A guideline to implement a CPS architecture in an SME
In Industry 4.0 context, data valorisation allows industries to develop new capabilities, create competitive advantages and achieve manufacturing sustainability, but technological infrastructures are needed to support system interoperability and to manage datas. These infrastructures are not enought mature in many industrial environments, especially in small and medium enterprises (SMEs). Technology integration is challenging due to system and information heterogeneity , and even more so in SMEs that have constraint environment and which lack specific research study. Although several approaches have been proposed, the literature lacks empirical evidence of the adoption of new technologies in SMEs. This paper presents a guideline for implementing a Cyber-Physical system (CPS) architecture in an SME and its application in an organic flour mill in Montreal. The case study provides evidence of the possibility to implement a CPS architecture in SMEs and can serve as an inspiration for SME to develop an Industry 4.0 strategy.
Applying deep learning to sensor data to support workers in manufacturing
To achieve next-generation production systems and Multiverse Mediation with CPSs, 4M (huMan, Machine, Material, and Method) work transitions need to be clarified and used more accurately. However, traditional systems cannot detect deviations in manual procedures. To resolve these issues, we are developing a highly accurate detection technology for “human work”. Figure 2 shows the assembly cells considered in this study.
Compared to conventional approaches, we achieved a 15% reduction in product assembly time and a deviation detection leak of almost zero (more than 95% work identification accuracy). These results demonstrated the potential for our system to efficiently and effectively support manufacturing workers and contribute to greater efficiency and quality management in the assembly of complex equipment.