The additive manufacturing (AM) field is rapidly expanding, attracting significant scientific attention. This family of processes will be widely used in the evolution of Industry 4.0, particularly in the production of customized components. However, as the complexity and variability of additive manufacturing processes increase, there is an increasing need for advanced techniques to ensure quality control, optimize performance, and reduce production costs. Multiple tests are required to optimize processing variables for specific equipment and processes, to achieve optimum processing conditions. The application of digital twins (DTs) has significantly enhanced the field of additive manufacturing. A digital twin, abbreviated as DT, refers to a computer-generated model that accurately depicts a real-world object, system, or process. A DT comprises the complete additive manufacturing process, from the initial conception phase to the final manufacturing phase. It enables the manufacturing process to be continuously monitored, studied, and optimized in real time. DT has emerged as an important tool in the additive manufacturing industry. They allow manufacturers to enhance the process, improve product quality, decrease costs, and accelerate innovation. However, the development of DT in AM is an iterative and continuous process. It requires collaboration between domain experts, data scientists, engineers, and manufacturing teams to guarantee an accurate representation of the process by the digital twin. This paper aims to provide a comprehensive analysis of the current state of DT for additive manufacturing, examining their applications, benefits, challenges, and future directions.

This paper introduces an innovative approach to Wind Turbine Blade (WTB) inspection through the synergistic use of thermal and RGB imaging, coupled with advanced deep learning techniques. We curated a unique dataset of 1000 thermal images of healthy and faulty blades using a FLIR C5 Compact Thermal Camera, which is equipped with Multi-Spectral Dynamic Imaging technology for enhanced imaging. This paper focuses on evaluating 35 deep learning classifiers, with a standout ensemble model combining Vision Transformer (ViT) and DenseNet161, achieving a remarkable 100% accuracy on the dataset. This model demonstrates the exceptional potential of deep learning in thermal diagnostic applications, particularly in predictive maintenance within the renewable energy sector. Our findings underscore the synergistic combination of ViT’s global feature analysis and DenseNet161’s dense connectivity, highlighting the importance of controlled environments and sophisticated preprocessing for accurate thermal image capture. This research contributes significantly to the field by providing a comprehensive dataset and demonstrating the efficacy of several deep learning models in ensuring the operational efficiency and reliability of wind turbines.

Fused Deposition Modelling (FDM) is one of the layer-based technologies that fall under the umbrella term “Additive Manufacturing”, where the desired part is created through the successive layer-by-layer addition process with high accuracy using computer-aided design data. Additive manufacturing technology, or as it is commonly known, 3D (three-dimensional) printing, is a rapidly growing sector of manufacturing that is incorporated in automotive, aerospace, biomedical, and many other fields. This work explores the impact of the Additive Manufacturing process on the mechanical proprieties of the fabricated part. To conduct this study, the 3D printed tensile specimens are designed according to the ASTM D638 standards and printed from a digital template file using the FDM 3D printer Raise3D N2. The material chosen for this 3D printing parameter optimization is Polylactic acid (PLA). The FDM process parameters that were studied in this work are the infill pattern, the infill density, and the infill cell orientation. These factors’ effects on the tensile behavior of printed parts were analyzed by the design of experiments method, using the statistical software MINITAB2020.

Computer-aided design (CAD) has become an integral part of engineering education, particularly for those studying mechanical engineering. By providing practical skills that are highly valued in the engineering industry, proficiency in CAD systems enhances students’ employability. Generally, CAD systems provide students with the tools and knowledge necessary to excel in their engineering education and future careers. In order to help teachers to give the best training to their students and to make the right evaluations, an automatized tool is needed to support the evaluation of CAD models during training sessions. After an extensive bibliographical search, this paper proposes a CAD Model Automatized Assessment (MAA) Tool for mechanical courses called the CAD MAA Tool. This tool is mainly based on a developed model that takes into account different aspects of modeling, such as geometric, feature-based, and parametric modeling. To correctly evaluate a given part compared to a reference one, the proposed model uses different coefficients fixed by the teacher according to their teaching strategies or course objectives.

This study presents a foundational step in a broader initiative aimed at leveraging thermal imaging technology to enhance wind turbine maintenance, particularly focusing on the challenges of detecting defects and object localization in small wind turbine blades. Serving as a preliminary experiment, this research project tested methodologies and technologies on a smaller scale before advancing to more complex applications involving large, operational wind turbines using drone-mounted cameras. Utilizing thermal cameras suitable for both handheld and drone use, alongside advanced image processing applications, we navigated the significant challenge of acquiring high-quality thermal images to detect small defects. This required a concentrated analysis of a select subset of data and a methodological shift towards object detection and localization using the You Only Look Once (YOLO) model versions 8 and 9. This effort not only paves the way for applying these techniques to larger-scale turbines but also contributes to the ongoing development of an integrated maintenance strategy in the wind energy sector. Highlighting the critical impact of environmental conditions on thermal imaging, our research underscores the importance of continued exploration in this field, especially in enhancing object localization techniques for the future drone-based maintenance of operational wind turbine blades (WTBs).

Wind turbine inspections are traditionally performed by certified rope teams, a manual process that poses safety risks to personnel and leads to operational downtime, resulting in revenue loss. To address some of these challenges, this study explores the use of deep learning and drones for automated inspections. Three Mask R-CNN models, leveraging different convolutional neural network (CNN) backbones—VGG19, Xception, and ResNet-50—were constructed and trained on a novel dataset of 3000 RGB images (size 300 × 300 pixels) annotated with defects, including cracks, holes, and edge erosion. To improve defect detection performance, a multi-variable fuzzy (MVF) voting system is proposed. This method demonstrated superior accuracy compared to the individual models. The best-performing standalone model, Mask R-CNN with Xception, achieved an mAP of 77.48%, while the MVF system achieved an mAP of 80.10%. These findings highlight the effectiveness of combining fuzzy voting systems with Mask R-CNN models for defect detection on wind turbine blades, offering a safer and more efficient alternative to traditional inspection methods.