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.

Remote control devices are commonly used for interaction with multimedia equipment and applications (e.g., smart TVs, gaming, etc.). To improve conventional keypad-based technologies, haptic feedback and user input capabilities are being developed for enhancing the UX and providing advanced functionalities in remote control devices. Although the sensation provided by haptic feedback is similar to mechanical push buttons, the former offers much greater flexibility, due to the possibility of dynamically choosing different mechanical effects and associating different functions to each of them. However, selecting the best haptic feedback effects among the wide variety that is currently enabled by recent technologies, remains a challenge for design engineers aiming to optimise the UX. Rich interaction further requires text input capability, which greatly influences the UX. This work is a contribution towards UX evaluation of remote control devices with haptic feedback and text input. A user evaluation study of a wide variety of haptic feedback effects and text input methods is presented, considering different technologies and different number of actuators on a device. The user preferences, given by subjective evaluation scores, demonstrate that haptic feedback has undoubtedly a positive impact on the UX. Moreover, it is also shown that different levels of UX are obtained, according to the technological characteristics of the haptic actuators and how many of them are used on the device.

The rapid integration of Machine Learning (ML) in organizational practices has driven demand for substantial computational resources, incurring both high economic costs and environmental impact, particularly from energy consumption. This challenge is amplified in dynamic data environments, where ML models must be frequently retrained to adapt to evolving data patterns. To address this, more sustainable Machine Learning Operations (MLOps) pipelines are needed for reducing environmental impacts while maintaining model accuracy. In this paper, we propose a model reuse approach based on data similarity metrics, which allows organizations to leverage previously trained models where applicable. We introduce a tailored set of meta-features to characterize data windows, enabling efficient similarity assessment between historical and new data. The effectiveness of the proposed method is validated across multiple ML tasks using the cosine and Bray–Curtis distance functions, which evaluate both model reuse rates and the performance of reused models relative to newly trained alternatives. The results indicate that the proposed approach can reduce the frequency of model retraining by up to 70% to 90% while maintaining or even improving predictive performance, contributing to more resource-efficient and sustainable MLOps practices.

The rapid integration of Machine Learning (ML) in organizational practices has driven demand for substantial computational resources, incurring both high economic costs and environmental impact, particularly from energy consumption. This challenge is amplified in dynamic data environments, where ML models must be frequently retrained to adapt to evolving data patterns. To address this, more sustainable MLOps pipelines are needed that reduce environmental impacts while maintaining model accuracy. In this paper, we propose a model reuse approach based on data similarity metrics, allowing organizations to leverage previously trained models where applicable. We introduce a tailored set of meta-features to characterize data windows, enabling efficient similarity assessment between historical and new data. The effectiveness of the proposed method is validated across multiple ML tasks using cosine and Bray-Curtis distance functions, evaluating both model reuse rates and the performance of reused models relative to newly trained alternatives. The results indicate that the proposed approach can significantly reduce the frequency of model retraining while maintaining or even improving predictive performance, contributing to more resource-efficient and sustainable MLOps practices.