Blow Molding Process Automation using Data-Driven Tools

With the increasing demand for goods in today’s world with its ever-increasing population, industries are driven to boost their production rate. This makes issues of process optimization, maintenance, and quality control difficult to be carried out manually. Furthermore, most industrial sectors have a plethora of data acquired every day with very little knowledge and ideas to handle it most effectively. Motivated by these considerations, we carry out a study in process automation using data-driven tools for a manufacturing process intended to produce high-quality containers. The first task involved studying the process, building and proving a hypothesis through data collection from experiments and the production line. Our hypothesis was the linear correlations between various sensor variables from our physical understanding of the process. We developed an automated process flow, i.e., a so-called Digital Twin (a numerical replication of the entire process) for this process to enhance the analytical and predictive capabilities of the process. This showed similar predictions when compared to static models. An automated in-line quality control algorithm was also built to remove the manual component from this task, using state-of-the-art computer vision techniques to utilize the power of data in the form of images. Lastly, to further provide the process engineers with predictive power for maintenance we carried out a few proof of concept projects to show the competence of such tools in minimizing costs and improving efficiency on the shop floor. All studies carried out showed great results and have immense potential to methodically use data to solve some of the pressing problems in the manufacturing sector

Improvement of multistage quality control through the integration of decision modeling and cyber-physical production systems

The proliferation of Information and Communication Technologies allowed the development of new solutions to be applied at the shop-floor and all the tools which helps the manufacturers. Hence, new solutions such as cyberphysical production systems, data analytics and knowledge management were developed and proposed to solve the wellknown issues, such as quality control in multistage manufacturing systems. However, those solutions can only have a small contribution in solving that issues compared to an optimized and fully integrated approach. To allow the development of a fully integrated environment, it is necessary to deliver a standard way to communicate and interact with the different functionalities. The proposed research aims to provide an integration layer, capable of translating the rules defined at the knowledge management level, structured as Decision Model and Notation rules, into an AutomationML based language. This allows the cyber-physical production system the ability to apply these rules near the shop-floor. This article presents the template defined to represent the rules in AutomationML as well as the infrastructure developed to receive the rules from the knowledge management, translate them and deliver to the cyber-physical production system. At the end of the article is presented a test bed where the solution is instantiated with rules focused on quality control.info:eu-repo/semantics/publishedVersio