利用Dhouib-Matrix-4元启发式算法提高计算机数控机床性能

IF 3.4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
S. Dhouib, Danijela Pezer
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引用次数: 4

摘要

计算机数控(CNC)机床代表了当今对产品精度要求很高的生产的一个转折点。数控机床在工作上具有很高的灵活性和节省时间,也减少了产品精度控制所需的时间。此外,数控机床用于几个活动,最常用于车削,钻孔和铣削操作。通常,由于刀具运动的非生产性最小化,任何数控机床的生产率都可以提高。本文给出了一种新的元启发式算法Dhouib-Matrix-4 (DM4)在基于旅行商问题的np困难问题上的应用结果。DM4用于在数控铣床上进行钻孔过程中通过优化刀具路径长度来提高数控机床的性能。通过对所选数控铣床的控制单元,与蚁群优化算法和G代码手工编程的结果进行比较,所提出的算法(DM4)得到了一个接近最优的解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Increasing the Performance of Computer Numerical Control Machine via the Dhouib-Matrix-4 Metaheuristic
The Computer Numerical Control (CNC) machine represents a turning point in today's production which has high requirements for product accuracy. The CNC machine enables a high flexibility in work and time saving and also reduces the time required for product accuracy control. Moreover, the CNC machine are used for several activities, most often for turning, drilling and milling operations. Usually, the productivity of any CNC machine can be increased thanks to the minimization of the non-productive of tool movement. In this paper, the results of a new metaheuristic named Dhouib-Matrix-4 (DM4) with an application on the NP-hard problem based on the Travelling Salesman Problem are presented. DM4 is used for increasing the performance of the CNC Machine by optimizing a tool path length in the drilling process performed on the CNC milling machine. The proposed algorithm (DM4) achieves a solution closed to the optimum, compared with the results obtained with the Ant Colony Optimization algorithm and the results found with the manual programming in G code by using a control unit for the selected CNC milling machine.
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来源期刊
CiteScore
2.00
自引率
0.00%
发文量
15
审稿时长
8 weeks
期刊介绍: Inteligencia Artificial is a quarterly journal promoted and sponsored by the Spanish Association for Artificial Intelligence. The journal publishes high-quality original research papers reporting theoretical or applied advances in all branches of Artificial Intelligence. The journal publishes high-quality original research papers reporting theoretical or applied advances in all branches of Artificial Intelligence. Particularly, the Journal welcomes: New approaches, techniques or methods to solve AI problems, which should include demonstrations of effectiveness oor improvement over existing methods. These demonstrations must be reproducible. Integration of different technologies or approaches to solve wide problems or belonging different areas. AI applications, which should describe in detail the problem or the scenario and the proposed solution, emphasizing its novelty and present a evaluation of the AI techniques that are applied. In addition to rapid publication and dissemination of unsolicited contributions, the journal is also committed to producing monographs, surveys or special issues on topics, methods or techniques of special relevance to the AI community. Inteligencia Artificial welcomes submissions written in English, Spaninsh or Portuguese. But at least, a title, summary and keywords in english should be included in each contribution.
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