Publications

The 2020s represent both the digital decade and the pivotal period in the fulfillment of long-standing commitments made by public, private, and institutional actors in favor of sustainable development. In the manufacturing context, Product Lifecycle Management (PLM) systems are used during the design phase to reduce product environmental footprint. However, only a few studies have thoroughly identified the environmental impacts associated with these technological solutions. This study proposes a sensitivity analysis of five environmental impact categories associated with two PLM system architectures and three mitigation scenarios. To this end, we use an engineering school as a representative PLM system case study, relying on the Life Cycle Assessment (LCA) methodology and leveraging specialized tools that enable the execution and comparative analysis of multiple LCA scenarios. Our results consistently identify the manufacturing and usage phases of PLM system users’ equipment as the main contributors of the PLM system to climate change, acidification, and the depletion of abiotic mineral and metal resources. End-of-life contributes significantly to particulate matter impact, and usage phase, in a nuclear mix country, to ionizing radiation. The policy of purchasing and reselling reconditioned users’ equipment is clearly identified as a key lever for reducing the magnitude of these five environmental impacts.

Patient-specific models have increasingly gained significance in medical and research domains. In the context of hemodynamic studies, computational fluid dynamics emerges as a highly innovative and promising approach. We propose to augment these computational studies with cell-based experiments in individualized artery geometries using personalized scaffolds and vascular cell experiments. Previous research has demonstrated that the development of Sickle Cell Disease (SCD)-Related Vasculopathy is dependent on personal geometries and flow characteristics of the carotid artery. This fact leaves conventional animal experiments unsuitable for gaining patient-specific insights into cellular signaling, as they cannot replicate the personalized geometry. These personalized dynamics of cellular signaling may further impact disease progression, yet remains unclear. This paper presents a six-step methodology for creating personalized large artery scaffolds, focusing on high-precision models that yield biologically interpretable patient-specific results. The methodology outlines the creation of personalized large artery models via Additive Manufacturing suitably for supporting cell culture and other cellular experiments. Additionally, it discusses how different Computer-Aided-Design (CAD) construction modes can be used to obtain high-precision personalized models, while simplifying model reconfigurations and facilitating adjustments to general designs such as system connections to bioreactors, fluidic systems and visualization tools. A proposal for quality control measures to ensure geometric congruence for biological relevance of the results is added. This innovative, interdisciplinary approach appears promising for gaining patient-specific insights into pathophysiology, highlighting the importance of personalized medicine for understanding complex diseases,

Mental workload overload is a major cause of human error in industrial tasks such as maintenance. Human errors can compromise not only system safety but also lead to high social and economic costs, reduce equipment productivity, and cause incidents, accidents, and fatalities. To this day, we do not have an adequate assessment of mental workload in maintenance, which would help design maintenance processes more effectively by incorporating this crucial aspect. The objective of this study is to determine the ability of our indicators to measure mental workload during a disassembly and assembly task in a laboratory condition. Thirty-six participants performed a disassembly and assembly task under two different mental workload conditions. Subjective measures (NASA-TLX), performance metrics (number of errors), and cardiovascular data (heart rate, heart rate variability, and breathing rate) were analyzed. We observed a higher number of errors and elevated NASA-TLX scores in the high mental workload condition. Regarding cardiovascular data, interesting trends in the temporal domain were observed despite mostly non-significant results. Although conducted in a laboratory, this multimodal mental workload measurement method is promising for diagnosing and understanding operators' cognitive behavior, and deserves validation in real-world maintenance conditions.

