Affiliation:
1. Dyrecta Lab srl, Italy
Abstract
The chapter presents different case studies involving technology upgrading involving Industry 4.0 technologies and artificial intelligence. The work analyzes four cases of study of industry projects related to manufacturing process of kitchen, tank production, pasta production, and electronic welding check. All the cases of study concern the analysis of engineered processes and the inline implementation of image vision techniques. The chapter discusses other topics involved in the production process such as augmented reality, quality prediction and predictive maintenance. The classic methodologies to map production processes are matched with innovative technologies of image segmentation and data mining predicting defects, machine failures, and product quality. The goal of the chapter is to prove how the combination of image processing techniques, data mining approaches, process simulation, chart process modeling, and process reengineering can constitute a scientific research project in industry research.
Reference29 articles.
1. Improvedpcharts to monitor process quality
2. A Metamodel for Evaluating Enterprise Readiness in the Context of Industry 4.0.;J.Basl;Information,2019
3. Bastos, P., Lopes, P. I., & Pires, L. (2014). Application of data mining in a maintenance system for failure prediction. In Safety, Reliability and Risk Analysis: Beyond the Horizon. Taylor & Francis Group.
4. Augmented reality: an application of heads-up display technology to manual manufacturing processes
5. Chakraborty, A. (2016). Importance of PDCA cycle for SMEs. SSRG International Journal of Mechanical Engineering, 3(5), 30-34.
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献