Muhammad Amir Aminuddin creator author. text my 2023 2023 monographic eng

electronic

xviii, 80 pages : illustrations ; 1 CD-ROM. Computer Numerical Control machining is a subtractive manufacturing technique that removes layers of material from a blank or workpiece to create a specific product. It is widely used in numerous industries including electronics. Electronic manufacturing organizations are facing an ever-increasing level of competition on a global scale making it an absolute requirement to decrease the amount of downtime that occurs during production operations to maximize machine availability and productivity. To ensure that customers' demands are met on time, downtime should be evaluated to identify the underlying causes of the issue and mitigate its effects. The study aims to analyze and forecast the trend of Computer Numerical Control (CNC) machine downtime through predictive modelling using machine learning models. In this study, eXtreme Gradient Boosting and Random Forest have been used to forecast the trend of future downtime occurrences. Based on the results of comparative performance analysis, the study reveals that eXtreme Gradient Boosting model outperforms Random Forest in forecasting future CNC downtime, demonstrating lower prediction errors. The outcome of this research is an interactive dashboard that integrates the analysis of historical and forecasted CNC machine downtime trends. This effort aims to provide valuable support to the industry by enhancing their preventive maintenance. specialized Muhammad Amir Bin Aminuddin Center for Mathematical Sciences Bachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023 Includes bibliographical references Center for Mathematical Sciences Dissertations Universities and colleges Dissertations Final Year Report Dissertations THE0010026 (Local) UMPSA 250505 20251125111037.0 eng