A big data analytics based machining optimisation approach

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.

Current landscape and influence of big data on finance

“…At present, most of the areas of business are linked to big data. It has significant influence on various perspectives of business such as business process management, human resources management, R&D management [8,63], business analytics [19,26,42,59,63], B2B business process, marketing, and sales [30,39,53,58], industrial manufacturing process [7,15,40], enterprise's operational performance measurement [20,69,81], policy making [2], supply chain management, decision, and performance [4,38,64], and so other business arenas.…”

“…This study also talked about three sophisticated types of analytics techniques such as descriptive analytics, predictive analytics, and prescriptive analytics in order to improve the traditional data analytics process. Duan and Xiong [19], Grover and Kar [26], Ji et al [42], and Pappas et al [59] also explored the significance of big data in business analytics. Big data helps to solve business problems and data management through system infrastructure, which includes any technique to capture, store, transfer, and process data.…”

Industrial robotic machining: a review

“…Machining is one of the most complex processes within manufacturing area [103]; therefore, it is a big challenge for robot manipulator with low stiffness. Many topics related to robotic machining have been discussed in terms of factors with conventional CNC machining.…”

A review on cutting tool technology in machining of Ni-based superalloys

“…The hidden patterns in big data enable a much-improved machining optimization when combined with analytics algorithms. By combining machining condition optimization, cutting tool design/selection, and machine tool selection as a whole, big data analytics has been applied to machining optimization [115][116][117]. In this way, the machining-related decision process can be transformed into a data problem, which could provide a global optimization for machining, as shown in Fig.…”