A systematic approach of process planning and scheduling optimization for sustainable machining. (15th January 2015)

• Record Type: Journal Article
• Authors: Wang, S.; Lu, X.; Li, X.X.; Li, W.D.
• Is Part Of: Journal of cleaner production. Volume 87(2015:Jan. 15)
• Page Start: 914
• Page End: 929
• Publication Date: 2015-01-15
• Subjects: Sustainable manufacturing -- Computer numerical control machining -- Process planning -- Process scheduling -- Intelligent algorithm -- Machining feature; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2014.10.008
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: The lack of effective process planning and scheduling solutions for the sustainable management of machining shop floors, whose manufacturing activities are usually characterized by high variety and low volume, has been crippling the implementation of sustainability in companies. To address the issue, an innovative and systematic approach for milling process planning and scheduling optimization has been developed and presented in this paper. This approach consists of a process stage and a system stage, augmented with intelligent mechanisms for enhancing the adaptability and responsiveness to job dynamics in machining shop floors. In the process stage, key operational parameters for milling a part are optimized adaptively to meet multiple objectives/constraints, i.e., energy efficiency of the milling process and productivity as objectives and surface quality as a constraint. In the consecutive system stage, to achieve higher energy efficiency and shorter makespan in the entire shop floor, sequencing/set-up planning of machining features/operations and scheduling for producing multiple parts on different machines are optimized. Artificial Neural Networks are used for establishing the complex nonlinear relationships between the key process parameters and measured datasets of energy consumption and surface quality. Several intelligent algorithms, including Pattern Search, Genetic Algorithm and Simulated Annealing, are applied and benchmarked to identify optimal solutions. Experimental tests indicate that the approach is effective and configurable to meet multiple objectives and technical constraints for sustainable process planning and scheduling. The approach, validated through industrial case studies provided by a European machining company, demonstrates significant potential of applicability in practice. Highlights: A systematic approach for sustainable process planning and scheduling is developed. Intelligent algorithms are applied for optimizing key machining process parameters. The effectiveness and potential are validated through industrial case studies.