مدل سازی از انتخاب فرآیند پارامتر با منطق ریاضی برای برنامه ریزی فرایند

Process planning is the systematic determination of detailed methods by which workpieces or parts can be manufactured economically and competitively from initial stages to finished stages. One of the key problems of computer-aided process planning (CAPP), however, is the complexity of process knowledge representation of process planning and the diversity of manufacturing background. Process knowledge representation and inference mechanism of process parameter selection is one of the most important issues in the research on CAPP. A proper methodology for modeling inference mechanism of process parameter selection, hence, is essential for selection of process parameters in process planning. The paper presents an atomic inference engine model of process parameter selection in process planning using mathematical logic. The methodology of modeling the inference mechanism of process parameter selection is proposed with backward chaining of mathematical logic that is a form of goal-directed reasoning. An illustrative case has been analyzed using the proposed approach to demonstrate its potential application in the real manufacturing environment, by combining with a practical application of a hole-making in a industrially relevant workpiece. The outcomes of this work provide a process reasoning mechanism for process parameter selection in process planning and thus alleviate automated process reasoning problems in process planning.

Process planning is the systematic determination of detailed methods by which workpieces or parts can be manufactured economically and competitively from initial stages to finished stages [1]. Process planning, as defined by Chang et al., is also an act of preparing detailed operation instructions to transform an engineering design to a final part or processing documentation for the manufacture of a piece part or assembly [2] and [3]. Computer-aided process planning (CAPP) is an essential component of a computer integrated manufacturing (CIM) system. The purpose of CAPP is to automate process planning tasks so that the process plans are generated consistently [4]. However, a successful automatic machining process planning system relies greatly on a good process reasoning mechanism which is still the basic crux of process planning. One of key problems of CAPP, furthermore, is the complexity of process knowledge representation of process planning and the diversity of manufacturing background. Although increasing researchers and engineers from both academia and industry have also attempted to develop CAPP systems, the application of most CAPP systems relies on the experienced process planners and thus the totally automatic process planning using computers is still far from being realized. An additional complication is due to the fact that process knowledge representation and inference mechanism in process planning are difficult in a changing manufacturing environment and different industries. Hence, we have presented a systematic methodology to address this problem in the previous work [5], which represents process knowledge by the first- and second-order logic of mathematical logic and maps them onto manufacturing resources. It should be noted, however, that process parameter selection in process planning is yet one of the most important subtasks in the research on automated process planning, and also a bottleneck hampering the automation process of CAPP. Therefore, it is of great importance to properly create a systematic approach of process knowledge representation and inference mechanism of process parameter selection in process planning, both qualitatively and quantitatively. In this paper, an atomic inference engine model of process parameter selection in process planning (ML-PPS) is presented using mathematical logic to address this problem. ML-PPS provides a process reasoning mechanism for process parameter selection in process planning and thus alleviates automated process reasoning problems in process planning. The first section introduces the background of the research field and the significance of modeling inference mechanism of process parameter selection in process planning. The second section briefly reviews the previous work on selection of machine tools and cutting tools and their parameters. The third section presents a systematic methodology of modeling process parameter selection using mathematical logic. The fourth section analyzes an illustrative case using the proposed approach to demonstrate its potential application in the real manufacturing environment, by combining with a practical application of making a hole in a industrially relevant workpiece. The remainder of this paper discusses the gains achieved by the systematic methodology of modeling of processparameter selection with mathematical logic for process planning.

ML-PPS presented in this paper serves process parameter selection in process planning with a process reasoning mechanism and thus alleviates automated process reasoning problems in process planning. ML-PPS, hence, lays foundation for improving feasibility of process plans and saving tremendous amount of process planners’ efforts. Some of the benefits of our approach are listed below. •• The methodology of modeling the inference mechanism of process parameter selection is proposed with backward chaining of mathematical logic that is a form of goal-directed reasoning. By introducing mathematical logic into process parameter selection of process planning, the atomic inference engine model provides effective reasoning mechanisms in process planning with reasonable structure, and facilitates automated process planning. •• In contrast to other existing approaches, ML-PPS is a systematic methodology that can address process parameter selection of process planning from the global point of view. •• Since process reasoning are complicated decision-making problems, ML-PPS can be applied to abstract and simplify realistic problems of process planning, to make problem analysis much simpler and clearer and process parameter selection much more convenient. Consequently, ML-PPS will facilitate automation of process planning by integrating process knowledge with mathematical logic. In this work, we have made an attempt in the direction of building a systematic approach to process reasoning in process planning, but the overall problem is far from solved. For example, in our current work we do not consider process reasoning of sequencing in process planning, which can have significant effects on rapid process preparation. In the future, process reasoning of sequencing in process planning will be embedded into automation of process planning.