In Robotized Incremental Sheet Forming (ISF), achieving precise geometrical accuracy is a challenging task due to trajectory tool center point (TCP) position errors at the forming tool attached to the robot’s end-effector. These errors primarily arise from external disturbance forces and torques generated during the interaction between the forming tool and the elastic metal sheet. While jointtorque space controllers can mitigate reaction forces and torques through dynamic modeling, jointspace control has inherent limitations, particularly for industrial high-load robots like the ABB IRB 8700. To overcome these challenges, thiswork implements an external force/torque (F/T) compensator in task-space using a deep neural network. The network predicts trajectory errors induced by reaction forces and torques measured via a 6-axis F/T sensor. Additionally, the forming tool’s trajectory is precisely monitored using a laser tracker, which serves as a feedback mechanism in a closed-loop task-space error-tracking controller. This controller detects and corrects trajectory deviations in real time. By integrating the F/T compensator and the task-space error-tracking controller, the proposed approach effectively compensates for reaction forces and torques while addressing additional errors introduced by other process-related factors. This integration results in significantly enhanced accuracy in robotic incremental forming processes.

Purpose of the research Prevention of spine disorders and their management require better understanding of related biomechanical issues. While tremendous progress has been performed for musculoskeletal modelling of the spine, subject specific modelling of the gravitational loads and their effects on the spine is still an issue. Recently, 3D reconstruction of the skeleton from biplanar head to feet X-rays in erect position has been completed by the external body envelope. An approach named “barycentremetry” based on density models to estimate the mass and centre of mass of each body segment, yielding a force plate less estimation of the gravity line, together with the estimation of the gravitational loads and the associated lever arm at each vertebral level. Principal results Due to vertebral pose, gravitational loads effect on intervertebral disc shows wide variation. Studies exploring barycentremetry clinical relevance were analysed, particularly for adolescent idiopathic scoliosis, adult spinal deformities and osteoporosis. They progressively yield a better comprehension of the potential vicious circles linking postural disorder to increase of spine loads to increase of postural disorder. Barycentremetry was also explored within gait and motion analysis research, allowing to estimate subject specific body segments inertial parameters for patient specific dynamic analysis. Indeed, 3D musculoskeletal modelling of posture and motion could benefit from subject specific dynamic analysis based on barycentremetry. Major conclusions Such approaches progressively provide a better understanding of the stability of this complex system and compensation strategies that could be useful for early detection of disorders that are responsible of a biomechanical cascade

Background In professional rugby, injury prevention and player availability are major challenges. Sports analytics use data from trainings and matches to address these issues. This study leveraged comprehensive daily data from a professional rugby club to predict players' readiness for training. Using this metric helped assess its effectiveness in predicting intrinsic injuries and improving injury prevention strategies. Methods Models including logistic regression, decision trees, and Long Short-Term Memory-based neural networks, were evaluated for their predictive accuracy and ability to discern patterns indicative of injury risks or readiness for physical activities. Findings The study demonstrated that long-short term memory and convolutional one-dimension models outperform traditional machine learning methods in analyzing players' physical conditions. This approach may support earlier identification of injury risks and inform workload management. Using model evaluation and interpretability techniques, including Local Interpretable Model-Agnostic Explanations (LIME) module, the study provided a framework for sports scientists, coaches, and medical staff to mitigate injury risks and optimize training sessions. Interpretation As a preliminary exploration, this study paves the way for further research into the integration of machine learning and neural networks in sports science, promising transformative impacts on injury prevention strategies in rugby.

Rugby is a sport with a high injury rate. Much has been done to make the sport safer, particularly in terms of limiting and identifying concussions. Recently, instrumented mouthguards have been developed and used to measure events that may lead to concussion. However, these instrumented mouthguards may not have an appropriate geometry regarding shock absorption and comfort. In addition, there is no specific international standard for instrumented mouthguards. This study proposed a geometric analysis of both instrumented and non-instrumented mouthguards. Ten instrumented mouthguards were analysed and compared with three non-instrumented mouthguards. They were inspected visually, with a 3D envelope scan and with a CT scan. The results showed that the mouthguards did not comply with recommendations such as indentation with the lower teeth which may increase injury or fracture risk.

