机械切削加工系统低碳运行优化关键技术研究及其应用
发布时间：2018-09-08 20:19
【摘要】：制造业是全球资源的主要消耗者,也是废弃物的主要排放者,给资源消耗和生态环境带来了巨大的压力。本文从机械切削加工系统的低碳运行优化出发,深入研究了机械切削加工系统的碳排放计算模型、面向高效低碳的切削参数优化和柔性作业车间调度问题。该研究对提高机械切削加工系统的能量使用效率,降低碳排放强度,提高运行效率,具有良好的应用前景和现实意义。机械切削加工系统的切削过程形式多样,切削时功率成分复杂、影响因素多,造成切削过程的碳排放计算困难。在机械切削加工系统碳排放计算模型的研究中,基于碳排放来源多样和切削过程呈周期性变化等特点,将切削过程分解为多个状态,基于正交表设计实验,并利用材料去除率来计算切削过程能耗,建立了一种基于回归分析的机械切削加工系统碳排放计算模型。实际应用中,切削参数一般是凭借经验或者参考加工手册来确定。此外,机械切削加工系统切削过程的碳排放指标缺少准确的数学模型。在面向高效低碳的切削参数优化研究中,基于实验数据,提出了单道切削过程和多道切削过程的加工时间、碳排放和加工成本等指标的数学模型,并考虑实际切削过程的约束条件。鉴于优化算法存在“没有免费午餐”的理论,设计了一种用于多目标连续优化问题的基于自适应教师因子的多教师教与学优化算法,用于国际经典多目标优化问题,其结果要优于当前具有代表性的算法。设计了一种基于实验和教与学优化算法的多目标切削参数优化框架,应用于切削过程优化模型,实现了切削参数高效低碳优化。作业车间调度问题属于典型的NP-hard优化问题,即使是当前最先进的优化算法也很难得到其最优解,其中柔性作业车间调度问题考虑了工序可在不同的机器上加工的情况。低碳制造是在不降低生产效率的前提下降低制造过程的碳排放强度,对高效调度和低碳调度都提出了更高的要求。在面向高效低碳的柔性作业车间问题研究中,建立了面向高效低碳的切削参数优化和柔性作业车间调度集成问题的数学模型,提出了延迟加工策略、重启机器策略和两阶段优化策略等三种低碳调度策略,以总完工时间和碳排放为优化目标,应用一种基于最大位置值规则的离散多教师教与学优化算法,实现了作业车间高效低碳运行。在以上研究工作基础上,以国内某知名汽车制造商的模具加工厂为应用对象,设计了能效管控原型系统,从能效优化、能效管理的根本需求出发,实时监测车间各相关设备的电、水、切削液、润滑油等各类能源的使用情况,以图表形式展现,应用现场数据对理论研究进行验证,取得了较好的效果。
[Abstract]:Manufacturing industry is the main consumer of global resources and the main emitter of waste, which brings great pressure to resource consumption and ecological environment. Based on the low carbon operation optimization of machining system, the carbon emission calculation model of machining system, the optimization of cutting parameters for high efficiency and low carbon and the problem of flexible job shop scheduling are studied in this paper. This research has good application prospect and practical significance to improve the energy efficiency of machining system, reduce the carbon emission intensity, and improve the operation efficiency. The cutting process of the machining system is diverse, the power component is complex and the factors are many, which makes the calculation of carbon emission difficult. In the research of carbon emission calculation model of machining system, the cutting process is decomposed into several states based on the characteristics of various sources of carbon emissions and periodic variation of cutting process, and the experiment is designed based on orthogonal table. The material removal rate is used to calculate the energy consumption in the cutting process, and a calculating model of carbon emission of machining system based on regression analysis is established. In practical applications, cutting parameters are usually determined by experience or by reference to a machining manual. In addition, the carbon emission index of machining system is short of accurate mathematical model. Based on the experimental data, the mathematical models of machining time, carbon emission and machining cost of single-channel and multi-channel cutting processes are proposed in the research of cutting parameter optimization for high efficiency and low carbon. The constraints of the actual cutting process are also considered. In view of the theory that there is no free lunch in the optimization algorithm, a multi-teacher teaching and learning optimization algorithm based on adaptive teacher factor is designed for multi-objective continuous optimization problems, which is used in international classical multi-objective optimization problems. The result is better than the representative algorithm. A multi-objective cutting parameter optimization framework based on experiment and teaching and learning optimization algorithm is designed, which is applied to the cutting process optimization model, and the cutting parameters are optimized with high efficiency and low carbon. Job shop scheduling problem is a typical NP-hard optimization problem, even the most advanced optimization algorithm is difficult to obtain its optimal solution, in which flexible job shop scheduling problem takes into account the process can be processed on different machines. Low carbon manufacturing is to reduce the carbon emission intensity of manufacturing process without reducing production efficiency, which puts forward higher requirements for efficient scheduling and low carbon scheduling. In the research of flexible job shop with high efficiency and low carbon, the mathematical model of cutting parameter optimization and flexible job shop scheduling integration problem for high efficiency and low carbon is established, and the delayed machining strategy is proposed. Restarting machine strategy and two-stage optimization strategy are three low carbon scheduling strategies. Taking total completion time and carbon emission as the optimization targets, a discrete multi-teacher teaching and learning optimization algorithm based on maximum location rule is applied. The high efficiency and low carbon operation of the job shop is realized. On the basis of the above research work, a prototype system of energy efficiency control is designed based on the mould processing factory of a well-known automobile manufacturer in China. The prototype system is based on the fundamental requirements of energy efficiency optimization and energy efficiency management. The use of electricity, water, cutting fluid, lubricating oil and other related equipments in the workshop is monitored in real time. It is shown in the form of chart, and the theoretical research is verified by the field data, and good results are obtained.
