تعیین توالی تعامل ویژگی های ماشینکاری منشوری برای برنامه ریزی عملیات
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|27098||2007||9 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Computers in Industry, Volume 58, Issue 4, May 2007, Pages 295–303
Today, feature-based process planning has been popular in academia and industry with its ability to rigorously integrate design and manufacturing. To date, research on feature sequencing is mainly focused on using expert systems or knowledge-based systems, geometric based approaches, unsupervised-learning or artificial neural network, and genetic algorithms. The approach presented in this paper, however, is a hybrid one using both knowledge-based rules and geometric reasoning rules. In addition to feature sequencing rules formulation, our research contributions consist of: (1) determining machining precedence constraints by a set of defined knowledge-based rules, (2) grouping machining features into setups based on tool approaching directions, and (3) sequencing features within each setup through geometric reasoning. The sequence of materials (features) to be removed depends on two types of interactions: adjacent interaction and volumetric interaction. A set of rules for geometric reasoning is therefore developed to generate feature sequence. The developed approach has been implemented as the Sequence Generator module in a Distributed Process Planning system and is validated through a case study.
Modern manufacturing today is constantly challenged by stiff global competition, low-volume large-variety production, requirements for high productivity and product quality, as well as short lead-time from design to manufacturing. During the last two decades, CAD/CAM technologies have been extensively developed to automate and integrate various activities in the design and manufacturing cycle. Despite these efforts, difficulties remain in the integration of CAD and CAM domains, mainly due to their diverse informational needs. CAD focuses on part specific geometry and technology while CAM concerns more on process-specific features and their accuracies. Integration efforts thus attempt to augment or translate information across the two domains. Feature-based process planning plays a crucial role in such an integration effort. In feature-based process planning, machining features are recognized from the part CAD model, and machining processes and their sequences are determined based on the features and other machining-relevant technological information. Features are considered as a main factor in the CAD and CAM integration because various design, engineering and manufacturing data can be associated with a feature. As a part may contain many features, proper sequencing of machining these features is crucial in achieving efficient and high-quality manufacture of the part . All corresponding actions (tool selection, setup planning, etc.) in process planning can be chained with features during feature sequencing. In order to machine a single part with several machining features, a number of different setups may be required. Machining features within a setup may or may not be intersecting, which further complicates the sequencing of features . Within a setup, one feature may require several tools to make. The sequencing of features within one setup that requires only a minimum number of tool changes is important in reducing part machining time. To address the above problems, we developed an approach for feature sequencing in process planning. In our approach, machining features are analyzed with a set of knowledge-based (KB) rules to determine the machining precedence constraints. These machining features are grouped together based on defined tool approaching directions. Feature sequencing in each group is partially dependent on the geometric interactions between features. A set of rules for geometric reasoning (GR) is also developed to generate feature sequence. This paper is organized into five sections. Section 2 gives a literature review on related research work. Section 3 depicts our approach to determining feature sequence for prismatic parts with interacting features. System implementation and a case study are presented in Section 4. Finally, conclusions and future work are summarized in Section 5.
نتیجه گیری انگلیسی
The sequence of machining processes is crucial in process planning, where feature sequencing and operation sequencing are the two issues of process sequencing but at differently levels. In the past, operation sequencing received more attentions and has been studied deeply in various aspects , , , ,  and . This paper presents a different approach as part of our DPP research. As a two-layer hierarchy is considered to separate the generic data from those machine-specific ones in DPP, machining process sequencing is treated as machining feature sequencing within the context. According to our survey , , , , , ,  and , the research on feature sequencing is mainly focused on using expert systems or knowledge-based rules, geometric based approaches, unsupervised-learning or artificial neural network, and genetic algorithms. The approach presented in this paper, however, is a hybrid one using knowledge-based rules and geometric reasoning rules. In addition to feature sequencing rules formulation, our research contribution can be considered as developing a systematic approach for machining feature sequencing, consisting of: (1) determining machining precedence constraints by a set of defined knowledge-based rules, (2) grouping machining features into setups based on tool approaching directions, and (3) sequencing features within each setup through geometric reasoning. The advantage of our approach is that both manufacturing interactions and geometric interactions are handled during feature sequencing. This approach has been implemented as the Sequence Generator module of a Distributed Process Planning system and is validated through a case study. A so-generated sequence plan is generic or machine-neutral. It is created based on datum references of machining features and manufacturing constraints, but not tied to a specific machine. Such a partially sequenced process plan can be used by different machines where the non-critical parallel sequences are finally determined. The approach presented here is mainly for process planning of prismatic parts. Our future work will focus on: (1) integration of operation extraction, tool selection and global optimization to obtain optimal process plans and (2) feature sequencing of non-prismatic parts under complex machining environments.