Product design and engineering

Purpose: Reverse Shoulder Arthroplasty (RSA) is widely used to treat shoulder joint pathologies. However, this procedure may result in reduced range of motion (ROM), scapular notching, and prosthetic instability. These complications vary among patients, highlighting the need for individualized preoperative planning. This study introduces a novel parametric methodology to determine optimal glenoid component positioning by evaluating ROM, instability ratio, and the percentage of bone resected. Method: The proposed approach was applied to four patient models treated with two prosthetic designs. The methodology consists of four steps within a patient-specific parametric tool: 3D anatomical reconstruction, virtual surgical planning, biomechanical and geometric evaluation, and identification of optimal configurations. Fifteen glenoid component orientations were generated by varying tilt angles. The best configurations were identified based on ROM and instability assessments, while bone resection volume was calculated as an additional parameter. Results: Maximum values of abduction–adduction, internal rotation, and external rotation were 87.23°, 90°, and 70.59°, respectively, although not achieved in a single configuration. Instability ratios ranged from 0.23 to 0.62. Bone resection varied between 0.4% and 5.5%, depending on the configuration. Conclusions: This methodology provides a patient-specific framework to support preoperative planning in RSA.

Keywords: CAD-based planning , Glenoid positioning , Joint stability , Patient-specific modeling , Range of motion , Reverse shoulder arthroplasty

This explorative study investigates the integration of artificial intelligence (AI) and composite-based additive manufacturing (AM) in the design and fabrication of yacht components. Traditional manufacturing methods in the nautical sector impose significant constraints on customization and sustainability. By leveraging AI-powered text-to-3D tools and fused deposition modelling (FDM) with carbon fiber-reinforced polymers, this work demonstrates the feasibility of generating and manufacturing structurally simple yet functionally relevant yacht components. The design pipeline involved iterative prompt engineering, AI-assisted 3D model generation, and physical prototyping. Model generation required multiple attempts and human-in-the-loop selection to achieve functional realism and printability. The printed parts exhibited overall good visual and structural quality, indicating a promising pathway for fast, sustainable prototyping. Although limited to three relatively simple case studies, the results validate the potential of AI-AM workflows to overcome traditional manufacturing constraints. Future developments will focus on expanding components complexity and dimension, incorporating mechanical testing to assess structural viability, and refining AI-generated geometries for improved functional fidelity. This study supports a paradigm shift in yacht design, highlighting AI and AM as enablers of flexible, efficient, and sustainable product innovation.

Keywords: Additive Manufacturing , Artificial Intelligence , Composite Materials , Fused Deposition Modelling , Yacht Design

Additive Manufacturing (AM) is no longer limited to prototyping, as it is gaining an increasingly large market share. With this expansion, there is a growing demand for the development of standardized rules to establish product specifications and verification procedures. AM offers unique opportunities in free-form shape design and multi-material processing, in contrast to traditional manufacturing processes and communication methods. Because of AM's unique features, existing standards struggle to describe its product specifications. New regulations are therefore under development This paper reviews the state of the art in Additive Manufacturing (AM) product specification. This research examines the most up-to-date literature and standards published by the ISO, and ASME committees. A research gap has been identified in the accurate determination of design intent, which is crucial for defining product specifications. “Profile tolerance” is recognized as the most versatile specification for free-form geometries; however, new tools can be developed to control lattice structures. Additionally, the authors identified a significant gap in the assessment of dimensional and geometrical deviations in AM processes, as these are often estimated using geometrical benchmarks designed for subtractive technologies. Finally, verification remains one of the most critical aspects of AM products. Computed Tomography (CT) currently represents the only viable approach for measuring inaccessible features; however, standardized reconstruction methods are still lacking, as is a specification method tailored specifically for this measurement technique.

Keywords: Additive Manufacturing , DfAM , Dimensional and Geometrical Specification , Product Specification

The growing need for sustainable mobility is driving the search for solutions to reduce energy waste and harmful environmental emissions. In this context, the design of vehicle lighting presents a promising approach. The use of composite materials, along with the automation of their manufacturing and assembly processes, is increasingly in demand to strike a balance between cost, lightweight, and the required performance of vehicle chassis. Carbon Fiber Sheet Molding Compounds allow complex car body parts to be produced through a one-step compression molding process. Manual labor represents a crucial operation in preparing the compound charge and subsequent manipulation towards compression molding. However, it directly affects productivity. The continuity between the preparation and manipulation phases can be relaxed by implementing robotic solutions to handle the pre-shaped uncured composite charge, automating loading onto the press mold. Efficient robotic handling requires grippers designed to match the shape of the compound charge. Specifically, the gripper must control the deformation of the temporary form of the composite charges, ensuring that the shape deviations stay within acceptable limits for proper fit in the compression mold. This work presents a systematic approach to designing and optimizing robotic grippers to pick up and place deformable compound layers. The key design parameter that guides this method is the maximum distortion threshold, which regulates the process in the two stages. The first stage focuses on finding the optimal balance between the lowest grasping points and acceptable distortion to define the grasping layout. In contrast, the second stage addresses the gripper structure, aiming to create the lightest, yet stiffest frame. The approach is fully integrated into a computer-assisted design platform.