【学位授予单位】：华中科技大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：TG50
本文编号：2231588
[Abstract]:Manufacturing industry is the main consumer of global resources and the main emitter of waste, which brings great pressure to resource consumption and ecological environment. Based on the low carbon operation optimization of machining system, the carbon emission calculation model of machining system, the optimization of cutting parameters for high efficiency and low carbon and the problem of flexible job shop scheduling are studied in this paper. This research has good application prospect and practical significance to improve the energy efficiency of machining system, reduce the carbon emission intensity, and improve the operation efficiency. The cutting process of the machining system is diverse, the power component is complex and the factors are many, which makes the calculation of carbon emission difficult. In the research of carbon emission calculation model of machining system, the cutting process is decomposed into several states based on the characteristics of various sources of carbon emissions and periodic variation of cutting process, and the experiment is designed based on orthogonal table. The material removal rate is used to calculate the energy consumption in the cutting process, and a calculating model of carbon emission of machining system based on regression analysis is established. In practical applications, cutting parameters are usually determined by experience or by reference to a machining manual. In addition, the carbon emission index of machining system is short of accurate mathematical model. Based on the experimental data, the mathematical models of machining time, carbon emission and machining cost of single-channel and multi-channel cutting processes are proposed in the research of cutting parameter optimization for high efficiency and low carbon. The constraints of the actual cutting process are also considered. In view of the theory that there is no free lunch in the optimization algorithm, a multi-teacher teaching and learning optimization algorithm based on adaptive teacher factor is designed for multi-objective continuous optimization problems, which is used in international classical multi-objective optimization problems. The result is better than the representative algorithm. A multi-objective cutting parameter optimization framework based on experiment and teaching and learning optimization algorithm is designed, which is applied to the cutting process optimization model, and the cutting parameters are optimized with high efficiency and low carbon. Job shop scheduling problem is a typical NP-hard optimization problem, even the most advanced optimization algorithm is difficult to obtain its optimal solution, in which flexible job shop scheduling problem takes into account the process can be processed on different machines. Low carbon manufacturing is to reduce the carbon emission intensity of manufacturing process without reducing production efficiency, which puts forward higher requirements for efficient scheduling and low carbon scheduling. In the research of flexible job shop with high efficiency and low carbon, the mathematical model of cutting parameter optimization and flexible job shop scheduling integration problem for high efficiency and low carbon is established, and the delayed machining strategy is proposed. Restarting machine strategy and two-stage optimization strategy are three low carbon scheduling strategies. Taking total completion time and carbon emission as the optimization targets, a discrete multi-teacher teaching and learning optimization algorithm based on maximum location rule is applied. The high efficiency and low carbon operation of the job shop is realized. On the basis of the above research work, a prototype system of energy efficiency control is designed based on the mould processing factory of a well-known automobile manufacturer in China. The prototype system is based on the fundamental requirements of energy efficiency optimization and energy efficiency management. The use of electricity, water, cutting fluid, lubricating oil and other related equipments in the workshop is monitored in real time. It is shown in the form of chart, and the theoretical research is verified by the field data, and good results are obtained.
【学位授予单位】：华中科技大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：TG50
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