طراحی بلوک های تابع برای برنامه ریزی عملیات توزیعی و کنترل تطبیقی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|27264||2009||12 صفحه PDF||سفارش دهید||5445 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Engineering Applications of Artificial Intelligence, Volume 22, Issue 7, October 2009, Pages 1127–1138
The objective of this research is to develop methodologies and a framework for distributed process planning and adaptive control using function blocks. Facilitated by a real-time monitoring system, the proposed methodologies can be applied to integrate with functions of dynamic scheduling in a distributed environment. A function block-enabled process planning approach is proposed to handle dynamic changes during process plan generation and execution. This paper focuses mainly on distributed process planning, particularly on the development of a function block designer that can encapsulate generic process plans into function blocks for runtime execution. As function blocks can sense environmental changes on a shop floor, it is expected that a so-generated process plan can adapt itself to the shop floor environment with dynamically optimized solutions for plan execution and process monitoring.
Recently, reconfigurable manufacturing system (RMS) has emerged as a promising manufacturing paradigm that allows flexibility not only in producing a variety of parts, but also in reconfiguring the system itself. The manufacturing processes involved in an RMS are complicated, especially at machining shop floors where a large variety of products are handled dynamically in small batch sizes. The dynamic RMS environment usually has geographically distributed shop floor equipment. It requires a decentralized system architecture that enables distributed shop floor planning, dynamic resource scheduling, real-time monitoring, and remote control. It should be responsive to unpredictable changes of distributed production capacity and functionality. An ideal shop floor should be the one that uses real-time manufacturing data and intelligence to achieve the best overall performance, with the least unscheduled machine downtime. However, traditional methods are inflexible, time-consuming, and error-prone, if applied directly to this dynamic environment. In response to the above needs and to coordinate the RMS activities, a new distributed process planning (DPP) approach supported by real-time manufacturing intelligence is proposed in this research to achieve the adaptability during process planning and its execution control. Aiming at the emerging RMS paradigm, our research objective is to develop methodologies and a framework for distributed process planning and adaptive control, capable of linking to dynamic scheduling functions. This framework is supported by a real-time monitoring system for adaptive decision-making. Within the context, the monitoring system is used to provide runtime information of shop floor devices from bottom up for effective decision-making at different levels. Compared with the best estimation of an engineer, this approach assures that the correct and accurate decisions are made in a timely manner. The ultimate goal of the research is to realize both the flexibility and dynamism of shop floor operations that meet the RMS requirements. In this paper, the focus is on a function block designer for process plan encapsulation with adaptive decision-making algorithms. Following a brief literature review and description of the entire research, this paper presents principles of function blocks, internal structure, and execution control chart (ECC) of the function blocks, as well as details of architecture design and implementation. Finally, it is validated through a case study on how a generic process plan can be generated and encapsulated in the function blocks.
نتیجه گیری انگلیسی
This paper briefly introduces the concept of a distributed process planning system called DPP, supported by function blocks and a real-time monitoring system. Details are given to the design and implementation of function blocks and a function block designer that is crucial to adaptive process plan generation. A prototype of the function block designer is implemented in Java and tested through a case study. During the design process, processing algorithms for decision-making and optimization are defined and prepared for function block embedding. Machining sequence determined by a separate module can be mapped to a function block chain automatically. It is shown through a test part that an adaptive process plan of the part can be generated by converting its machining features to appropriate function blocks using the function block designer. Based on the event-driven model of a function block, a so-generated process plan can adjust its machining parameters to best fit a selected machine during the plan execution. Since the function blocks embed algorithms rather than fixed data such as G-code, they are generic and portable to alternative machines, for adaptive CNC control. It is the embedded algorithms that generate runtime solutions at the request of an event, reflecting a dynamic change during job shop machining operations. It is also expected that the function blocks can be utilized, for process monitoring and integration with dynamic scheduling. The DPP research aims at uncertainty problems of job shop operation with more dynamism than mass productions, including availability of machines and tools, urgent customer orders, product design change, reconfiguration of shop floors, and variation of production parameters. Compared with conventional centralized process planning systems, our DPP approach can distribute decision-making in two steps and at two levels. The high-level process plans are generic and portable to alternative machines, and only need to be generated once. The low-level operation plans are adaptive and optimal to the chosen available machine, and are generated at runtime to absorb the last-minute change on a shop floor. Future research of distributed process planning will focus on real-time machining intelligence sharing, and its seamless integration with machining process monitoring and dynamic scheduling. Mechanisms for linking DPP to other higher level reactive tools such as MRP and ERP will also be under investigations.