4.7 Article

A method for minimizing the energy consumption of machining system: integration of process planning and scheduling

Journal

JOURNAL OF CLEANER PRODUCTION
Volume 137, Issue -, Pages 1647-1662

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2016.03.101

Keywords

Machining; Machining system; Energy consumption; Therblig; Integration of process planning and scheduling; Genetic algorithm

Funding

  1. National Natural Science Foundation of China [51175464]
  2. Defense Industrial Technology Development Program of China [A3920110001]
  3. Ningbo Science and Technology Innovation Team [2011B81006]

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Green Low-carbon development has been recognized as a key requirement for advanced manufacturing, and reducing the energy consumption of machining system is a critical aspect. Traditionally, process planning and scheduling were carried out separately and sequentially in manufacturing industry, which limits the work for energy-efficient machining system. Considering the fact that the functions of these two systems are usually complementary, the energy-saving of machining system can be further improved if they are tightly integrated. In this paper, an integration model based on nonlinear process planning (NLPP) is proposed to implement such energy-saving method, and the Therblig-based model is used to predict the energy consumption of machine tools in product manufacturing process. Then, a genetic algorithm-based approach is adopted to solve the established integration model. Given the alternative process schemes generated by NLPP, such integration model can help select the suitable process plan and machines for each job and generate the scheduling scheme simultaneously for saving energy. To verify the energy-saving effect and the performance of the algorithm, case studies have been conducted and the experimental results show that the energy-saving potential in a shop floor environment can be further enhanced through the integration of process planning and scheduling and the solution method is feasible. (C) 2016 Elsevier Ltd. All rights reserved.

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