A Novel Mechanism for Continual Learning based Predictive Quality Inspection in Smart Manufacturing

Edge-enabled Deep Learning (DL) solutions for Predictive Quality Inspection (PQI) of products in Industry 4.0 are mostly designed for static manufacturing environments. In general, modern manufacturing processes are dynamic in nature. In this context, continual learning-based model retraining accommodates the dynamism for PQI of multiple processes (tasks) using a single DL model. However, the impact of the task ordering in sequentially arriving tasks and solution to reduce this impact on the overall PQI is yet to be solved. To this end, a novel mechanism using a light-weight similarity analysis module is introduced in the quality prediction system at the resource-limited edge. Sequential training of tasks above a similarity threshold (γ) is preferred, and dissimilar tasks are overlooked to train a separate model. This enables a PQI system to hover over training efficiency and model sustainability. The experimental results validate the impact of task order and the effectiveness of the proposed similarity-based analysis to reduce this impact by 70% on the model's overall performance in the real-world use case of plastic bricks.