A design of experiments Cyber–Physical System for energy modelling and optimisation in end-milling machining. (April 2023)

• Record Type: Journal Article
• Authors: Pantazis, Dimitrios; Pease, Sarogini Grace; Goodall, Paul; West, Andrew; Conway, Paul
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 80(2023)
• Publication Date: 2023-04
• Subjects: Cyber–Physical System -- Automotive machining -- Design of experiments -- Machine learning -- Feature extraction -- Energy waste; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2022.102469
• Languages: English
• ISSNs: 0736-5845

Abstract

Abstract: Industrial energy consumption accounts for 50% of global use and manufacturers that invest in energy waste reduction strategies can have a significant impact on emission reduction while ensuring they operate within energy usage limits. Exceeding these limits can result in taxation from national and international policy makers and charges from national energy providers. For example, the UK Climate Change Levy, charged to businesses at 0.554 p/kWh can equate to 7.28% of a manufacturing business's energy bill based on an average total usage rate of 7.61 p/kWh. There has been growing interest in optimising the process energy consumption of machining when machine tools are responsible for 13% of industrial energy consumption, generating 16 million tonnes of CO 2 emissions in the UK alone but demonstrate less than 30% energy efficiency (Gutowski et al., 2006). This paper presents the design, development and validation of a novel automated Design of Experiments (DoE) toolset that forms part of a larger Cyber–Physical System (CPS). The CPS offers the capability to automate, characterise and predict the power of three-phase industrial machining processes and to select the machining toolpath that optimises energy consumption. Validation of the DoE toolset has been conducted through automation of an industrial three-phase Hurco VM1 computer numerical control (CNC) machine and energy feature extraction with a Hidden Markov Model. Highlights: A Cyber–Physical System for energy optimisation through automation of experimental design. Validation demonstrates automation of industrial CNC machine and Markov feature extraction. Toolset automates Design of Experiments, feature extraction and workpiece creation. Enables the user to determine impact of spindle speed, feed rate, depth of cut and width of cut.