استفاده از الگوریتم ژنتیک برای برنامه ریزی عملیات به کمک کامپیوتر در برنامه ریزی اولیه و دقیق
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|27097||2009||9 صفحه PDF||سفارش دهید||6941 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Engineering Applications of Artificial Intelligence, Volume 22, Issue 8, December 2009, Pages 1179–1187
Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in the computer integrated manufacturing (CIM) environment. A good process plan of a part is built up based on two elements: (1) optimized sequence of the operations of the part; and (2) optimized selection of the machine, cutting tool and tool access direction (TAD) for each operation. On the other hand, two levels of planning in the process planning is suggested: (1) preliminary and (2) secondary and detailed planning. In this paper for the preliminary stage, the feasible sequences of operations are generated based on the analysis of constraints and using a genetic algorithm (GA). Then in the detailed planning stage, using a genetic algorithm again which prunes the initial feasible sequences, the optimized operations sequence and the optimized selection of the machine, cutting tool, and TAD for each operation are obtained. By applying the proposed GA in two levels of planning, the CAPP system can generate optimal or near-optimal process plans based on a selected criterion. A number of case studies are carried out to demonstrate the feasibility and robustness of the proposed algorithm. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature for most problems. The main contribution of this work is to emerge the preliminary and detailed planning, implementation of compulsive and additive constraints, optimization sequence of the operations of the part, and optimization selection of machine, cutting tool and TAD for each operation using the proposed GA, simultaneously.
Computer-aided process planning (CAPP) is considered the key technology for computer-aided design/manufacturing (CAD/CAM) integration. It consists of the determination of processes and parameters required to convert a block into a finished product. The process planning activity includes interpretation of design data, selection and sequencing of operation to manufacture the part, selection of machines and cutting tools, determination of cutting parameters, choice of jigs and fixtures, and calculation of machining times and costs. To clarify the process planning, parts are represented by manufacturing features. Fig. 1 shows a part composed of m features, in which each feature can be manufactured by one or more machining operations (n operations in total for the part). Each operation can be executed by several alternative plans if different machines, cutting tools, or setup plans are chosen for this operation ( Case and Harun Wan, 2000; Maropoulos and Baker, 2000). A process plan for a part consists of all operations needed to process the part and their relevant machines, cutting tools, tool access directions (TADs), and operation sequences. Full-size image (29 K) Fig. 1. Representation of a process plan. Figure options Two major tasks are involved within the process planning, namely, operation selection and operation sequencing. The operation selection is based on the form-feature geometry, its technological requirements and mapping these specifications to the appropriate operation or series of operations (Weill et al., 1982). Operation sequencing is concerned with selection of machining operations in steps that can produce each form feature of the part by satisfying relevant technological constraints specified in part drawing, while minimizing the number of setups, maximizing the machines utilization, minimizing the number of tool changes, etc. In other words, the operation sequencing problem in the process planning is considered to produce a part with the objective of minimizing the sum of machine, setup, and tool change costs. In general, the problem has combinatorial characteristics and complex precedence relations, which makes the problem difficult to solve. A good process plan for a part is built up based on two elements: (1) the optimized sequence of the operations of the part and (2) the optimized selection of machine, cutting tool, and TAD for each operation. Although many CAPP systems have been reported in literature, only few of them have considered the optimization of the sequence of operations, and suggested alternative sequence of operations or process plans. Operation sequencing is a complex task exhibiting the combinatorial nature. As the operations sequencing problem involves various interdependent constraints, it is very difficult to formulate and solve this problem using integer programming and dynamic programming methods alone. Evolutionary algorithms, which mimic living organisms in achieving optimal survival solutions, can often outperform conventional optimization methods. In the past two decades, GA has been widely applied for solving complex manufacturing problems, e.g. job shop scheduling and process planning. In this paper, a genetic algorithm (GA) is chosen for solving this optimization problem. The process planning is divided into preliminary planning and secondary/detailed planning. In the preliminary stage, feasible sequences of operations is carried out considering compulsive constraints of operations using the proposed GA and during the secondary and detailed level of planning, the optimized sequence of the operations of the part, and the optimized selection of the machine, cutting tool, and TAD for each operation is acquired using a genetic algorithm considering additive constraints as well. It means during the secondary of planning, relevant manufacturing information, such as, machine tools, cutting tools, and TADs for the operations of the part is determined. This paper is organized into five sections. Section 2 gives a literature review on the related research work. Section 3 illustrates our approach for determining the optimized operations sequence and determines a machine, cutting tool, and TAD for each operation. System implementation and a case study are presented in Section 4. Finally, conclusions are summarized in Section 5.
نتیجه گیری انگلیسی
In this paper, the process planning was divided into preliminary planning and secondary/detailed planning. The preliminary planning is independent of resources, as it involves abstractions of processes, setups, etc. In this stage after necessary operations for a part based on the form features selected and on the operations and their inter-relationships, the preliminary sequences are determined. During the preliminary planning, an efficient genetic algorithm is proposed to explore the large solution space of valid operation sequences under compulsive constraints. The proposed GA generates the initial feasible sequences based on the order and clustering constraints as compulsive constraints. The results of the preliminary planning are used in the next phase, the secondary/detailed planning, in which machine tools, cutting tools and TADs are considered, and sequences of operations are optimized at the process level. Another proposed GA optimizes the sequences of the operations using a criterion involving the minimum production cost that represents machine changes, setup changes, and tool changes. The process planning is a combinatorial problem with interacting constraints, and the preliminary planning reduces the number of combinations to be examined. The proposed GA handles this combinatorial problem very well, and the reduction in the size of the problem at each stage makes the algorithm a very fast one. The proposed GA can obtain optimal/near-optimal solutions, requiring about 10–40 s, depending on the number of operations and the number of generations. Several case studies were considered and the related results were obtained in less than 40 s. For a typical case of an oil pathway board with 29 operations, the results were obtained in 15 s for a population size of 20 and 100 generations. The case study involving 38 operations took 20 s. Since the computational time taken to generate the optimal/near-optimal sequences is low, the software can be run several times to facilitate the process planner in obtaining alternative sequences.