Data-driven Context Awareness of Smart Products in Discrete Smart Manufacturing Systems

Traditionally, smart-connected products are predominantly utilized during the usage phase of the product lifecycle. However, we argue that there are distinct benefits of system-integrated sensor systems during the beginning of life, more specifically in manufacturing and assembly. In this paper, we analyze the ability of a smart-connected product with an integrated sensor system to recognize and label different manufacturing processes, generating a distinct process fingerprint within a discrete smart manufacturing system. The ability of the smart-connected product to detect distinct manufacturing process patterns ('process fingerprint') enables the production planner and operator, e.g., to optimize the scheduling, improve part quality, and/or reduce the energy footprint. The experimental setup is based on a FestoDidactics CPlab with eight different manufacturing processes. The smart-connected product is equipped with a sensor system providing data from eight different sensors (e.g., temperature, humidity, acceleration). We used an Artificial Neural Network (ANN) algorithm to create a model to detect specific events/patterns within the dataset after labelling it manually over the course of a complete production cycle. The focal manufacturing process was the heating tunnel where the smart-connected product was exposed to a heat treatment process and sequence. The results of this prototypical implementation indicate that a smart-connected product can reliably recognize specific process patterns with a system-integrated sensor system during a simulated manufacturing process. While this work is only a first step, the potential applications and benefits are promising and further research should focus on the potential quality implications within smart manufacturing of product-integrated sensor readings compared to machine tool-based sensors, both of which monitored during the beginning of life. Smart products' integrated sensor systems provide the means to obtain measurements relevant for smart manufacturing systems that are not obtainable with common external sensor systems today. (C) 2020 The Authors. Published by Elsevier B.V.