Konstantinos Tzimanis

Sheet Molding Compound (SMC) compression molding enables the cost-efficient manufacturing of lightweight structural components for automotive applications. However, the final part quality is highly sensitive to complex interactions between various manufacturing parameter that are difficult to optimize through trial-and-error. Physics-based Finite Element simulations support process understanding and parameter optimization, but their computational cost and modeling assumptions limit their applicability during production. This work introduces a co-deployed decision-support framework combining a Reduced Order Model (ROM) derived from physics-based simulations with complementary Data-Driven Models (DDMs) trained on real manufacturing measurements. The ROM provides instantaneous predictions of internal process fields, while the DDMs infer quality indicators not reliably obtainable from simulation alone. Both models are integrated into the manufacturing machine’s Process Control Manager and connected to the press via OPC-UA, enabling real-time insight for operators. The methodology is demonstrated on the industrial production of an automotive upper suspension control arm manufactured by MARELLI, yielding reduced design time, lower scrap rates, enhanced quality, and actionable feedback. These results highlight the potential of complementary digital-twin approaches for optimizing composite manufacturing processes.

Multi-material Directed Energy Deposition (DED) Additive Manufacturing (AM) processes enable the integration of different material properties into a single structure, addressing the requirements of various applications and working environments. Laser-based Directed Energy Deposition (DED-LB) has been employed in the past for surface coatings as well as for the repair and repurposing of high-value industrial components, with the goal of extending product lifetime without relying on expensive and time-consuming manufacturing from scratch. While powder DED-LB has traditionally been used for multi-material AM, the more resource-efficient and cost-effective wire DED-LB process is now being explored as a solution for creating hybrid materials. This work focuses on the critical aspects of implementing multi-material DED-LB, specifically defining an optimal operating process window that ensures the best quality and performance of the final parts. By investigating the possibility of combining stainless steel and nickel-based alloys, this study seeks to unlock new possibilities for the repair and optimization of naval components, ultimately improving operational efficiency and reducing downtime for critical naval equipment. The analysis of the experimental results has revealed strong compatibility of stainless steel 316 with Inconel 718 and stainless steel 17-4PH, while the gray cast iron forms acceptable fusion only with stainless steel 17-4PH. Finally, during the experimental phase, substrate pre-heating and process monitoring with thermocouples will be employed to manage and assess heat distribution in the working area, ensuring defect-free material joining.

The wire Laser-based Directed Energy Deposition (DED-LB) metal additive manufacturing (AM) process is time- and cost-effective, providing high-quality, dense parts while supporting multi-scale manufacturing, repair, and repurposing services. However, its ability to consistently produce parts of uniform quality depends on process stability, which can be achieved through monitoring and controlling key process phenomena, such as heat accumulation and variations in the distance between the deposition head and the working surface (standoff distance). Part quality is closely linked to achieving predictable melt pool dimensions and stable thermal conditions, which in turn influence the end-part’s cross-sectional stability, overall dimensions, and mechanical properties. This work presents a workflow that correlates process and metrology data, enabling the determination of tunable process parameters and their operating process window. The process data are acquired using a vision-based monitoring system and a load-cell embedded in the deposition head, which together detect variations in melt pool area and standoff distance during the process, while metrology devices assess the part quality. Finally, this monitoring setup and its ability to capture the complete process history are fundamental for developing in-line control strategies, enabling optimized, supervision-free, and repeatable processes.

Industries are leveraging the wire laser-beam-directed energy deposition (DED-LB) additive manufacturing (AM) process to manufacture and repair high-quality, defect-free, and cost-effective parts. However, expensive, non-easily accessible, and complex metrology equipment is needed to quantify part-related performance metrics such as cross-sectional dimensional accuracy and intrinsic defects. This information is necessary for establishing the operating process window and for the quality characterization of the part. Therefore, this work presents a methodology that combines information captured from a vision-based monitoring system with the output of Computed Tomography (CT) towards the knowledge generation and process optimization of wire DED-LB. The design of experiments as well as the interpretation of the results are achieved by employing Nested ANOVA where the dependency of cross-sectional stability on the laser power parameter is demonstrated, enabling, at the same time, the understanding of unstructured datasets where multiple parameters vary at different levels. Finally, this work can be the pillar for adopting new production and part requirements while also giving directions about the effect of control strategies on the part quality.

Laser-Based Powder Bed Fusion is one of the most widely used additive manufacturing processes, mainly due to its high-quality output. End users would greatly benefit from a virtual simulation of the process; however, the modelling of the process is very complicated and slow and therefore restricted mainly to academic users. In this work, a computationally efficient approach to the thermal modelling of PBF-LB/M is presented. This approach is based on the enthalpy method and the division of the simulation into three characteristic scales of the process. Despite the small runtime of the simulations, the model captures the critical phenomena of the process achieving sufficient accuracy.

The first and most important target of the industrial world is to offer products that appeal to customers’ demands (affordable prices), while at the same time, respect the global effort of CO₂ emissions reductions, which is required under strict emissions rules. There is, however, an apparent contradiction between the two concepts; productivity and sustainability, leading to two strategies—innovation economy and circular economy, respectively. To this end, this work aims, through modeling the long-term environmental impact of producing new goods in short terms (innovation economy) and impact of rebuying, repairing and reusing products for extended use (circular economy), to study the relationship between the two economies. For this purpose, the terms of innovation and circular economy are introduced and described, in order to define the environmental impact during the lifecycle of a product. Two products are assessed for this study—a well-known, medium price vehicle, as well as an expensive mobile phone with several generations. The cost of purchase and the recurring costs are used as indicators of environmental impact, instead of calculating the impact directly for the production phase, due to the enormous size of the production data that are desired. The results, despite being indicative of the modelling complexity, can still be used to pave the way towards a modelling framework, proving, at the same time, that innovation and circular economy are not contradictive concepts.

Vision technologies are used in both industrial and smart city applications in order to provide advanced value products due to embedded self-monitoring and assessment services. In addition, for the full utilization of the obtained data, deep learning is now suggested for use. To this end, the current work presents the implementation of image recognition techniques alongside the original the quality assessment of a Parabolic Trough Collector (PTC) reflector surface to locate and identify surface irregularities by classifying images as either acceptable or non-acceptable. The method consists of a three-step solution that promotes an affordable implementation in a relatively small time period. More specifically, a 3D Computer Aided Design (CAD) of the PTC was used for the pre-training of neural networks, while an aluminum reflector surface was used to verify algorithm performance. The results are promising, as this method proved applicable in cases where the actual part was manufactured in small batches or under the concept of customized manufacturing. Consequently, the algorithm is capable of being trained with a limited number of data.