Study On The Technology Of Manufacturability Evaluation And Manufacturing Resources Optimization For Automobile Gears

Virtual manufacturing (VM) is the mapping of the actual manufacturing process in computer, and the virtual manufacturing system can realize the manufacturing process on a computer on the premise of not consuming the actual material and energy. Manufacturability evaluation is to design the structure of product under the constraints of manufacturing resources, which can consider the factors concerned with product manufacturing in design stage. Manufacturability evaluation is an important component of VM. As the increasing demands of users for product, the design and structure of product become more and more complicate. Thus, the manufacturing of a part becomes more and more difficulty, and the lead time of product will increase, which needs to improve manufacturing technology rapidly. By manufacturability evaluation of product effectively in design stage, the problems in design and manufacture of products can be found as soon as possible, and the problems can be fed back to designer to improve them. In this way, the manufacturing cost and the development period is reduced and the quality of product is improved.The automobile industry is an important part of the national economy. In an increasingly competitive environment of the current international automotive industry, for the economic development of china, it is important to enhance the level of automotive industry. Gear is the important transmission part in vehicle, and the manufacturing level of gear has an important influence on the performances of automobile. As a result, the study on the technology of manufacturability evaluation and manufacturing resources optimization for automobile gears is sufficiently significant to improve the automobile industry of china. Building a perfect manufacturability evaluation system is the key to realize the manufacturing evaluation. After analyzing the research status and development trend of manufacturability evaluation at home and abroad, this paper proposed the method of manufacturability evaluation based on features for automobile gears in VM system. The feature modeling and optimization technology based on intelligent algorithm are involved in the system of evaluation. Firstly, the models of parts and manufacturing resources are built based on features; then, the manufacturability of parts are evaluated based on features to find out whether the parts can be machined; lastly, the parts are assessed synthetically, namely, the optimization of manufacturing resources are implemented, which is to find the optimum manufacturing environment and find the optimum machining scheme according to different demands. The study on manufacturability evaluation has important significance to shorten the development period and reduce manufacturing cost in theory and practical.The information models of manufacturing features and manufacturing resources are the preconditions for manufacturability evaluation, therefore, this paper proposed the method of feature-based modeling for parts and manufacturing resources. After synthetically analyzing the demands of information of manufacturability evaluation, using the object-oriented technology, the models of parts and manufacturing resources are built. The manufacturing features of parts include geometry feature, structure feature and accuracy feature, which has important influence on the selection of manufacturing resources and process planning. According to the differences of automobile gears in design and manufacturing, the gears are classified into cylindrical gears and bevel gears to carry on the feature modeling separately, and the models can provide the information support for the manufacturability evaluation. In order to simplify the modeling work of the large manufacturing resources, the manufacturing resources are divided into the basic information and the machining capability information of resources to carry on the feature-based modeling. At the same time, the association relation between resources (machining equipment, cutting tool, fixture, measuring tool and so on) and features is built. By modeling the parts and manufacturing resources, the efficiency of manufacturability evaluation is increased. To evaluate the manufacturability of part, the method of three-level manufacturability evaluation is proposed. Manufacturability evaluation needs to solve two problems, one is whether the part can be manufactured, and the other is how to process the part. The first level of evaluation is the structural processing evaluation, and the purpose is to find whether the design of part has processing problem based on the rules which are summarized from the experiences in design and manufacturing, and the information is sent back to designer. The second level of evaluation is the machinability evaluation of features, and the purpose is to assess whether the design requirements of features can meet the constraints of manufacturing resources which include machining equipments, cutting tools, fixtures, measuring tools, etc, and all the equipments which can be used to machine the part are record. The first and second level manufacturability evaluation and design of part are carried out simultaneously. In this way, the problems in design can be found and solved as early as possible, and repetitive redesigns can be avoided.The third level manufacturability evaluation is comprehensive evaluation, namely, the optimization of manufacturing resources, which mainly solves the problem how to manufacture, and the aim is to find the optimum manufacturing environment and the optimum machining scheme. The comprehensive evaluation is divided into two parts: the selection of the distributed manufacturing environments and machining scheme optimization. In this paper, the genetic algorithm (GA) is used in the selection of the distributed manufacturing environments for the first time. After the processing requirements of the part or product are given, maybe there are many factories can manufacture the part or product. Since each plant has different manufacturing resources and processing capacity, so each plant's machining scheme is also different. Therefore, selecting the optimum manufacturing environment according to the selected criterions is very important. In this paper, the minimum production time and the minimum production cost are adopted as the optimization objectives. Because of the diversity of evaluation factors, when the minimum production time is the optimization objective, the evaluation indices include machine change time index, tool or grinding wheel change time index, set-up change time index and machining time index; when the minimum production cost is the optimization objective, the evaluation indices include machine change cost index, tool or grinding wheel change cost index, set-up change cost index and machining cost index. The proposed method can select the optimum manufacturing environment effectively and rapidly.The other part of comprehensive evaluation is the machining scheme optimization. In this paper, the improved particle swarm optimization (PSO) algorithm and the analytic hierarchy process (AHP) are used in the selection of machining scheme for the first time. As manufacturing technology continues to progress and develop, one part maybe has a lot of machining schemes. In conventional machining process, the selection of machining scheme is always based on the single factor (the lowest cost), and the effects of different factors are not considered fully. Because the diversity of the evaluation factors and the different demands of decision-maker, the machining scheme selection is a typical multi-objective decision-making problem and plays an important role in the manufacturability evaluation. In order to reflect the effects of different factors objectively, the evaluation factors are summarized under four main categories: production cost, production time, machining quality and production profit. The AHP is introduced to assignment the weights to different factors. The multi-objective optimization problem is to combine the individual objective functions into a single composite function and the improved PSO is used to optimize the machining scheme according to different demands of decision-maker. The proposed method can select the optimum machining scheme simply, effectively and objectively.