A hybrid four-stage detection model to pre-identify the sustainable manufacturing process of Li-ion battery pack.

The usage of lithium-ion batteries has significantly increased by various applications in recent years due to the advantages of long lifespan, high energy density, high power density, and eco-friendly environment benefits for sustainable usage. Although it has attracted much interest on its manufacturing process from practitioner in Industry 4.0 now, academia has relatively less concern on addressing the manufacturing process in using hybrid techniques. Thus, an advanced hybrid four-stage detection model is proposed into the original process to help identify optimal real-time production performance to hold sustainable manufacturing. This proposed hybrid model creates four key detection stages corresponded to four core challenges, including autoregressive integrated moving average (ARIMA), If-Then-Else programming rule, convolutional neural networks (CNNs), and artificial neural networks (ANNs) for achieving the purpose of economizing manpower and material resources to save manufacturing cost. (1) In cyclic process stage, ARIMA makes a successful prediction of a 20-min discharging curve with a significant low 13.38% error rate. (2) If-Then-Else programming rule sets up min–max threshold range (MMTR) on both attributes of voltage and capacity to find out failure packs before final test 1 and benefit with saving manual testing time. (3) CNNs achieve 100% classification accuracy in a productive result and save average 3.5 h for each battery pack. (4) ANNs conclude an empirical result of 100% accuracy in predicting the battery pack pass or failure. Conclusively, the study makes a comparative research with various deep learning algorithms to evaluate its performance; the proposed hybrid detection model is never seen in the challengeable lines of predicting Li-ion battery pack, and thus, it has an innovative value and priority performance for benefiting the sustainable manufacturing on offering green and renewable energy. This study contributes the research rationality and practical significance. [ABSTRACT FROM AUTHOR]