金属结构件生产过程若干优化问题研究与应用

发布时间：2018-02-04 20:44

本文关键词： 批量优化分组下料加工排程制造执行系统　出处：《华中科技大学》2014年博士论文　论文类型：学位论文

【摘要】：金属结构件被广泛地应用于工程机械、港口机械、船用机械等领域,但由于金属结构件具有产品结构复杂、生产周期长等特点,从而导致其生产过程中存在交货拖期、材料利用率低、在制品数量大、组焊齐套性差及设备负载不平衡等问题。本文围绕着生产批量优化问题、零件下料分组优化问题、加工排程优化问题等金属结构件生产过程中关键优化问题及生产过程管控方法展开深入研究。通过对这些优化问题及管控方法的研究,对缩短金属结构件生产周期,降低生产成本具有重要意义。首先,根据金属结构件制造系统的特点,针对其制造成本和需求不确定性扰动,提出用鲁棒优化方法制订金属结构件生产批量计划。该方法首先在传统生产批量优化模型的基础上构建了一个新的鲁棒模型,然后将该模型转化为易于求解的线性规划,最后通过单纯形法(利用LINGO数学优化软件)对该模型进行求解。通过对金属结构件生产批量的优化,可以减少订单交付拖期情况,降低了产品库存费用及生产成本,并通过实例分析证明了该方法的有效性。其次,针对金属结构件生产过程中大规模集中下料问题,提出了零件下料分组优化方法。该方法首先将待下料零件按材质、板厚及加工工艺相似性进行分组,然后构建一种能够描述零件形状特征的零件特征矩阵,并设计一种人工神经网络算法求解该零件特征矩阵与零件分组之间的映射关系,最后将经第一次分组后的零件根据这种映射关系进一步分组。通过对零件下料分组优化,可以提高零部件的齐套性、降低在制品数量,并且能够有效解决排料效率和材料利用率相互矛盾的问题。再次,针对金属结构件生产过程中多任务混合下料与加工排程优化问题,提出多种排料方案下的带工艺约束多目标加工排程优化模型,并设计一种蚁群一递阶遗传算法来求解该模型。该算法首先利用蚁群算法选择一组优异的排料方案,然后利用递阶遗传算法的选择、交叉、变异等操作求解作业加工顺序及机器选择问题。通过对其加工排程的优化,缩短金属结构件产品生产周期,降低其在制品数量和提高设备利用率,并通过实例分析证明了该方法的有效性。然后,在金属结构件制造执行过程管控方法与应用方面展开研究。针对数据采集、生产进度控制、在制品管理和质量控制等问题,分别提出了相应的过程管控方法。通过这些方法实现了对生产过程实时监控,保障了生产批量计划正确执行。另外,基于上述理论和方法,以某金属结构件产品生产企业为应用对象,设计并开发了金属结构件生产管控系统,通过该系统在相关企业成功应用,验证了本文所研究的生产批量计划优化方法、零件下料分组优化方法、加工程排优化方法及执行过程管控方法的可行性与实用性。最后,对全文所做的主要工作进行了总结,并对后续的研究工作进行了展望。
[Abstract]:Metal structural parts are widely used in engineering machinery , port machinery , marine machinery and so on , but because the metal structural members have the characteristics of complex product structure and long production period , this paper studies the key optimization problems and the control methods of production process in the production process of metal structures such as batch optimization , group optimization of parts , optimization of processing scheduling and so on . Through the research on these optimization problems and control methods , it is of great significance to shorten the production period of metal structural members and reduce the production cost . Firstly , according to the characteristics of the manufacturing system of the metal structure , a new robust optimization method is proposed to produce a batch plan for the production of metal structural parts . The method firstly builds a new robust model on the basis of the traditional production batch optimization model , then solves the model by simplex method ( using LINGO mathematical optimization software ) . Secondly , according to the problem of large - scale centralized blanking in the production process of metal structural parts , the paper puts forward a method for optimizing the part blanking grouping . The method comprises the following steps : firstly , grouping the parts to be discharged according to the similarity of material , plate thickness and processing technology , then constructing a part characteristic matrix capable of describing the shape characteristics of the parts , and designing an artificial neural network algorithm to solve the mapping relationship between the part characteristic matrix and the part grouping . Thirdly , aiming at the problem of multi - task mixing and scheduling optimization in the production process of metal structural members , a multi - objective process scheduling optimization model with process constraints is proposed , and an ant colony algorithm is designed to solve the model . In addition , based on the above theory and method , the production management and control system of the metal structural member is designed and developed based on the theory and the method , the production batch plan optimization method , part blanking grouping optimization method , the optimization method of the part blanking and the control method of the execution process are verified through the system . Finally , the thesis summarizes the main work done in full text , and looks forward to the follow - up research .

【学位授予单位】：华中科技大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TH186

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