Abstract
Currently, machine tool selection, cutting tool selection and machining conditions determination are not usually performed at the same time but progressively, which may lead to suboptimal or trade-off solutions. Targeting this issue, this paper proposes a big data analytics based optimisation method for enriched Distributed Process Planning by considering machine tool selection, cutting tool selection and machining conditions determination simultaneously. Within the context, the machining resources are represented by data attributes, i.e. workpiece, machining requirement, machine tool, cutting tool, machine conditions, machining process and machining result. Consequently, the problem of machining optimisation can be treated as a statistic problem and solved by a hybrid algorithm. Regarding the algorithm, artificial neural networks based models are trained by machining data and used as optimisation objectives, whereas analytical hierarchy process is adopted to decide the weights of the multi-objective optimisation; and evolutionary algorithm or swarm intelligence is proposed to perform the optimisation. Finally, the results of a simplified proof-of-concept case study are reported to validate the proposed approach, where a Deep Belief Network model was trained by a set of hypothetic data and used to calculate the fitness of a genetic algorithm.
Highlights
Modern industries are characterised by smartness and intelligence in manufacturing, as identified in Industry 4.0 and Made in China 2025 where automation and digitalisation are the key elements
Apart from the theory and empirical research to address rather specific issues, the proposed method is to solve a machining optimisation issue from the big data’s perspective, where all machining resources are considered as a whole
It is important to show the potential of the proposed method in real-word applications and encourage companies to fully utilise the benefits of big data
Summary
Modern industries are characterised by smartness and intelligence in manufacturing, as identified in Industry 4.0 and Made in China 2025 where automation and digitalisation are the key elements. In modern manufacturing, machining being highly nonlinear is the most complex process. In order to decompose the complexity, a method named Distributed Process Planning (DPP) is used to divide the machining process planning into supervisory planning, execution control and operation planning (Wang et al 2003). Generic processes are obtained based on machining features and machining knowledge, whereas the resource-specific processes including machine tool selection, cutting tool selection and machining conditions determination are carried out during the execution control and operation planning. Journal of Intelligent Manufacturing (2019) 30:1483–1495 clusions” section, together with the contributions and future work of this research
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