This study examines how different AR platforms support learning and creativity in Additive Manufacturing (AM) education. Design students used either a smartphone- or headset-based AR app to explore virtual AM models before completing a design task and questionnaire. Expert reviews and Mann–Whitney U tests showed that headset AR users reported higher usability, better AM understanding, and produced more creative designs. The results highlight the educational value of immersive AR in enhancing technological comprehension and creative performance.

Additive Manufacturing (AM) enables the local adjustment of material properties using multi-material strategies, especially with Material Extrusion (MEX). Electrically conductive structures like conductors, Joule heating structures, and their transitions can be realised with conductive polymer composites (CPC). However, specific Design for Additive Manufacturing (DfAM) guidelines for the afore mentioned structures are still missing. This work uses experimental data by thermography and the measurement of resistivity to derive twelve design rules. The rules are applied to an application example.

AM enables the design of compliant mechanisms that encode functions directly into geometry. Existing DfAM frameworks rarely address microscale AM, such as two-photon polymerisation (2PP). We present the design process of an airtight, monolithic bellows structure in rigid 2PP resin that serves both as a sensor and an actuator. Through co-evolution of problem and solution, we identify 2PP-specific design considerations and opportunities, including fabrication uncertainties, cross-scale iteration, and design for post-processing, contributing to a case-based DfAM framework for microscale AM.

Thermal history is critical to part performance and reliability in material-extrusion additive manufacturing. Using encoders and an infrared camera, we developed a method to generate thermal clouds, where each node has its distinct spatio-thermal data. Filters removed up to 20.68% of the data while preserving relevant thermal features. This study enables in-situ process monitoring that establishes the basis for part certification, particularly for high-performance polymers, and for predicting material strength from thermal clouds.

Advances in additive manufacturing (AM) enable the use of AM components in demanding complex applications with high functional requirements. As a result, integrating standardized machine elements such as conventional rolling bearings is gaining growing relevance. However, limitations regarding achievable tolerances or surface qualities in the MEX process stand in contrast to strict specifications for bearing integration. This study introduces a novel interface element and a corresponding integration process that considers both bearing requirements and the layered structure of MEX components.

Turbine blades are high-value components whose replacement is costly and slow, increasing the demand for effective repair strategies. Although PBF-LB/M supports precise additive repair, its application is limited by manual and time-intensive part preparation. This work introduces an automated digital workflow for cutting plane definition, repair geometry reconstruction and part alignment, improving reproducibility and reducing preparation time in PBF-LB/M-based turbine blade repair.

Individualisation in military equipment aims to improve performance by aligning design with soldier-specific needs. Existing DfAM methodologies lack structured integration of user variability and iterative evaluation in defence contexts. This study develops an iterative DfAM process linking anthropometric input, additive strategy, constraints, and performance assessment. Demonstrated through a helmet liner case, three iterations addressed geometry, manufacturability, and impact-response behaviour within regulatory limits.

We experimentally characterise the effect of layer temperature on the mechanical properties of PA6-CF manufactured by MEX. Ultimate tensile strength (UTS) and tensile modulus was investigated across layer temperatures ranging from 67 °C to 165 °C. UTS increased from 7.55 MPa at 67 °C to 36.04 MPa at 165°, while tensile modulus increased from 1.6 GPa at 67°C to 4.0 GPa at 140 °C. Measurements on a manufactured component show in-process layer temperatures between 88 °C and 123 °C. These findings quantify the attainable performance window and implications for functional component design.

Additively manufactured particle-damped (AMPD) beam structures produced by PBF-LB/M are tested in the Einstein-Elevator under microgravity conditions. The first bending mode is evaluated by laser Doppler vibrometry and compared with results from microgravity experiments, using power spectral density inputs resynthesised from those runs and replayed on a shaker. Frequency-domain transmissibility with confidence intervals shows stable mode-specific damping behaviour and supports a validated workflow for future space structures.

Residual stress is inherent in Additive Manufacturing process due to the heat cycling of the material being deposited on the build plate. The manufacturing toolpath can have a considerable effect on the development of residual stress distribution within a component. This paper examines the impact of the shell feature to generate some design heuristics on whether to include it when residual stress is of concern. The stress feature did increase the residual stressed observed during printing but was mitigated by the cooling regime after the process was complete.

We present a geometry-based complexity factor for additive manufacturing that estimates relative printing effort of fused filament fabricated parts from STL geometry alone. A reference effort is derived by slicing thousands of parts and volume-equivalent cubes. Eight interpretable geometric metrics feed a constrained, regularised index with monotonic calibration, achieving robust test accuracy and revealing which shape features dominate structural complexity.

