| With the development of modern manufacturing technology,the requirements of various manufacturing industries for products are gradually improving.As the main industrial machine,machine tool plays an important role in the manufacturing industry.As the key part of machine tool,the manufacturing accuracy of headstock affects the performance of machine tool directly,and then affects the quality of other processed parts.As a typical box part,the machining characteristics of the headstock are distributed on multiple surfaces of the box.In order to improve the machining accuracy and efficiency,the vertical and horizontal machining center is selected as the machining equipment.Compared with traditional machine tools,the vertical and horizontal machining center is equipped with the structure of double spindle and rotary worktable,as well as the multi axis linkage function,which is able to process multiple surfaces of parts under one clamping,so that the clamping times can be reduced significantly and the processing time can be shorten.The processing accuracy of parts and the processing efficiency are improved,and the management cost and the floor area are reduced,which is in line with the requirements of the manufacturing industry for high precision,high efficiency and low cost requirements.The number of workpiece transposition and tool replacement in the machining process are affected by the work step sequence arrangement of parts,and then the auxiliary machining time is affected,so optimizing the work step sequence is of great significance to improve the machining efficiency.In addition,as the core of manufacturing process,the optimization of cutting parameters is an important way to further improve production efficiency,enhance product quality and reduce production cost.The headstock of machine tool was taken as the research object.Its structural characteristics and processing requirements were analyzed,the processing methods of each feature were determined,and the processing technology was formulated preliminarily.Then,based on this processing technology,the work step sequence of part processing was optimized to reduce the auxiliary processing time;At the same time,the influence of cutting parameters of typical drilling and milling processes on tool wear and surface roughness was analyzed and studied,and the machining quality was further improved through the optimization of cutting parameters.The main research contents are summarized as follows:First,the machining process planning of headstock was carried out by analyzing its process characteristics,and the machining process was formulated in combination with the principle of machining sequence arrangement.The structural characteristics and main processing requirements of the headstock of machine tool were analyzed,and the vertical and horizontal machining center was selected as the processing equipment.The process method of part processing was analyzed,the positioning datum of the part and the processing scheme of each feature were determined,the machining allowance of each process were calculated,and the reasonable cutting parameters were selected by expounding the process specification design method.On this basis,the processing technology of headstock was formulated by combining with the principle of processing sequence arrangement.Secondly,the work step priority constraint matrix and auxiliary machining time model of vertical and horizontal machining center for machining box parts were constructed,and the improved genetic algorithm was used to optimize the work step sequence to reduce the auxiliary machining time.Aiming at the characteristics of high flexibility and multi constraints that the vertical and horizontal machining center could process all the features on the five faces of box parts through transposition and tool change under one clamping,a step priority constraint matrix based on polychromatic set theory was established,and the mathematical model of part auxiliary machining time was constructed.In order to improve the optimization ability of genetic algorithm,crossover and mutation operators were designed to solve the problem of illegal solution in the iterative process.The auxiliary machining time in the machining process of headstock was taken as the optimization goal,and the work step sequence of parts was optimized by using the improved genetic algorithm to minimize the auxiliary machining time on the premise that the priority constraint was met.The auxiliary processing time of the optimized step sequence was reduced by 19%compared with the initial process.Finally,the influence of cutting parameters of typical drilling and milling processes on tool wear and part surface roughness was analyzed.In order to reduce tool wear and improve machining efficiency,NSGA-II algorithm was used to optimize cutting parameters.The full factor experiment and orthogonal experiment of cutting gray cast iron with high-speed steel drill bit and cemented carbide milling cutter were designed respectively.The wear state of the tool was observed by optical microscope and the average wear of the flank was measured.At the same time,the surface roughness of the workpiece was measured by the instrument.Through the experimental data,the variation laws of the average wear of the tool flank and the workpiece surface roughness were analyzed,and the prediction models of them under a certain metal removal volume were fitted.Then,NSGA-Ⅱ algorithm was used to optimize the tool wear and machining efficiency in the drilling process and milling process respectively.Experiments were used to verify the accuracy of the prediction model and the feasibility of the optimization results,and the machining process of the headstock was improved according to the optimization results.The processing technology formulation,work step sequencing optimization and cutting parameter optimization for the headstock of the machine tool provide a reference for the formulation and optimization of the processing technology of other typical box parts in the enterprise,which is of great significance to improve the product quality and production efficiency. |