Iñigo Flores Ituarte

Additive manufacturing of multimaterial metallic components enables functional grading and design flexibility; however, heterogeneous microstructures, residual stresses, and interfacial regions often limit mechanical integrity and tribological performance. Wire Arc Additive Manufacturing (WAAM), a directed energy deposition technique, further introduces microstructural anisotropy and surface roughness, making post-processing essential for wear-critical applications. This study investigates the combined effects of post-deposition heat treatment (annealing, normalising) and ultrasonic nanocrystal surface modification (UNSM) on the microstructure, surface integrity, and tribological behaviour of WAAM-fabricated ER70S-6/ER100S-G bimetallic steel. Microstructural characterisation, hardness profiling, residual stress and roughness measurement and reciprocating dry sliding wear tests were conducted across as-built, annealed, and normalised conditions with and without UNSM treatment. UNSM increased near-surface hardness and induced compressive residual stress in all conditions. However, its effect on surface topography was strongly condition-dependent: roughness (Sa) decreased by approximately 42% and 22% after UNSM on annealed and normalised material, respectively, yet increased by approximately 141% on as-built material. Despite this divergent topographical response, UNSM reduced the specific wear rate in every condition by 48.7% (as-built), 46.5% (annealed), and 33.6% (normalised), with the annealed-plus-UNSM condition giving the lowest overall wear rate. Worn-surface and subsurface analyses revealed mixed adhesive, abrasive, and oxidative wear with tribo-film formation, and UNSM suppressed plastic deformation and adhesive damage without fully eliminating abrasive or crack-assisted wear. The results show that bulk heat treatment and UNSM act at complementary length scales and that their combined application is an effective strategy for tailoring the wear performance of WAAM bimetallic steel, even where the two treatments produce opposing effects on surface roughness alone.

Wire-arc directed energy deposition (WA-DED) is the subject of extensive research in metal additive manufacturing (AM). This study investigates the influence of ultrasonic vibration (UV) on the material properties of deposited ER2209 duplex stainless steel filler wire and evaluates the effects of UV treatment on walls fabricated both with and without weaving. The results demonstrate the effectiveness of a specially developed UV table prototype, showing clear impacts on grain size and ferrite content in the deposited samples. UV treatment reduces the primary ferrite grain width and enhances phase distribution homogeneity, potentially influencing the ferrite-to-austenite transformation during successive reheating cycles. Although the effects on surface morphology and hardness were minimal, significant microstructural changes occurred within the deposited material. UV-induced grain refinement modifies the austenite content, revealing a beneficial interaction between vibration and phase evolution. The UV table design provides a valuable foundation for reproducibility; nevertheless, further optimization of the setup is required to improve performance and support its integration into advanced industrial applications.

The next generation of computer-aided intelligent manufacturing systems must enable the exploration and exploitation of cause-and-effect relationships across multiple disciplines. This capability strengthens human decision-making and supports sustainability-by-design in digital design-to-manufacturing workflows. To enhance system intelligence, seamless integration is needed between material systems, design methods, manufacturing processes, and sustainability metrics. This study presents a case study on large-scale mold manufacturing using large area additive manufacturing. A multidisciplinary design optimization (MDO) framework combines parametric and generative design strategies with manufacturing process planning, material selection, and environmental impact analysis. The study examines the trade-offs between structural integrity, production efficiency, and ecological impact, focusing on two different short fiber-reinforced polymer materials. Empirical and model-driven analyses methods reveal a direct correlation between mass reduction and improved sustainability. While carbon fiber reinforcement offers better structural performance, it also increases the carbon and water footprints by approximately 400% and 100%, respectively, compared to glass fiber alternatives. The case study on wind turbine rotor blade mold manufacturing highlights how parametric and generative design approaches can produce both structurally sound and sustainable solutions. Future research should focus on improving the algorithmic transparency of commercial software, increased flexibility to add manufacturability constraints, and potentially including sustainability models to enhance the intelligence in design-to-manufacturing workflows. This study highlights the potential of intelligent manufacturing systems to drive cleaner, more efficient, and sustainable production processes.