Keywords: CF-SMC , Integrated design , Limp uncured composite geometries , PLM Platform , Robotic Grippers

Directed Energy Deposition (DED) is increasingly utilized for the construction of large components, repair of worn and damaged parts, and integration into Hybrid Manufacturing systems to leverage diverse technological features. A burgeoning application of DED is in Additive Remanufacturing (ARem), by which existing components can be enhanced with additional features and materials to improve functionalities. This paper presents a systematic and integrated approach for Design for Additive Remanufacturing (DARem), focusing on the optimization of product structural requirements to enhance performance and the process design to minimize flaws while ensuring material integrity and expected tolerances. The suggested approach involves product and process design phases within a computer aided design platform. Identification of critical issues in the original product design is the fundamental phase that drives the selection of the appropriate materials and quantities for deposition. Next, topological optimization is employed to shape and position additional volumes, generating enhanced design variants. A simulation phase ends the product design steps, assessing the actual improvement of the component over its original design. Subsequent phases are related to process design assessment. Selection of process parameters and build strategies, and next, the behavioral simulation of deposition are fundamental to verify feasibility and generate paths instructions. Finally, thermomechanical simulation is conducted to estimate the final component state accurately, ensuring it meets functional requirements before proceeding to actual manufacturing pre-processing, material deposition, and post-processing phases. A case study involving an automotive suspension component is used to demonstrate the feasibility of the proposed integrated approach. Experimental phases are performed to define DED material properties for the redesign and investigate the reliable simulation of process-induced distortions. By reducing stress in critical areas through localized deposition of 316L stainless steel alloy using laser powder DED, the study explores the impact of toolpath strategies on process-induced distortions through a Design of Experiments (DoE) approach. The results confirm the feasibility of the deposition process in meeting functional requirements, particularly geometrical tolerances, highlighting the potential of DARem for the redesign and enhancement of existing components.

Keywords: 316L, Remanufacturing , Directed Energy Deposition , Finite Element Method , Product-Process design

The integration of automation into the Compression Molding process for mass production of automotive components using Carbon Fiber Sheet Molding Compound (CF-SMC) represents a significant improvement in the sector. It improves efficiency, precision, and safety while reducing production costs and allowing product customization. A review of current technologies highlights the limitations of manual lay-up and the potential of automated pick-and-place systems to transfer uncured charges from the preform stage to the mold. Needle grippers have emerged as the most suitable solution to minimize the retention time of composite material in the heated mold, reducing the risk of premature curing. This study proposes a validation model to analyze the needle insertion process, which includes the piercing, detachment, transfer, and release phases, with the goal of developing a numerical model capable of accurately replicating real physical behavior. Focusing on the piercing phase, the analytical model identifies key design parameters that correlate the insertion force, the tilt angle, and the geometry of the needle and the CF–SMC. Subsequently, a hybrid FEM–SPH numerical model, known for its suitability to simulate large-deformation problems, was then implemented in Abaqus.

Keywords: Carbon Fiber Sheet Molding Compound (CF-SMC) , Compression molding , Integrated design , Robotic Grippers

This study aims to gather a sustainable understanding of additive manufacturing and other Manufacturing 4.0 approaches like horizontal and vertical integration and cloud computing techniques with a focus on industrial applications. The DfAM will apply 4.0 tools to gather product feasibility and execution, with CAE—FEM analysis and CAM. This publication focuses on the redesign of a vehicle suspension arm. The main objective is to apply innovative design techniques that optimize component performance while minimizing cost and time. The IDeS method and TRIZ methodology were used, resulting in a composite element, aiming to make the FDM-sourced process a viable option, with a weight reduction of more than 80%, with less material consumption and, hence, less vehicle energy consumption. The part obtained is holistically sustainable as it was obtained by reducing the overall labor used and material/scrap generated, and the IDES data sharing minimized rework and optimized the overall production time.

