: Since the advent of Industry 4.0, the manufacturing sector has had to face new challenges, which require the development of new skills and innovative tools. This scenario includes innovative production processes such as additive manufacturing (AM), a technology capable of producing a component layer-by-layer directly from the 3D model without needing specific tools during the building phase. Generative design (GD) may represent an opportunity to maximise the potential of AM techniques. GD is based on parametric computer-aided design (CAD) tools capable of generating multiple optimised outputs, among which the designer could select the most promising solution. This paper presents a general methodology for evaluating the GD outputs in the conceptual phase of design to select the best possible solution through a series of criteria at several levels. The evaluation method is deployed in an aerospace field case study. The procedure demonstrates the benefits of synergising GD with AM in the early stages of product development. This indicates that the developed methodology could reduce the number of iterations during the design process, and the result is a decrease in the overall time spent on the project, avoiding problems during the final stages of the design.

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.

Directed energy deposition-laser beam/powder (DED-LB/Powder) is an additive manufacturing process that is gaining popularity in the manufacturing industry due to its numerous advantages, particularly in repairing operations. However, its application is often limited to case studies due to some critical issues that need to be addressed, such as the degree of internal porosity. This paper investigates the effect of the most relevant process parameters of the DED-LB/Powder process on the level and distribution of porosity. Results indicate that, among the process parameters examined, porosity is less affected by travel speed and more influenced by powder mass flow rate and laser power. Additionally, a three-dimensional finite element transient model was introduced, which was able to predict the development and location of lack-of-fusion pores along the building direction.

Laser-Powder Directed Energy Deposition (DED-LB/Powder) is an additive manufacturing process that is gaining popularity in the manufacturing industry due to its numerous advantages, particularly in repairing operations. However, its application is often limited to case studies due to some critical issues that need to be addressed, such as the degree of internal porosity. This paper investigates the effect of the most relevant process parameters of the DED-LB/Powder process on the level and distribution of porosity. Results indicate that, among the process parameters examined, porosity is less affected by travel speed and more influenced by powder mass flow rate and laser power. Additionally, a three-dimensional finite element transient model was introduced, which was able to predict the development and location of lack-of-fusion pores along the building direction.

Laser Beam Directed Energy Deposition (DED-LB) is mainly used for coating and remanufacturing applications. Moreover, this technology can also be used for the production of functional components, although its industrial adoption in this context remains limited. Ensuring the quality of manufactured components is a complex task unless destructive tests are performed, which do not allow the certification of 100% of the components. To overcome this limitation, this study introduces a novel approach centred on the development and experimental validation of two Machine Learning (ML) models capable of detecting small fluctuations in processing conditions and predicting clad geometry in the DED-LB process. By leveraging monitoring data, these models enable rapid and reliable assessment of process deviations and build quality, facilitating their integration into online defect-detection frameworks where fast and accurate decision-making is essential. The first model, a classification algorithm, is trained on monitoring data and achieved an accuracy of 90% and a precision of 83% for the reference class, with less than 20% false positives. The second model, a regression algorithm, predicts key geometrical features of the deposited clads: width, height, depth, area, and dilution, with an average 92% R2 value. Both models are validated under real working conditions using clads with intentionally induced process deviations, demonstrating their ability to detect anomalies and assess their impact on build geometry with a maximum relative error below 8.9%. These results highlight the potential of ML-based techniques to enable real-time quality assurance in DED-LB manufacturing. Graphical abstract

Since the advent of Industry 4.0, the manufacturing sector has had to face new challenges, which have required the development of new skills and innovative tools. This scenario includes innovative production processes such as Additive Manufacturing (AM), a technology capable of producing a component layer-by-layer directly from the 3D model, without the need of specific tools. Generative Design (GD) may represent an opportunity to maximize the potential of AM techniques. GD is based on parametric computer-aided design (CAD) tools capable of generating multiple optimized outputs, among which the designer could select the most promising solution. This paper presents a general methodology for evaluating the GD outputs in the conceptual phase of design, to select the best possible solution through a series of criteria at several levels. The evaluation method is deployed in an aerospace field case study. The procedure demonstrates the benefits of adopting GD synergistically with AM in the early stages of product development. This indicates that the developed methodology could reduce the number of iterations during the design process, and the result is a decrease in the overall time spent on the project, avoiding problems during the final stages of the design.