【摘要】：制造業(yè)是全球資源的主要消耗者,也是廢棄物的主要排放者,給資源消耗和生態(tài)環(huán)境帶來了巨大的壓力。本文從機械切削加工系統(tǒng)的低碳運行優(yōu)化出發(fā),深入研究了機械切削加工系統(tǒng)的碳排放計算模型、面向高效低碳的切削參數(shù)優(yōu)化和柔性作業(yè)車間調(diào)度問題。該研究對提高機械切削加工系統(tǒng)的能量使用效率,降低碳排放強度,提高運行效率,具有良好的應(yīng)用前景和現(xiàn)實意義。機械切削加工系統(tǒng)的切削過程形式多樣,切削時功率成分復(fù)雜、影響因素多,造成切削過程的碳排放計算困難。在機械切削加工系統(tǒng)碳排放計算模型的研究中,基于碳排放來源多樣和切削過程呈周期性變化等特點,將切削過程分解為多個狀態(tài),基于正交表設(shè)計實驗,并利用材料去除率來計算切削過程能耗,建立了一種基于回歸分析的機械切削加工系統(tǒng)碳排放計算模型。實際應(yīng)用中,切削參數(shù)一般是憑借經(jīng)驗或者參考加工手冊來確定。此外,機械切削加工系統(tǒng)切削過程的碳排放指標(biāo)缺少準(zhǔn)確的數(shù)學(xué)模型。在面向高效低碳的切削參數(shù)優(yōu)化研究中,基于實驗數(shù)據(jù),提出了單道切削過程和多道切削過程的加工時間、碳排放和加工成本等指標(biāo)的數(shù)學(xué)模型,并考慮實際切削過程的約束條件。鑒于優(yōu)化算法存在“沒有免費午餐”的理論,設(shè)計了一種用于多目標(biāo)連續(xù)優(yōu)化問題的基于自適應(yīng)教師因子的多教師教與學(xué)優(yōu)化算法,用于國際經(jīng)典多目標(biāo)優(yōu)化問題,其結(jié)果要優(yōu)于當(dāng)前具有代表性的算法。設(shè)計了一種基于實驗和教與學(xué)優(yōu)化算法的多目標(biāo)切削參數(shù)優(yōu)化框架,應(yīng)用于切削過程優(yōu)化模型,實現(xiàn)了切削參數(shù)高效低碳優(yōu)化。作業(yè)車間調(diào)度問題屬于典型的NP-hard優(yōu)化問題,即使是當(dāng)前最先進的優(yōu)化算法也很難得到其最優(yōu)解,其中柔性作業(yè)車間調(diào)度問題考慮了工序可在不同的機器上加工的情況。低碳制造是在不降低生產(chǎn)效率的前提下降低制造過程的碳排放強度,對高效調(diào)度和低碳調(diào)度都提出了更高的要求。在面向高效低碳的柔性作業(yè)車間問題研究中,建立了面向高效低碳的切削參數(shù)優(yōu)化和柔性作業(yè)車間調(diào)度集成問題的數(shù)學(xué)模型,提出了延遲加工策略、重啟機器策略和兩階段優(yōu)化策略等三種低碳調(diào)度策略,以總完工時間和碳排放為優(yōu)化目標(biāo),應(yīng)用一種基于最大位置值規(guī)則的離散多教師教與學(xué)優(yōu)化算法,實現(xiàn)了作業(yè)車間高效低碳運行。在以上研究工作基礎(chǔ)上,以國內(nèi)某知名汽車制造商的模具加工廠為應(yīng)用對象,設(shè)計了能效管控原型系統(tǒng),從能效優(yōu)化、能效管理的根本需求出發(fā),實時監(jiān)測車間各相關(guān)設(shè)備的電、水、切削液、潤滑油等各類能源的使用情況,以圖表形式展現(xiàn),應(yīng)用現(xiàn)場數(shù)據(jù)對理論研究進行驗證,取得了較好的效果。
[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.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TG50

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