Terahertz (THz) imaging is gaining attention as a nondestructive testing technique for assessing damage due to its high axial resolution and nonionizing nature, presenting a promising alternative to conventional methods such as ultrasound and X-ray imaging. Its practical implementation, however, remains limited by the reliance on expert interpretation and the frequent need for validation using supplementary techniques such as X-ray microcomputed tomography (µCT), particularly for complex damage modes. This study focuses on woven-fabric-reinforced thermoplastic composites subjected to low-velocity impact, which typically causes barely visible impact damage (BVID). The damage is subtle yet critical, potentially leading to failure under subsequent loading. The multilayered and spatially distributed characteristics of BVID make it especially challenging to identify. To overcome these challenges, this work integrates deep learning with pulsed THz time-of-flight tomography (TOFT) imaging to enable automated damage detection in composite laminates. In contrast to existing research that mainly targets delamination using A- or C-scan data, this study emphasizes the detection of low-velocity impact damage by leveraging THz B-scans, which offer nondestructive depth-resolved cross-sectional imaging. The training dataset is labeled by correlating THz TOFT scans with X-ray CT images used as ground truth. A transfer learning approach, based on convolutional neural network (CNN) architectures, is employed for binary classification to distinguish damaged from undamaged regions. The resulting classifier achieves over 95 % accuracy, demonstrating the viability of this method for industrial applications such as quality assurance and in-service inspection of composite structures.

Advancements in artificial intelligence (AI) have ushered in the era of the fourth industrial revolution, transforming workplace dynamics with AI's enhanced decision-making capabilities. While AI has been shown to reduce worker mental workload, improve performance, and enhance physical safety, it also has the potential to negatively impact psychosocial factors, such as work meaningfulness, worker autonomy, and motivation, among others. These factors are crucial as they impact employee retention, well-being, and organizational performance. Yet, the impact of automating decision-making aspects of work on the psychosocial dimension of human-AI interaction remains largely unknown due to the lack of empirical evidence. To address this gap, our study conducted an experiment with 102 participants in a laboratory designed to replicate a manufacturing line. We manipulated the level of AI decision support—characterized by the AI's decision-making control—to observe its effects on worker psychosocial factors through a blend of perceptual, physiological, and observational measures. Our aim was to discern the differential impacts of fully versus partially automated AI decision support on workers' perceptions of job meaningfulness, autonomy, competence, motivation, engagement, and performance on an error-detection task. The results of this study suggest the presence of a critical boundary in automation for psychosocial factors, demonstrating that while some automation of decision selection can nurture work meaningfulness, worker autonomy, competence, self-determined motivation, and engagement, there is a pivotal point beyond which these benefits can decline. Thus, balancing AI assistance with human control is vital to protect psychosocial well‑being. Practically, industry and operations managers should keep employees involved in decision making by adopting partial, confirm‑or‑override AI systems that sustain motivation and engagement, boosting retention and productivity.

Augmented Reality (AR), a technology that overlays digital content onto the physical environment, holds promise for enhancing creativity and usability in product design education. However, despite the advantages of Additive Manufacturing (AM) in enabling complex and customizable designs, designers often struggle to grasp its abstract principles. Grounded in theories of immersive learning and multimodal visualization, this study investigates whether integrating AR visualization can facilitate better understanding and stimulate creativity in AM education. A controlled experiment was conducted with 34 master's students in product design, randomly assigned to either an AR-based learning group or a traditional card-based learning group. Participants engaged with AM principles through either an interactive AR application featuring manipulable 3D cube models or static information cards. Usability perceptions and creativity of design outputs were assessed respectively through structured questionnaires and expert evaluations by five domain specialists. Mann–Whitney U tests, appropriate for non-normally distributed data, revealed that the AR group reported significantly higher usability ratings and produced more original design outcomes compared to the card-based group. These findings demonstrate that AR-based educational tools can directly improve the usability and creative engagement of students in learning AM principles. This study contributes to advancing the understanding of how immersive technologies can be effectively integrated into design education to foster both practical skills and innovative thinking.