Angélica Muffato Reis

Inspection strategy (IS) is a key component impacting quality costs. Although often considered an inflexible output of initial quality plans, it may require revisions given the dynamic quality situation of the manufacturing system. It is from this background that the present study aims to model and compare different IS based on the cost of quality (CoQ) approach for a case study in the automotive manufacturing industry. While many computational inspection strategy models (ISMs) are available in the literature, most of them face application challenges and struggle to incorporate real-world data. The present study addresses this gap by developing a model that not only represents a real testing station in a manufacturing line but also uses historical production data. Additionally, in relation to model inputs, this study explores the challenges and opportunities of acquiring reliable quality cost estimates in the Industry 4.0 context. Among the main contributions of this work, the developed CoQ-based ISM can be used as a decision-making aiding tool for inspection revision and improvement, while conclusions about quality cost data collection in the industrial digitalization context can help advance the CoQ approach in practice.

Human-Robot Collaboration (HRC) systems are often implemented seeking for reducing risk of Work-related Musculoskeletal Disorders (WMSD) development and increasing productivity. The challenge is to successfully implement an industrial HRC to manage those factors, considering that non-linear behaviors of complex systems can produce counterintuitive effects. Therefore, the aim of this study was to design a decision-making framework considering the key ergonomic methods and using a computational model for simulations. It considered the main systemic influences when implementing a collaborative robot (cobot) into a production system and simulated scenarios of productivity and WMSD risk. In order to verify whether the computational model for simulating scenarios would be useful in the framework, a case study in a manual assembly workstation was conducted. The results show that both cycle time and WMSD risk depend on the Level of Collaboration (LoC). The proposed framework helps deciding which cobot to implement in a context of industrial assembly process. System dynamics were used to understand the actual behavior of all factors and to predict scenarios. Finally, the framework presented a clear roadmap for the future development of an industrial HRC system, drastically reducing risk management in decision-making.