As a key technology in the field of Artificial Intelligence (AI), large language models (LLMs) have gained substantial attention due to their wide range of capabilities in understanding and generating human-like speech. These advancements extend opportunities beyond text generation offering solutions for more complex engineering tasks. Despite extensive exploration of LLM capabilities in multiple fields, there is a noticeable lack of comprehensive studies within the engineering sector. This study aims to address this gap by investigating the potential of LLMs in solving engineering design problems. This study presents a framework for LLM-assisted CAD that employs advanced artificial intelligence systems, specifically GPT-4o model, to provide users with the ability to generate and manipulate 3D models through text commands, visual inputs such as blueprints, images, voice, and any other type of general or professional information about the desired model. By integrating LLMs with CAD software and incorporating image recognition and voice detection capabilities, we aim to lower the barrier to entry for 3D modeling, making it more user-friendly and accessible to non-professionals. The proposed framework employs LLM to process user inputs (text, images, and/or voice) and generate the code corresponding the CAD model. A closed feedback loop ensures that the generated models are refined iteratively until they meet the user’s specifications. This research demonstrates the potential of generative AI-assisted design (GAD) in simplifying complex design processes and providing valuable insights into areas where LLM enhanced processes can assist design to manufacturing workflows.

Duplex stainless steels (DSS) in wire and arc additive manufacturing (WAAM) have attracted significant research attention due to their mechanical properties and corrosion resistance. This study uses conventional and nanomechanical testing methods to compare the mechanical and microstructural behaviors at macroscopic and microscopic length scales. Macro hardness (HV10) testing yielded 259 and 249 in low and high heat input (HI) samples, respectively, while ferrite content averaged 52.7 and 48.5%. However, these results fail to provide conclusive insight into the potential influence of microstructural variations at the macroscopic level, likely due to the composite response of the material. To overcome this limitation, the mechanical response of the DSS samples is assessed at the grain level via high throughput nanoindentation mapping with image processing to track the location of each indent. This approach enabled differentiating the indents landing on ferrite and austenite phases as well as those landing on the interfaces. The results showed that the austenite phase had higher hardness (4.30 and 4.35 GPa) than the ferrite phase (3.89 GPa and 4.03 GPa) for high and low HI samples, respectively. The observed differences in hardness between the phases can be attributed to higher nitrogen content in the austenitic phase.

An intelligent manufacturing paradigm requires material systems, manufacturing systems, and design engineering to be better connected. Surrogate models are used to couple product-design choices with manufacturing process variables and material systems, hence, to connect and capture knowledge and embed intelligence in the system. Later, optimisation-driven design provides the ability to enhance the human cognitive abilities in decision-making in complex systems. This research proposes a multidisciplinary design optimisation problem to explore and exploit the interactions between different engineering disciplines using a socket prosthetic device as a case study. The originality of this research is in the conceptualisation of a computer-aided expert system capable of exploring process–structure–property–performance linkages in digital manufacturing. Thus, trade-off exploration and optimisation are enabled of competing objectives, including prosthetic socket mass, manufacturing time, and performance-tailored socket stiffness for patient comfort. The material system is modelled by experimental characterisation—the manufacturing time by computer simulations, and the product-design subsystem is simulated using a finite element analysis (FEA) surrogate model. We used polynomial surface response-based surrogate models and a Bayesian Network for design space exploration at the embodiment design stage. Next, at detail design, a gradient descent algorithm-based optimisation exploits the results using desirability functions to isolate Pareto non-dominated solutions. This work demonstrates how advanced engineering design synthesis methods can enhance designers’ cognitive ability to explore and exploit multiple disciplines concurrently and improve overall system performance, thus paving the way for the next generation of computer systems with highly intertwined material, digital design and manufacturing workflows. Graphical abstract