three methods. The accuracy of the fusion decision model reaches 88%, which shows that the proposed decision model can provide decision guidance for intelligent optimization of tumbling chip abrasives. |
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