范晓建,田建艳,杨英波,菅垄,杨胜强.基于改进D-S证据理论的滚抛磨块融合决策模型[J].表面技术,2021,50(4):393-401.
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].Surface Technology,2021,50(4):393-401 |
| 基于改进D-S证据理论的滚抛磨块融合决策模型 |
| Fusion Decision Model of Tumbling Chip Abrasives Based on Improved D-S Evidence Theory |
| 投稿时间：2020-08-10 修订日期：2020-11-24 |
| DOI：10.16490/j.cnki.issn.1001-3660.2021.04.042 |
| 中文关键词: 滚抛磨块 智能优选 融合决策 D-S证据理论 辨识框架 基本概率赋值 |
| 英文关键词:tumbling chip abrasives intelligent optimization fusion decision D-S evidence theory frame of discernment basic probability assignment |
| 基金项目:山西省重点研发计划项目(201903D121057);山西省回国留学人员科研资助项目(2017-032);山西省自然科学基金重点项目(201801D111002) |
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| Author | Institution |
| FAN Xiao-jian | School of Electrical and Power Engineering, Taiyuan 030024, China |
| TIAN Jian-yan | School of Electrical and Power Engineering, Taiyuan 030024, China |
| YANG Ying-bo | School of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China |
| JIAN Long | School of Electrical and Power Engineering, Taiyuan 030024, China |
| YANG Sheng-qiang | School of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China |
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| 中文摘要: |
| 目的 为了能够有效利用滚磨光整加工数据库平台的案例知识和专家经验，提高新零件加工时滚抛磨块优选的准确率，解决不同优选方式优选结果的冲突问题。方法 将案例推理、专家推理、迁移学习3种优选方式的滚抛磨块优选结果作为3种证据，根据3种优选方式计算的相似度结果构建滚抛磨块决策辨识框架，并采用合理的方法确定基本概率赋值。然后依据按冲突信息的比例分配基本概率赋值的方法对证据合成公式进行改进，避免传统的D-S证据理论在证据间高度冲突时出现融合结果有悖于实际情况的问题。接着采用改进的证据合成公式对3种证据进行融合决策。最后利用数据库平台中工厂加工实例数据进行仿真。结果 基于数据库平台中已有的成功案例结果，通过仿真结果可以表明，该改进的融合决策模型可以解决不同优选方式优选结果之间的冲突问题，解决了原始合成公式的弊端问题，且融合决策结果较3种方法单独使用时具有更高的准确率，该融合决策模型的准确率达到88%。结论 基于改进D-S证据理论的滚抛磨块融合决策模型，可以为滚磨光整加工时滚抛磨块的智能优选提供决策指导。 |
| 英文摘要: |
| 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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