Browsing by Author "Peres, Ricardo Silva"
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- IDARTS – Towards intelligent data analysis and real-time supervision for industry 4.0Publication . Peres, Ricardo Silva; Rocha, André Dionísio; Leitão, Paulo; Barata, JoséThe manufacturing industry represents a data rich environment, in which larger and larger volumes of data are constantly being generated by its processes. However, only a relatively small portion of it is actually taken advantage of by manufacturers. As such, the proposed Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework presents the guidelines for the implementation of scalable, flexible and pluggable data analysis and real-time supervision systems for manufacturing environments. IDARTS is aligned with the current Industry 4.0 trend, being aimed at allowing manufacturers to translate their data into a business advantage through the integration of a Cyber-Physical System at the edge with cloud computing. It combines distributed data acquisition, machine learning and run-time reasoning to assist in fields such as predictive maintenance and quality control, reducing the impact of disruptive events in production.
- Improvement of multistage quality control through the integration of decision modeling and cyber-physical production systemsPublication . Rocha, André Dionísio; Peres, Ricardo Silva; Barata, José; Barbosa, José; Leitão, PauloThe 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.
- Integration and deployment of a distributed and pluggable industrial architecture for the PERFoRM projectPublication . Angione, Giacomo; Barbosa, José; Gosewehr, Frederik; Leitão, Paulo; Massa, Daniele; Matos, João; Peres, Ricardo Silva; Rocha, André Dionísio; Wermann, JeffreyTo meet flexibility and reconfigurability requirements, modern production systems need hardware and software solutions which ease the connection and mediation of different and heterogonous industrial cyber-physical components. Following the vision of Industry 4.0, the H2020 PERFoRM project targets, particularly, the seamless reconfiguration of robots and machinery. This paper describes the implementation of a highly flexible, pluggable and distributed architecture solution, focusing on several building blocks, particularly a distributed middleware, a common data model and standard interfaces and technological adapters, which can be used for connecting legacy systems (such as databases) with simulation, visualization and reconfiguration tools.
- Multistage quality control using machine learning in the automotive industryPublication . Peres, Ricardo Silva; Barata, José; Leitão, Paulo; Garcia, GiselaProduct dimensional variability is a crucial factor in the quality control of complex multistage manufacturing processes, where undetected defects can easily be propagated downstream. The recent advances in information technologies and consequently the increased volume of data that has become readily available provide an excellent opportunity for the development of automated defect detection approaches that are capable of extracting the implicit complex relationships in these multivariate data-rich environments. In this paper, several machine learning classifiers were trained and evaluated on varied metrics to predict dimensional defects in a real automotive multistage assembly line. The line encompasses two automated inspection stages with several human-operated assembly and pre-alignment stages in between. The results show that non-linear models like XGBoost and Random Forests are capable of modelling the complexity of such an environment, achieving a high true positive rate and showing promise for the improvement of existing quality control approaches, enabling defects and deviations to be addressed earlier and thus assist in reducing scrap and repair costs.
- Selection of a data exchange format for industry 4.0 manufacturing systemsPublication . Peres, Ricardo Silva; Parreira-Rocha, Mafalda; Rocha, André Dionísio; Barbosa, José; Leitão, Paulo; Barata, JoséWith the emergence of the Industry 4.0 concept, or the fourth industrial revolution, the industry is bearing witness to the appearance of more and more complex systems, often requiring the integration of various new heterogeneous, modular and intelligent elements with pre-existing legacy devices. This challenge of interoperability is one of the main concerns taken into account when designing such systems-of-systems, commonly requiring the use of standard interfaces to ensure this seamless integration. To aid in tackling this challenge, a common format for data exchange should be adopted. Thus, a study to select the foundations for the development of such a format is hereby presented, taking into account the specific needs of four different use cases representing varied key European industry sectors.