دانلود مقاله ISI انگلیسی شماره 27200
عنوان فارسی مقاله

دستگاه مانیتورینگ دسترس بودن و برنامه ریزی عملیات ماشینکاری نسبت به تولید ابری

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
27200 2013 11 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Machine availability monitoring and machining process planning towards Cloud manufacturing
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : CIRP Journal of Manufacturing Science and Technology, Volume 6, Issue 4, 2013, Pages 263–273

کلمات کلیدی
نظارت - برنامه ریزی عملیات - ماشین کار فروشگاه - عدم قطعیت - تولید ابری -
پیش نمایش مقاله
پیش نمایش مقاله  دستگاه مانیتورینگ دسترس بودن و برنامه ریزی عملیات ماشینکاری نسبت به تولید ابری

چکیده انگلیسی

Cloud manufacturing as a trend of future manufacturing would provide cost-effective, flexible and scalable solutions to companies by sharing manufacturing resources as services with lower support and maintenance costs. Targeting the Cloud manufacturing, the objective of this research is to develop an Internet- and Web-based service-oriented system for machine availability monitoring and process planning. Particularly, this paper proposes a tiered system architecture and introduces IEC 61499 function blocks for prototype implementation. By connecting to a Wise-ShopFloor framework, it enables real-time machine availability and execution status monitoring during metal-cutting operations, both locally or remotely. The closed-loop information flow makes process planning and monitoring feasible services for the Cloud manufacturing.

مقدمه انگلیسی

Today, the global market is characterised by turbulent demands for highly customised products. Customers are increasingly demanding for higher quality products at low cost with quick delivery, and for shorter times between successive product generations. Cooperation among different companies becomes product-specific, customer-centric and dynamic. Manufacturing jobs are diversified and urgent. Moreover, outsourcing, joint ventures, and cross-border collaborations have led to a shop-floor environment geographically distributed across corporate and national boundaries. Moreover, the uncertainties of today's machining operations make this distributed environment further complicated. Companies and decision systems must be more flexible and adaptive to unplanned deviations on turbulent shop floors where metal-cutting processes should be adjusted dynamically to the changes. It is evident that factories of the future must contain smart decision modules that can fine-tune runtime operations adaptively to achieve specified production objectives. However, today's manufacturing systems still exhibit various limitations, especially in flexibility and adaptability. On the other hand, modern manufacturing industries have shown clear trends in recent years – away from long standing and well-established products and relevant production that have been stable over many years, away from comprehensive trusts that may cover all the processes of production, and also away from the single economic consideration of production; instead, companies increasingly focus on their core manufacturing competencies, develop and produce adaptive and customised products, enter more often into alliances for manufacturing and resource optimisation, and integrate environmental and social responsibilities into their operations. This trend will lead to an Internet- and Web-based service-oriented Cloud manufacturing in the future to overcome today's limitations in rigid system structure, standalone software usage, centralised resource utilisation, unidirectional information flow and off-line decision making. As one of the core components of a manufacturing system, computer-aided process planning (CAPP) is desired to be responsive and adaptive to the changes in production capacity and functionality. Unfortunately, conventional CAPP systems are neither flexible nor adaptive, if applied directly to dynamic operations. Quite often, a process plan generated in advance is found unfeasible or even unusable to targeted resources, resulting in wasted time and effort spent in early process planning – a productivity drop when idle machines must wait for re-planning the remaining operations. Within the context, an adaptive approach is considered suitable and is thus proposed in this paper for dealing with the dynamic situation, e.g. job-shop machining. Targeting the Cloud manufacturing, the objective of this research is to develop an Internet- and Web-based service-oriented system for machine availability monitoring and process planning. Particularly, this paper proposes a tiered system architecture and introduces an event-driven approach using IEC 61499 function blocks. By connecting to a Wise-ShopFloor framework, it enables real-time machine availability monitoring and machining status monitoring during metal cutting, locally or remotely. The closed-loop information flow makes process planning and monitoring feasible services for the Cloud manufacturing. The remainder of the paper is organised as follows. Section 2 reviews the state of the art of the relevant research works. Section 3 introduces a new Web-DPP concept, which is extended to system architecture design in Section 4. System analysis of the Web-DPP is reported in Section 5 in form of IDEF0. The system is implemented and outlined in Section 6. In Section 7, a sample part machining is chosen to demonstrate and validate the capability of the prototype system in terms of process planning and machine availability monitoring. Finally, research contributions and future work are summarised in Section 8.

نتیجه گیری انگلیسی

Targeting the future manufacturing shifting towards Cloud manufacturing, this paper proposes a Web-based and service-oriented approach for distributed machining process planning in a decentralised and dynamic manufacturing environment, particularly for SMEs of job-shop machining operations with uncertainty. The advantages of this approach include network-wide accessibility and adaptive decision-making capability to process planning with unpredictable shop-floor changes. This is facilitated by a web-based user interface and a real-time execution monitoring service of machine availability. A Web-DPP prototype has been designed and implemented as web services, which was extended from a Wise-ShopFloor framework to separate generic information from machine-specific ones. The novelty of this work can be summarised as: • Two-tier system architecture for distributed decision making. • Machining feature-based geometry reasoning for machining sequence planning. • Design of function blocks for controller-level operation planning. • Algorithm-based process execution and machine availability monitoring. • Closed-loop information flow for scheduling and job dispatching via real-time monitoring. The developed Web-DPP prototype runs inside a standard web browser, whereas the decision modules reside in one or more application servers, constituting a part of the Cloud manufacturing services. As a result of Cloud manufacturing, no dedicated software is needed to be installed in local computers at client side. The limitation of this prototype in its current implementation is the inability of dealing with complex products with freeform surfaces. Our future work will explore along the direction to cover more product variety and complexity. At the same time, our research effort will focus on functionality enhancement, new processing algorithm development, and testing using real-world cases via open-architecture CNC controllers in dynamic shop-floor environment. Moreover, integration with a third-party scheduling system, a more sophisticated feature-parsing system and a function block compliant CNC controller will also be under investigation, the results of which will be reported separately. It is envisioned that Cloud manufacturing will re-organise the manufacturing practices of today by means of Cloud services, where resources (software and hardware) can be shared cost-effectively by many. It is particularly useful and beneficial to SMEs who do not have the luxury of resources that are expensive for hosting and maintenance. Web-DPP intends to share knowledge and solutions in machining process planning with SMEs based on a pay-as-you-go or pay-per-use business model. Clients willing to use the service would open their machining resources for availability monitoring and thus adaptive machining by function blocks.

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