Data-Driven Predictive Maintenance of Spare Parts for Smart Manufacturing

The rise of Artificial Intelligence (AI) and Industry 4.0 has led to a growing interest in predictive maintenance strategies, which offer benefits like reduced downtime, increased availability, and improved efficiency. This paper explores data-driven predictive maintenance of spare parts at a smart manufacturing company, based on AI methodologies to enhance efficiency and reduce downtime. The success of a smart manufacturing company is partly attributed to its advanced production facilities, particularly the precision injection moulds used for producing detailed and consistent parts. Injection moulding involves melting plastic and injecting it into a mould under high pressure. These moulds consist of many critical spare parts, such as gate bushes and inserts, which are prone to wear and tear due to intense pressures and temperatures. Failures in these small parts can halt production and affect efficiency. This study highlights the limitations of deep learning models due to insufficient data and the need for explain-ability and interpretability of models due to interaction with non-technical personnel. Also, results show that tree-based classification models, particularly Random Forest (RF) and XGBoost, perform best, with test accuracies of 69.59% for gate bushes and 69.23% for centre units. This investigation advances the manufacturing company's predictive maintenance capabilities, offering insights for future AI-driven maintenance optimization, leading to reduced costs, enhanced efficiency, and improved health and safety standards.

Keywords: Artificial intelligence, Data-driven modelling, Explain-ability, Predictive maintenance, Smart manufacturing, Spare parts.