Improving the High-Quality Product Consistency in a Digital Manufacturing Environment

Producing high-quality product consistently is crucial in manufacturing, as discarding or reprocessing low-quality products increases waste and energy consumption and reduces overall production efficiency. Ensuring high-quality manufactured products is challenging due to relying on human activities for product quality and related consistency assessment, which is often performed postproduction instead of assessing these during each production run and making real-time production adjustments that can mitigate product quality and related consistency issues. In this article, we proposes a novel machine-state learner algorithm that captures the dependencies between product quality and related consistency and machine data (specifically the machine settings and corresponding sensor data). In addition, this article shows how this novel machine-state learner algorithm can be used to predict product quality during the production runs and how such prediction are used to make machine setting recommendations that mitigate product quality and related consistency issues before or during the production runs. These advances in machine state-based data modeling, predictive data analysis and recommendation are incorporated into an inline prediction and decision support system that achieves significant improvement in producing high-quality products consistently by guiding decision-making via recommendations during production in a digital manufacturing environment. In this article, we present an evaluation of the above contributions in a real-world manufacturing plant and yield double digit first pass and nearly perfect second pass product improvements in terms of product quality and related consistency and production efficiency.