4,842 research outputs found

    The challenges for energy efficient casting processes

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    Casting is one of the oldest, most challenging and energy intensive manufacturing processes. A typical modern casting process contains six different stages, which are classified as melting, alloying, moulding, pouring, solidification and finishing respectively. At each stage, high level and precision of process control is required. The energy efficiency of casting process can be improved by using novel alterations, such as the Constrained Rapid Induction Melting Single Shot Up-casting process. Within the present study the energy consumption of casting processes is analyzed and areas were great savings can be achieved are discussed. Lean thinking is used to identify waste and to analyse the energy saving potential for casting industry

    Energy consideration in machining operations - towards explanatory models for optimisation results

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    Part of: Seliger, Günther (Ed.): Innovative solutions : proceedings / 11th Global Conference on Sustainable Manufacturing, Berlin, Germany, 23rd - 25th September, 2013. - Berlin: Universitätsverlag der TU Berlin, 2013. - ISBN 978-3-7983-2609-5 (online). - http://nbn-resolving.de/urn:nbn:de:kobv:83-opus4-40276. - pp. 153–158.This paper reports the application of a systematic research methodology for uncovering the reasons behind results obtained when energy is considered in machining optimisation. A direct search optimisation method was used as a numerical experimentation rig to investigate the reasoning behind the results obtained in applying Taguchi methods and Genetic algorithm (GA). Representative data was extracted from validated machining science equations and studied using graphical multivariate data analysis. The results showed that over 80% of reduction in energy consumption could be achieved over the recommendations from machining handbooks. It was shown that energy was non-conflicting with the cost and time, but conflicting with quality factors such as surface roughness and technical factors such as power requirement and cutting force. These characteristics of the solutions can provide an explanative motif required for practitioners to trust and use the optimisation results

    A new model and metaheuristic approach for the energy-based resource-constrained scheduling problem

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    [EN] This article focuses on obtaining sustainable and energy-efficient solutions for limited resource programming problems. To this end, a model for integrating makespan and energy consumption objectives in multi-mode resource-constrained project scheduling problems (MRCPSP-ENERGY) is proposed. In addition, a metaheuristic approach for the efficient resolution of these problems is developed. In order to assess the appropriateness of theses proposals, the well-known Project Scheduling Problem Library is extended (called PSPLIB-ENERGY) to include energy consumption to each Resource-Constrained Project Scheduling Problem instance through a realistic mathematical model. This extension provides an alternative to the current trend of numerous research works about optimization and the manufacturing field, which require the inclusion of components to reduce the environmental impact on the decision-making process. PSPLIB-ENERGY is available at http://gps.webs.upv.es/psplib-energy/.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Spanish Government under the research projects TIN2013-46511-C2-1 and TIN2016-80856-R.Morillo-Torres, D.; Barber, F.; Salido, MA. (2017). A new model and metaheuristic approach for the energy-based resource-constrained scheduling problem. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture. 1(1):1-13. https://doi.org/10.1177/0954405417711734S1131

    An optimization-based control strategy for energy efficiency of discrete manufacturing systems

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    In order to reduce the global energy consumption and avoid highest power peaks during operation of manufacturing systems, an optimization-based controller for selective switching on/off of peripheral devices in a test bench that emulates the energy consumption of a periodic system is proposed. First, energy consumption models for the test-bench devices are obtained based on data and subspace identification methods. Next, a control strategy is designed based on both optimization and receding horizon approach, considering the energy consumption models, operating constraints, and the real processes performed by peripheral devices. Thus, a control policy based on dynamical models of peripheral devices is proposed to reduce the energy consumption of the manufacturing systems without sacrificing the productivity. Afterward, the proposed strategy is validated in the test bench and comparing to a typical rule-based control scheme commonly used for these manufacturing systems. Based on the obtained results, reductions near 7% could be achieved allowing improvements in energy efficiency via minimization of the energy costs related to nominal power purchased.Peer ReviewedPostprint (author's final draft

    AI and OR in management of operations: history and trends

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    The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
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