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Projeto de investigação
Zero Defect Manufacturing Platform
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Relational network of innovation ecosystems generated by digital innovation hubs
Publication . Serrano-Ruiz, Julio C.; Ferreira, José; Jardim-Goncalves, Ricardo; Ortiz, Ángel; CTS - Centro de Tecnologia e Sistemas; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; Springer Netherlands
Collaboration plays a key role in the success attained to date by networks of innovation ecosystems generated around entities known as Digital Innovation Hubs (DIHs), recently created following European Commission initiatives to boost the digitisation of the European economic fabric. This article proposes a conceptual framework that brings together, defines, structures and relates the concepts involved in the collaborative interaction processes within and between these innovation ecosystems to allow comprehensive conceptualisation. The developed framework also provides an approach that helps to tangibilise collaboration as a management process. Here the goal is to ultimately move towards not only qualitative, but also quantitative modelling to bridge the research gap in the state of the art in this respect. The data-driven business-ecosystem-skills-technology (D-BEST) model, devised to configure DIHs service portfolios in a collaborative context, provides the reference basis for the interorganisational asset transfer methodology (IOATM). This is the keystone that structures the framework and constitutes its main contribution. Through the IOATM, this conceptual framework points out collaboration quantification, and serves as a lever for its modelling to deal with collaboration accounting by: turning it into a more controllable management element; guiding practitioners' efforts to improve collaborative processes efficiency with an approach that pursues objectivity and maximises synergies.
Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing
Publication . Silva, Bruno; Marques, Ruben; Faustino, Dinis; Ilheu, Paulo; Santos, Tiago; Sousa, João; Rocha, André Dionisio; DEE - Departamento de Engenharia Electrotécnica e de Computadores; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; CTS - Centro de Tecnologia e Sistemas; MDPI - Multidisciplinary Digital Publishing Institute
With the spread of the Industry 4.0 concept, implementing Artificial Intelligence approaches on the shop floor that allow companies to increase their competitiveness in the market is starting to be prioritized. Due to the complexity of the processes used in the industry, the inclusion of a real-time Quality Prediction methodology avoids a considerable number of costs to companies. This paper exposes the whole process of introducing Artificial Intelligence in plastic injection molding processes in a company in Portugal. All the implementations and methodologies used are presented, from data collection to real-time classification, such as Data Augmentation and Human-in-the-Loop labeling, among others. This approach also allows predicting and alerting with regard to process quality loss. This leads to a reduction in the production of non-compliant parts, which increases productivity and reduces costs and environmental footprint. In order to understand the applicability of this system, it was tested in different injection molding processes (traditional and stretch and blow) and with different materials and products. The results of this document show that, with the approach developed and presented, it was possible to achieve an increase in Overall Equipment Effectiveness (OEE) of up to 12%, a reduction in the process downtime of up to 9% and a significant reduction in the number of non-conforming parts produced. This improvement in key performance indicators proves the potential of this solution.
Zero-defect manufacturing terminology standardization
Publication . Sousa, João; Nazarenko, Artem A.; Grunewald, Christian; Psarommatis, Foivos; Fraile, Francisco; Meyer, Olga; Sarraipa, Joâo; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; CTS - Centro de Tecnologia e Sistemas
Zero-Defect Manufacturing (ZDM) is the next evolutionary step in quality management for manufacturing that makes use of Industry 4.0 technologies to support quality in manufacturing. These technologies help reduce the cost of inspection, allowing for more inspection points throughout the manufacturing process, reducing the size of quality feedback loops, and guaranteeing that no defective product is delivered to the customer. There are several ZDM-related initiatives, but still no harmonized terminology. This article describes the methodological approach to provide a common agreement on the ZDM concept and its associated terminology taking place within an open CENCENELEC Workshop. The methodology has the support of ISO standards for terminology work such as ISO 704, ISO 860, and ISO 10241–1/2. This work shows that the terminology for ZDM has a significant overlap with the terminology of quality management, metrology, dependability, statistics, non-destructive inspection, and condition monitoring. The proposed new terms and definitions can be used to further extend ISO’s and IEC’s already available terminologies and support present and future researchers in the field to conduct their research using a common vocabulary.
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Entidade financiadora
European Commission
Programa de financiamento
H2020
Número da atribuição
825631