【摘要】：隨著中國制造2025計劃的提出,制造業(yè)迎來了發(fā)展的新方向——向智能制造方向發(fā)展,切削參數(shù)的智能優(yōu)化選擇是智能制造技術的重要環(huán)節(jié)。合理的切削參數(shù)有助于提高工件的加工效率、改善工件的加工質(zhì)量以及延長刀具的使用壽命等。目前,切削參數(shù)的選擇主要依靠工藝人員的實踐經(jīng)驗或是通過查詢切削手冊來獲得,對人的依賴性較大,切削參數(shù)參數(shù)的選擇較為保守,將制約制造資源發(fā)揮其最大的效益。針對以上問題,結合目前人工智能技術已有的研究成果,對切削參數(shù)智能選擇過程中切削參數(shù)知識的表示、推理和優(yōu)化選擇等問題進行了研究與探索,建立電梯零部件切削參數(shù)知識庫。本文主要展現(xiàn)以下幾個方面的研究內(nèi)容:(1)對電梯零部件制造過程特點進行分析,并進一步闡述實現(xiàn)電梯零部件制造工藝參數(shù)智能選擇意義,在研究切削參數(shù)優(yōu)化及知識庫的國內(nèi)外研究現(xiàn)狀基礎上,發(fā)現(xiàn)電梯零部件制造過程中在切削參數(shù)優(yōu)選方面存在的問題,總結該方向研究的發(fā)展趨勢,整理出本課題要研究的重點內(nèi)容和章節(jié)安排。(2)闡述電梯零部件離散制造加工過程切削過程原理,結合某電梯零部件企業(yè)實際情況,對所研究電梯零部件進行分類并結合具體零件分析電梯零部件加工工藝。研究優(yōu)化理論在機械制造中的應用,為進一步建立切削參數(shù)優(yōu)化模型和進行切削參數(shù)的智能選擇的研究奠定理論基礎。(3)研究電梯零部件實際加工過程中的相關因素,融入綠色低碳制造的理念,以加工時間和CO2排放量為優(yōu)化目標,建立連續(xù)工步條件下的切削參數(shù)優(yōu)化模型。研究多目標的優(yōu)化方法,提出基于Pareto的改進人工魚群算法,并對算法性能進行測試,將改進算法應用于實際切削加工案例中,為知識庫構建提供案例知識。(4)在對電梯零部件加工過程切削參數(shù)優(yōu)化選擇理論分析的基礎上,結合某電梯零部件制造企業(yè)實際情況,確定電梯零部件切削參數(shù)知識庫的整體結構。綜合運用各種技術和方法建立符合本課題研究的電梯零部件切削參數(shù)知識表示方法,并確定基于CBR和RBR的切削參數(shù)智能選擇方法。(5)基于以上研究理論,利用PowerBuilder作為客戶端開發(fā)工具,以SQL Server2012作為數(shù)據(jù)庫管理工具,結合Matlab開發(fā)語言對系統(tǒng)進行了設計與實現(xiàn),并以此為基礎設計一套能初步夠適應企業(yè)實際需求的切削參數(shù)智能選擇原型系統(tǒng),初步實現(xiàn)電梯零部件切削參數(shù)的智能優(yōu)選。
[Abstract]:With the development of the 2025 Plan of Manufacturing in China, the manufacturing industry is facing a new direction of development, that is, intelligent manufacturing. The intelligent optimization of cutting parameters is an important part of intelligent manufacturing technology. Reasonable cutting parameters can improve the machining efficiency of the workpiece, improve the machining quality of the workpiece and prolong the service life of the tool. At present, the choice of cutting parameters mainly depends on the practical experience of the technicians or through the inquiry of cutting manual, which is more dependent on people, and the choice of cutting parameters is more conservative, which will restrict the manufacturing resources to give full play to its maximum benefit. In view of the above problems, combined with the existing research results of artificial intelligence technology, the knowledge representation, reasoning and optimization selection of cutting parameters in the process of intelligent selection of cutting parameters are studied and explored. The knowledge base of cutting parameters of elevator parts is established. The main research contents of this paper are as follows: (1) the characteristics of elevator parts manufacturing process are analyzed, and the significance of realizing intelligent selection of elevator parts manufacturing process parameters is further expounded. On the basis of the research status of cutting parameter optimization and knowledge base at home and abroad, the problems of cutting parameter optimization in elevator parts manufacturing are found, and the development trend of this research direction is summarized. The main content and chapter arrangement of this subject are sorted out. (2) the cutting process principle of discrete manufacturing process of elevator parts is expounded, and the actual situation of an elevator parts enterprise is combined. The elevator parts are classified and the machining process of elevator parts is analyzed. The application of optimization theory in mechanical manufacturing is studied, which lays a theoretical foundation for further research on optimization model of cutting parameters and intelligent selection of cutting parameters. (3) the relevant factors in the actual machining process of elevator parts are studied. Based on the idea of green low carbon manufacturing and taking processing time and CO2 emission as the optimization target, the cutting parameter optimization model under continuous working step is established. The multi-objective optimization method is studied and an improved artificial fish swarm algorithm based on Pareto is proposed. The performance of the algorithm is tested, and the improved algorithm is applied to the actual cutting case. It provides case knowledge for the construction of knowledge base. (4) based on the theoretical analysis of cutting parameters optimization of elevator parts machining process, combined with the actual situation of an elevator parts manufacturing enterprise, The whole structure of the knowledge base of cutting parameters of elevator parts is determined. The knowledge representation method of cutting parameters of elevator parts is established by synthetically using all kinds of techniques and methods, and the intelligent selection method of cutting parameters based on CBR and RBR is determined. (5) based on the above research theory, The system is designed and implemented by using PowerBuilder as client development tool, SQL Server2012 as database management tool and Matlab development language. On this basis, a prototype system of intelligent selection of cutting parameters is designed, which can meet the actual needs of enterprises, and the intelligent selection of cutting parameters of elevator parts is preliminarily realized.
【學位授予單位】：江南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TG501

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