Keywords: additive manufacturing , IDeS , product design , product reverse engineering , TRIZ

In today's fast-paced design landscape, innovation is not just about generating new ideas, it's about choosing the right problems to solve. Traditional product development often struggles to balance customer expectations, technical feasibility, and time constraints, resulting in scattered efforts and suboptimal solutions. This paper introduces an integrated methodology that combines Quality Function Deployment (QFD), the Kano model, and TRIZ to streamline the front end of innovation. The process begins with QFD to capture and translate customer needs into measurable design parameters. To avoid diluting design focus, the Kano model is then used to classify and prioritize these parameters based on their actual impact on user satisfaction. Finally, TRIZ is strategically applied to address only the most critical and innovation-driven features, those with the potential to deliver real competitive advantage. The approach is validated through the conceptual design of a sport-oriented in-ear headphone, a product where ergonomic, emotional, and functional qualities converge. By guiding the design team through a focused and evidence-based process, the method fosters both creativity and efficiency. The results demonstrate how this integration supports more targeted innovation, minimizes unnecessary iteration, and aligns technical decisions with what truly matters to end users.

Keywords: Customer-Centered Design , Innovation Management , Product Design Methodology , Quality Function Deployment , TRIZ

In response to the growing demand for sustainable solutions in the food and beverage industry, this study explores alternative technologies to replace conventional steam boilers in aseptic filling machines during cleaning and sterilization cycles. The research applies a structured methodology rooted in engineering design principles at the conceptual design stage. A functional analysis using a black-box model maps thermal energy flows, followed by heuristic-based module identification to isolate key subsystems reliant on thermal energy. A comprehensive solution generation process, integrating brainstorming, patent searches, artificial intelligence tools, and TRIZ methodology, yields diverse technological alternatives. These alternatives are evaluated using three distinct metrics: Technology Readiness Level (TRL), Sustainability Readiness Level (SRL), and Technology Applicability Level (TAL). Results identify electrical resistance and heat pump systems as the most promising solutions, offering high efficiency and readiness for industrial implementation. While limitations exist in heuristic subjectivity and early exclusion of emerging technologies, the approach demonstrates robust potential for guiding sustainable innovation in industrial equipment. The study contributes a replicable framework for technology scouting in conceptual design and informs future steps in embodiment, detail design, and real-world validation.

Keywords: Concept generation , Conceptual design , Engineering design , Food machinery

This study presents a method for estimating product disassembly times using machine learning techniques. It demonstrates how to integrate the machine learning model with economic evaluations, circularity indices, and environmental impacts. The goal is to support sustainable design and optimize product end-of-life processes, enabling more informed decisions within the circular economy. The predictive model will be trained on experimental data to accurately estimate disassembly times, considering variables such as the condition of mechanical joints. A preliminary exploratory case study validated the characteristics and experimental dataset required for data collection through a screw rusting test. The results show a significant correlation between joint characteristics, their end-of-life conditions, disassembly configuration, and total disassembly time. Further developments are planned to expand the experimental database and refine the model, extending its application to different products and industrial contexts.

Keywords: Circularity , Disassembly , Environmental sustainability

In an era where aviation systems face mounting complexity and heightened performance demands, traditional design methods often underperform in capturing the full spectrum of stakeholders' value. This paper introduces the Value Enhancement Methodology, a novel and structured framework aimed at embedding value-driven decision-making into the development of complex systems. The methodology was applied to evaluate a ground anti-collision system within the OPTIMATE project, with a focus on airline companies as key stakeholders. Using operational data from U.S. and Australian databases, the number of mitigable taxi-phase events was quantified. Findings indicate that the safety-related benefits are limited, especially in relation to the significant investment and procedural adaptation required for implementation. Conversely, other values, such as Profitability, Operability, and Sustainability may present more persuasive incentives for adoption. These results emphasize the importance of a multi-dimensional, stakeholder-centered evaluation approach when assessing technological innovations in aviation, particularly within an increasingly constrained and competitive industry landscape.

