多品种混合涂装计划排程优化与能耗在线监测系统研究
发布时间：2019-05-19 16:32
【摘要】：市场竞争的持续加剧与环境污染的日趋恶化,对生产过程的能效水平提出了更高的要求。尤其是多品种小批量的个性化生产模式,进一步提高了能耗控制问题的复杂性。汽车零部件的涂装生产过程会伴随着大量的能量消耗,它已经成为制造业节能降耗的重要研究方向。因此,本文以汽车涂装为例,研究大规模定制生产模式下的涂装计划优化排程方法,设计开发了多品种混合涂装计划排程优化与能耗在线监测系统,并通过在线监测获得工艺参数与能耗数据,为提高汽车涂装生产质量与能效水平提供了有力保障。本文分析了涂装生产过程中产生无效能耗的原因,通过优化生产订单的排产序列来降低涂装过程产生的无效能耗。首先以混合品种产品集的无效能耗最低、涂装生产时间最短与涂装成本最小为目标,建立了涂装线多目标优化模型。然后基于多目标非支配快速排序遗传算法与改进粒子群算法对多目标优化模型分别进行了优化求解。在传统遗传算法的基础上,通过快速简易的前向比较操作对染色体种群进行非支配前沿等级的划分,克服传统排序方式分层速度过慢的缺点;然后采用小生境技术中的拥挤距离对同一非支配层的染色体进行排序,保持种群多样性;最后将求得的Pareto解集通过层次分析法选出最优排产序列。同时对比两种算法的求解结果与求解效率,最终将改进的遗传算法应用于多品种混合涂装计划排程优化与能耗在线监测系统中的排程优化模块。利用C#开发语言、MATLAB与SQL Server数据库,开发实现了多品种混合涂装计划排程优化与能耗在线监测系统,主要功能包括系统管理、计划排程、报表查看、能耗统计、质量分析、实时监控六大功能模块。系统管理实现了基础数据的录入以及人员/用户/角色权限的定义与设置;计划排程是整个系统的核心,依据给定的订单内容,通过多目标非支配排序遗传算法,实现了涂装生产的排产优化;报表查看实现了对涂装生产线各个工序工艺参数的存储、查询与分析;能耗统计实现了对涂装车间日生产/各工件生产的能耗统计;质量分析对各工件漆膜厚度与漆膜光泽度数据进行了平均值与方差的计算与分析;实时监控实现了对整条涂装生产线的动态实时监测。通过在山东某汽车涂装生产车间的实际应用,验证了该系统的可行性与有效性。
[Abstract]:The continuous intensification of market competition and the deterioration of environmental pollution put forward higher requirements for the energy efficiency level of the production process. Especially, the individualized production mode of multi-variety and small batch further improves the complexity of energy consumption control problem. The painting process of automobile parts will be accompanied by a lot of energy consumption, which has become an important research direction of energy saving and consumption reduction in manufacturing industry. Therefore, taking automobile painting as an example, this paper studies the optimization scheduling method of painting planning under mass customization production mode, and designs and develops an on-line monitoring system for multi-variety mixed coating planning scheduling and energy consumption. The process parameters and energy consumption data are obtained by on-line monitoring, which provides a powerful guarantee for improving the quality and energy efficiency of automobile coating production. In this paper, the causes of invalid energy consumption in coating production process are analyzed, and the invalid energy consumption in painting process is reduced by optimizing the production sequence of production orders. Firstly, aiming at the lowest invalid energy consumption, the shortest coating production time and the minimum coating cost, the multi-objective optimization model of coating line is established. Then the multi-objective optimization model is optimized based on the multi-objective non-dominated fast sorting genetic algorithm and the improved particle swarm optimization algorithm. On the basis of traditional genetic algorithm, the non-dominant frontier grade of chromosome population is divided by fast and simple forward comparison operation, and the disadvantage of slow stratification speed of traditional sorting method is overcome. Then the crowded distance in niche technique is used to sort the chromosomes of the same non-dominant layer to maintain the diversity of the population. Finally, the obtained Pareto solution set is selected by analytic hierarchy process (AHP) to select the optimal scheduling sequence. At the same time, the solution results and efficiency of the two algorithms are compared, and finally, the improved genetic algorithm is applied to the scheduling optimization module of multi-variety mixed coating planning and energy consumption online monitoring system. Using C # development language, MATLAB and SQL Server database, a multi-variety mixed coating planning scheduling optimization and energy consumption online monitoring system is developed and realized. The main functions include system management, planning scheduling, report viewing, energy consumption statistics, quality analysis. Real-time monitoring of six functional modules. The system management realizes the input of basic data and the definition and setting of personnel / user / role permissions. Planning and scheduling is the core of the whole system. According to the given order content, the multi-objective non-dominant sorting genetic algorithm is used to optimize the production scheduling of painting production. The report review realizes the storage, query and analysis of the process parameters of each process of the painting production line, and the energy consumption statistics realizes the energy consumption statistics of the daily production of the painting workshop / the production of each workpiece. The average value and variance of the film thickness and gloss data of each workpiece are calculated and analyzed by quality analysis, and the dynamic real-time monitoring of the whole painting production line is realized by real-time monitoring. The feasibility and effectiveness of the system are verified by the practical application in an automobile painting workshop in Shandong Province.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U466;TP274
