Deep learning‐based clustering of processes and their visual exploration: An industry 4.0 use case for small, medium‐sized enterprises

Abstract This paper proposes a multi‐stage approach consisting of deep learning‐based image classification, process trace clustering, and visual/statistical knowledge discovery of process data. The proposed decision augmentation solution aims to facilitate the production planners in estimating the process‐specific production parameters such as activity duration, idle time, or machine utilization. This study focuses on ‘one‐of‐a‐kind production’ (OKP). Planning in OKP is especially challenging due to the increasing individualization of customer requirements. Furthermore, the uniqueness of products adds complexity to data and information structuring. To tackle this issue, we first train deep convolutional neural networks (CNN) with image data of production parts obtained from computer‐aided design (CAD) systems to extract meaningful features. After cross‐validation, uncertainty, and robustness assessment of the adopted deep learning approach, we use the data representation from the penultimate layer as input for clustering production parts. The goodness of clustering results is evaluated using a series of internal clustering validation indices. Finally, process event log data provided by manufacturing execution systems (MES) is mapped to each production part, allowing us to conduct statistical and visual knowledge discovery of process parameters for each cluster. The relevance of our proposed approach has been validated by studying a real‐world use case in a small, medium‐sized enterprise (SME) operating in the fixture and jig manufacturing industry.