This paper examines how ambient airflow, temperature, and humidity impact the print quality of upcycled biomaterials in Direct Ink Writing, and explores strategies for mitigation. A standardized pecan shell flour ink was used with optimized slicing parameters. Experiments in a controlled climate chamber involved sensor logging and statistical analysis. Airflow improved structural stability, overhang fidelity and bridging, but increased Z-axis shrinkage. Higher temperatures slightly improved bridging, while elevated humidity reduced stability and increased sagging, despite small bridging gains.

This paper explores the application of Web3.0 technologies to provide de-centralised secure, private, and provenance preserving trust networks for society’s increasingly digital design and manufacture workflows. It provides an overview of the key technologies involved and an example of a minimal trust framework required for issuing jobs between actors and machines in a makerspace. A comparison with centralised AM farm platforms is made and demonstrates how Web3.0 can support emergent trust structures compared to fixed centrally managed structures that actors need to agree to.

Industry is experiencing rising thermal loads, so geometries that improve energy transfer are needed. However, defects arising from overhang in additive manufacturing affect the functionality of triply periodic minimal surface (TPMS) based heat exchangers. This study addresses how TPMS superposition affects heat transferring and overhang critical surfaces. The objective is to quantify the functional and manufacturing trade-offs, and to identify the optimal hybrid cells formed from gyroid, Schwarz and diamond units.

Industrial adoption of additive manufacturing (AM) remains limited, partly due to challenges in determining when AM is more suitable than conventional processes. Since this decision must be made early to enable effective design for AM, understanding the factors that shape such assessments is essential. This study used iterative need analysis and prototype development loops to investigate these factors. The findings identify key needs and barriers influencing early decisions on when to design for AM and show that effective support requires a deep understanding of the underlying problem.

This study investigates the mechanical performance of PA6-CF and PLA components fabricated with desktop material extrusion additive manufacturing. To define the geometry, low-cost 3D scanning was used in combination with Generative Design in Autodesk Fusion 360. PA6-CF outperformed PLA by 25% in pre-failure peak load (1.85 kN vs. 1.47 kN), despite the datasheet values suggesting a 450% advantage in interlayer strength. Poor interlayer bonding of PA6-CF is attributed to low layer temperatures (87–136 °C) during the printing process, indicating that a chamber temperature of 60 °C is inadequate.

Generative AI and additive manufacturing (AM) are shifting orthotic design from generic devices to data-driven, patient-specific solutions. This paper presents a systematic review of Generative AI in Design for AM (DfAM) for orthotic devices. It examines how AI-driven methods generate customised, lightweight orthoses via 3D printing, improving both design efficiency and anatomical fit. The review identifies biomechanical and workflow challenges that hinder adoption and outlines how Generative AI can advance orthotic DfAM, providing a conceptual workflow and suggestions for future research.

This paper examines whether the empirical knowledge of the TRIZ design theory is suitable for Design for Additive Manufacturing (DfAM). We systematically assess TRIZ engineering parameters (EP) and inventive principles (IP) in the context of contradiction analysis via DfAM, drawing on 11 semi-structured interviews. Findings indicate thematic alignment between DfAM methods and TRIZ IP, but reveal that the original TRIZ engineering parameters inadequately capture the multidimensional design space offered by DfAM. We outline directions to adapt the TRIZ EP for improved applicability.

We present a thermal process-monitoring system for MEX tracking layer temperature as a proxy for interlayer adhesion. Python-based hottest-point tracking by infrared thermography is implemented on a chamber-heated desktop printer to track nozzle movements and measure the temperature field millimeters ahead of deposition logging the results on a CSV file. We quantify accuracy versus camera distance (Δd=73mm) and probe radius (R2-R5). Where R3-R4 provided just a ΔRMSE of 1.52°C suggesting R3 as the optimal distance. The results can inform mechanical properties in load-bearing AM applications.

This work presents an ML-based inverse design framework for multi-material lattices with curved struts, targeting mechanical and thermal performance. Using cubic-spline parameterization and discrete material assignment, the design space expands beyond conventional lattices. A workflow combining a material classifier, property predictor, and inverse generators addresses one-to-many mapping, enabling probabilistic sampling and diverse designs. The approach supports multi-objective trade-offs and lays the foundation for multi-scale optimization of functionally graded metamaterials.