Research And Application On The Key Technologies Of Low Carbon Operational Optimization In A Mechanical Machining System

Manufacturing industry is the main resource consumer and waster emitter in the world. It has brought tremendous pressure to the resource supply and ecological environment. To achive the objective of operational optimization in machining systems for low carbon manufacturing, the author of this dissertation investigated the quantitative method of carbon emission in machining systems, machining optimization problem and flexible jobshop scheduling problem for high efficiency and low carbon manufacturing. This research has practical significance in improving energy efficiency and production efficiency and reducing carbon emissions of machining systems.Because of the complex constitution of power consumption in machining and various indepent factors, it is difficult to calculate carbon emission in a machininig process. In the study of a carbon emission quantitative metod of machining systems, a simple and maneuverable quantitative method of carbon emission in machining systems is proposed. A machining process is divided into several states according to the multi-source and periodic characterstics of carbon emission in maching systems. Using orthogonal array method, experiments are designed to obtain real data in machining. Then, MATLAB Curve Fitting Toolbox is employed to analyze the data and find relationships between energy consumption and machining paramters.Machining paremeters are determined through experience and test cutting in the survyed workshop. Thus, it is impossible to obtain the optimal parameters for any machining process. In the study of machining parameter optimization, two mathematical models are presented; they are single-pass and multi-pass machining parameter optimization models. Three objectives (i.e. operation time, carbon emission and machining cost) and practical constraints are considered in the proposed models. Then, a multi-objective optimization framework for machining parameters is proposed and used to deal with the models.Flexible jobshop scheduling problem is a classical NP-hard problem, which is difficult to get the optimal solution of it even using state-of-art algirthms. Besides, sustainable manufacturing requires scheduling to be more production efficiency and environment-friendly. In the study of flexible jobshop scheduling problem, a mathematical model of an integrated flexible jobshop scheduling and machining parameter optimization and three carbon-footprint-reduction strategies are proposed. Makespan and carbon emission are considered as its objectives. Then, a discreted teaching-learning-based optimization algorithm is developed to solve the integrated model.Based on the aforementioned research, an energy management and control prototype system was developed for the mould parts workshop. It monitors real-time resource usage, including electricy, water, cutting fluids, lubricant etc. Real data in the workshop was used to test the aformentiaonal researches.The results showed that the system satisfies the workshop's demands and gets a good application effect.