Bernardete Ribeiro

In this work, a new methodology for the identification of the CPB06 yield criterion parameters is presented. This methodology is based on the application of Machine Learning models and the Levenberg-Marquardt optimization algorithm. The proposed methodology relies on data obtained from a biaxial tensile test with a cruciform specimen and aims to overcome some of the challenges usually faced during material characterization with the CPB06 yield criterion. The predictive performances achieved were positive overall, when comparing the yield surfaces obtained for testing cases, highlighting the potential of the proposed methodology.

The quality and dimensional accuracy of sheet metal components are strongly influenced by various sources of uncertainty, including variations in material properties, tool geometry and process parameters. Determining the specific source responsible for deviations in bending outcomes is usually costly and time-consuming, especially in industrial settings where numerous factors interact. In this study, a machine learning framework that can detect and quantify the impact of uncertainties in both air and bottom bending processes is presented. A dataset comprising forming results such as bending angles, final thickness and measured deviations, is used to train two neural networks metamodels (one for each process) that link input uncertainties to process outcomes. The predictive performance of these models was evaluated using different metrics achieving high predictive accuracy, with coefficients of determination close to 1 for most uncertainty sources in air bending and values above 0.95 for the majority of parameters in bottom bending. These results demonstrate the capability of the methodology to reliably identify dominant sources of uncertainty and support robust process optimization.

Real-world problems are often embedded in highly imbalanced contexts, where traditional machine learning algorithms struggle to achieve both strong performance and fairness. In high-stakes industries, incorporating sensitive attributes can enhance performance, but often at the cost of fairness. However, while artificial neural networks typically have the drawback of high energy usage, spiking neural networks (SNNs) offer a promising alternative because of their energy efficiency, in addition to their performance that is frequently hindered by their sensitivity to the hyperparameters. Our research introduces a fair and calibrated convolutional SNN modeling framework, CalibraSNN, designed for constrained, real-world problems by addressing the challenges in performance, fairness, energy usage, and sensitivity to hyperparameters. We evaluate our approach using the Bank Account Fraud dataset suite, which comprises real-world data and the business constraint of maintaining a false positive rate below 5%. Our results show up to 65% recall in fairly classifying fraudulent behaviors with competitive energy usage and power demand towards non-spiking approaches. The greener model is able to demand only 8 watts of power and use 30 joules with a recall of 54%. The experimental source code is available at https://github.com/dylanperdigao/CalibraSNN.

Industry 5.0, the fifth industrial revolution, envisions collaboration between humans and machines, where human intelligence directs decision-making and machines handle empirical processing. This paper presents a decision support framework that combines human-centered design with a multi-objective reinforcement learning model (MORL), specifically multi-criteria decision-making with deep Q-networks (MCDM-DQN). This approach evaluates the importance of maintenance factors in achieving sustainability in manufacturing, emphasizing the perspectives of various stakeholders. By fostering collaboration between stakeholders and the MCDM-DQN, the framework effectively integrates their feedback, improving prioritization according to the operational context of the organization. The experiments confirmed the effectiveness of the method, demonstrating that MCDM-DQN efficiently ranks key factors while adhering to conventional methods and offering advanced features such as real-time feedback. These results assist decision-makers in selecting appropriate sustainable strategies and improve the synergy between advanced automation and human insight within the Industry 5.0 framework, providing valuable guidance to leaders and practitioners.