Keywords: Aeronautics , Design methodology , Product development process (PDP) , Systems engineering (SE) , Value driven design (VDD)

The human-centric approach of Industry 5.0 underscores the integration of advanced technologies and information systems to enhance worker well-being, productivity, and safety. Significant progress has been made in automation and digitalization; however, the high prevalence of work-related musculoskeletal disorders (WRMSDs) remains a critical challenge, translating into a significant socioeconomic burden. Nevertheless, industrial practice still predominantly relies on observational ergonomic assessment methods and reactive ergonomic strategies, creating an urgent need for flexible, proactive, individualized, and easy-to-implement risk mitigation approaches. This paper addresses this gap by proposing an intelligent decision-support system based on a multi-objective optimization model that integrates heterogeneous information - workers' anthropometric measures, task requirements, and personal habits - within an expert-system architecture for industrial applications. The method relies on a convex integer program that can be embedded in machine controllers to compute the relative position between the product and the operator, minimizing ergonomic risks. Key innovations include the adoption of ergonomic principles without complex inverse kinematics, the explicit involvement of workers to account for their preferences, and the joint consideration of tasks involving both visual and physical interaction with the product. Experimental validation was conducted in a virtual environment simulating typical manufacturing scenarios, with diverse users and products. Results showed significant reductions in ergonomic risks with optimized positions, especially for smaller products, whereas larger ones posed challenges due to their size and task distribution. Statistical analyses validated these findings, highlighting the model's potential to reduce the REBA (Rapid Entire Body Assessment) risk index and enhance operational efficiency. Overall, the proposed system provides actionable set-points for workstation configuration and practical guidance for implementation, thus supporting human-centric manufacturing in Industry 5.0.

Keywords: Ergonomics , Intelligent decision-support systems , Manufacturing , Mathematical programming , Multi-objective optimization , Work-related musculoskeletal disorders

The rapid growth of waste from electrical and electronic equipment (WEEE) poses significant environmental challenges. Efficient management of WEEE is critical for minimizing environmental harm and recovering valuable secondary raw materials. This study compares two alternatives end-of-life (EoL) modeling approaches in a Life Cycle Assessment (LCA) study to identify the applicability and consequences on results related to Circular Footprint Formula (CFF) implementation. The CFF explicitly incorporates circular economy principles by accounting for recycling, reuse, and energy recovery in a standardized way. Results demonstrate how the composition of the product, applying CFF approach, can lead to a substantial reduction in environmental impacts compared to the traditional EoL modeling methods in LCA. Sensitivity analysis highlights the importance of key CFF parameters, such as recycling rates and allocation factors in influencing results. The study underscores the relevance of adopting advanced modeling methods like CFF to enhance the accuracy and transparency of environmental assessments for WEEE, supporting sustainable product design and policy development.

Keywords: environmental impact , LCA , PEF , WEEE

In the early stages of product design, considering environmental impacts throughout a product's life cycle is essential to support sustainable industrial development. Traditional Life Cycle Assessment (LCA) methods, while comprehensive and systematic, are often limited in the conceptual design phase due to the lack of detailed information and the time required for modeling. Parametric LCA approaches attempt to bridge this gap but still demand extensive data and expertise. To address these challenges, this study proposes a methodology based on Machine Learning (ML) techniques, enabling the implementation of a Surrogate LCA for preliminary environmental evaluation during early design. ML models can learn from existing LCA datasets to predict environmental impacts using limited design parameters, providing rapid and informed feedback to designers. This approach transforms LCA into a proactive design aid, capable of handling data uncertainty and dynamically adapting to design variations. The study demonstrates the methodology through a case study, showing how product geometry influences prediction accuracy and, consequently, the uncertainty of environmental impact estimations. Results highlight the potential of ML-driven LCA tools to enhance early-stage design decisions, supporting the transition toward circular economy principles and environmentally responsible product development.

Keywords: Conceptual design , Environmental sustainability , Life Cycle Assessment , Machine learning , Manufacturing

The growing focus on environmental sustainability has driven industries to adopt Life Cycle Assessment (LCA) to quantify environmental impacts. However, in the furniture sector, challenges arise due to the emphasis on craftsmanship, quality materials, and customization, making complex assessments difficult for small and medium enterprises. This research presents an integrated eco-design framework tailored to the furniture sector, which utilizes machine learning algorithms to support sustainable decision-making from early design stages. The framework introduces three key tools: a Qualitative Feedback Tool for gathering stakeholder input, an Eco-Design Tool for developing guidelines through machine learning, and a Simplified LCA Tool for conducting rapid environmental assessments. A case study with a leading furniture company validates the approach, focusing on the development of a sustainable seating archetype. Results show that the framework enhances decision-making efficiency, embeds sustainability early in the design process, and reduces costly late-stage modifications. By merging machine learning with eco-design, the study provides a structured and accessible path to sustainable product development, emphasizing the importance of integrating sustainability as a core design principle rather than an external constraint. This methodology offers valuable support for companies aligning with environmental regulations and meeting the growing demand for sustainable products.

