The development of a data-driven decision support tool to reduce the energy consumption of a manufacturing process

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Date
2022-10-07
Authors
Morris, Liam
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University College Cork
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Abstract
With an ever-growing urgency to reduce energy consumption in the manufacturing industry, process stakeholders need more visibility and insights into how much energy they consume, or can expect to consume, for production. In industry today and with the use of Industry 4.0, the way data is utilised has evolved, with data collection and analysis performed digitally. With many long-established manufacturing processes, the jump from older empirical practices to digitalised practices can be difficult. Similarly, many process stakeholders use process data for different means such as production efficiency improvements. From this it can be difficult to ascertain what information is recorded on machines. And with various machines performing varying tasks in part production, this may drive high energy consumption. One such example is computer numerically controlled (CNC) machining tools. These tools are a common manufacturing apparatus and are known to consume energy inefficiently. This thesis describes the development of a hybrid methodology to identify and select key data features on a CNC machine in medical devices manufacturing. Subsequently, this data is used in an empirical energy consumption model of a CNC machine which enables the energy consumption to be determined from the number of parts processed by the machine. In using a calibrated approach, the data undergoes initial preparation followed by exploratory data analysis and subsequent model development via iteration. During this analysis, relationships between parameters are explored to identify which have the most significance on energy consumption. A training set of 191 data points yielded a linear correlation coefficient of 0.95 between the power consumption and total units produced. Root Mean Square Error, Mean Absolute Percentage Error and Mean Bias Error validation tests yielded results of 0.198, 6.4% and 2.66%, respectively.
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Energy model , Empirical model , Calibrated model , CNC , Digital model , Linear regression , Energy consumption , Decision support platform
Citation
Morris, L. 2022. The development of a data-driven decision support tool to reduce the energy consumption of a manufacturing process. MSc Thesis, University College Cork.