本文编号：2480873
[Abstract]:The continuous intensification of market competition and the deterioration of environmental pollution put forward higher requirements for the energy efficiency level of the production process. Especially, the individualized production mode of multi-variety and small batch further improves the complexity of energy consumption control problem. The painting process of automobile parts will be accompanied by a lot of energy consumption, which has become an important research direction of energy saving and consumption reduction in manufacturing industry. Therefore, taking automobile painting as an example, this paper studies the optimization scheduling method of painting planning under mass customization production mode, and designs and develops an on-line monitoring system for multi-variety mixed coating planning scheduling and energy consumption. The process parameters and energy consumption data are obtained by on-line monitoring, which provides a powerful guarantee for improving the quality and energy efficiency of automobile coating production. In this paper, the causes of invalid energy consumption in coating production process are analyzed, and the invalid energy consumption in painting process is reduced by optimizing the production sequence of production orders. Firstly, aiming at the lowest invalid energy consumption, the shortest coating production time and the minimum coating cost, the multi-objective optimization model of coating line is established. Then the multi-objective optimization model is optimized based on the multi-objective non-dominated fast sorting genetic algorithm and the improved particle swarm optimization algorithm. On the basis of traditional genetic algorithm, the non-dominant frontier grade of chromosome population is divided by fast and simple forward comparison operation, and the disadvantage of slow stratification speed of traditional sorting method is overcome. Then the crowded distance in niche technique is used to sort the chromosomes of the same non-dominant layer to maintain the diversity of the population. Finally, the obtained Pareto solution set is selected by analytic hierarchy process (AHP) to select the optimal scheduling sequence. At the same time, the solution results and efficiency of the two algorithms are compared, and finally, the improved genetic algorithm is applied to the scheduling optimization module of multi-variety mixed coating planning and energy consumption online monitoring system. Using C # development language, MATLAB and SQL Server database, a multi-variety mixed coating planning scheduling optimization and energy consumption online monitoring system is developed and realized. The main functions include system management, planning scheduling, report viewing, energy consumption statistics, quality analysis. Real-time monitoring of six functional modules. The system management realizes the input of basic data and the definition and setting of personnel / user / role permissions. Planning and scheduling is the core of the whole system. According to the given order content, the multi-objective non-dominant sorting genetic algorithm is used to optimize the production scheduling of painting production. The report review realizes the storage, query and analysis of the process parameters of each process of the painting production line, and the energy consumption statistics realizes the energy consumption statistics of the daily production of the painting workshop / the production of each workpiece. The average value and variance of the film thickness and gloss data of each workpiece are calculated and analyzed by quality analysis, and the dynamic real-time monitoring of the whole painting production line is realized by real-time monitoring. The feasibility and effectiveness of the system are verified by the practical application in an automobile painting workshop in Shandong Province.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U466;TP274
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