Keywords: Eco-design , Furniture , Life Cycle Assessment

The maritime industry is looking for ways to decarbonize rapidly through design strategies, and technologies, also developing solutions for eco-friendly boating. The hydrofoil technology is one of the most promising due to its high efficiency compared to traditional planing or displacing vessels. The research was focused on the development of a systematic design method for fully submerged hydrofoil wing system with a longitudinal flight control. Starting from a reference hull model, five alternative wing configurations were designed based on geometric, hydrodynamic, and dynamic criteria. A preliminary hydrodynamic model was developed to determine the dimensions and positions of the wing systems in five different configurations. The equations of the motion were numerically implemented, and a longitudinal double PID flight control for the wing systems was developed. The configurations were designed benefiting from a parametric CAD model and simulated through transient CFD analysis at different speeds. In terms of drag, the lowest values were found for R4 (62 N) and R3 (64 N) during cruise. R1 and R0.75 were the best configurations in terms of following the flight pattern. Furthermore, at the same speed and displacement condition, the drag estimated for R4 was more than halved, if compared to the original model (130 N).

Keywords: Computational fluid dynamics , Double PID flight control system , Hydrofoil , Measurements , Parametric design and modelling

In the field of boating, the design of eco-sustainable solutions has been recently focused on hydrofoils. Such technology is employed in small and large vessels. A great challenge may be represented by the reconstruction of geometric details in objects with large sizes through traditional approaches based on simple and manual instrumentations and measuring systems. With regard to reverse engineering techniques, photogrammetry may be suitable for reconstructing devices with large sizes. The aim of the work was to report and provide an analysis on the last progresses related to the feasibility of the photogrammetric approach in the hydrofoil research, summarising a specific methodology reported in the literature to acquire and reconstruct the outer sector of the front wing of a Kolkhida-class hydrofoil. The potential of computational fluid dynamics was also stressed in the evaluation of the characteristic curves, focusing on drag and lift coefficients as well as on the error due to the reconstruction through photogrammetry. Data analysis and research generally require the possibility of sifting through a great amount of information, thus providing a very overwhelming and time-consuming approach. In this scenario, the focused summarising approach may play an important role in providing a simplification of the complex content and technical features into digestible insights. This means that the proposed summarising approach should be able to boost retention providing a help in remembering key features more effectively through their condensation to a reduced number of information and steps.

Keywords: computational fluid dynamics , Design methods , hydrofoil , measurements , reverse engineering

Traditional design reviews often rely on static inspection of 3D CAD models and physical prototyping, approaches that are typically time-consuming, costly, and limited in interactivity. While Virtual Reality (VR) offers a promising, immersive, and cost-effective alternative, its adoption in industry remains limited due to the limitations of commercial solutions and their poor integration with CAD environments. To overcome these challenges, this paper introduces a flexible, VR-based framework aimed at enabling an automated scene setup and immersive design reviews for early-stage assembly validation, before physical prototyping begins without the constraints of proprietary platforms. The framework is composed of two interconnected applications: (1) Model Setup Application, which facilitates the import and conversion of CAD models, along with the definition of assembly sequences enriched with technical and safety alerts, and (2) Immersive Design Review Application, which provides an interactive VR environment for navigating assemblies, validating procedures, and annotating key steps. This approach supports collaborative, virtual exploration of assembly processes, enhancing early design communication, reducing development lead times, and improving knowledge retention. The framework is compatible with consumer-grade VR hardware and supports standalone operation, making it a scalable and accessible solution.

Keywords: Assembly Validation , Immersive Design Review , Virtual Assembly , Virtual Reality

In this paper, we propose a novel, simple and effective design and prototyping method to realize pre-shaped soft growing robots (SGRs) enabled by material patching. By attaching two elastic and differently pre-tensioned fabric sheets, a tubular robot body segment is prototyped to realize a pre-determined constant-curvature shape. Multiple segments are then assembled sequentially to create SGRs that can grow along complex continuous shapes. We present the working principle, the equivalent kinematic model, a task-specific design tool for patched SGRs, and the prototyping method. Then, we evaluate the patched SGR capabilities, and compare them with respect to the most widespread pre-steered SGRs' fabrication method, known as pinching. The evaluation is performed in terms of amount of utilized body material (15.3% reduction), mechanical properties (up to 7.3 times stiffer) and capability to carry tip-mounted payloads. Finally, we deployed a single-segment pre-shaped prototype in two different case studies, showing the capability of a single-patched SGR to handle remote visual inspection and cable-delivery in known constrained environments, achieving 81.7% and 68.

Keywords: Bio-inspiration , Embodied intelligence , Pneumatic-driven , Pre-shaped , Soft robotics