Design for Additive Manufacturing (DfAM) is an essential method of product design and development process. The product design analysis, including improved production, is carried out according to the key parameters of Additive Manufacturing (AM), such as layer thickness, build orientation, creation of support and slicing planning. The production parameters, together with the AM method and the type of material, significantly influence product quality through surface finish and strength. The paper presents an approach that helps users define layer thickness by evaluating the design with several attributes, where both strength and surface roughness are criteria of interest for improving product quality. Weighted features, DfAM knowledge and a multi-attribute decision support tool, are used to recommend the most desirable layer thickness. Additive powder samples were used in the case study to show how the proposed approach contributes to product quality.

This paper describes the analysis of steel reel shaft as drive shaft in a process of umbilical spooling of wire rope. There is a need to investigate shaft behaviour in the conditions of increasing workload. The aim is to investigate...

The paper explores enhancing the mechanical behavior of stochastic lattice structures through a semi-controlled design approach. By leveraging the Gibson-Ashby model and predefined geometric routines, the study aims to optimize the mechanical response of lattice structures under compressive stress. Transitioning from stochastic to semi-controlled tessellation using Rhinoceros 7 software enables more predictable deformation behavior. Design parameters such as node formation, strut thickness, and lattice generation patterns are correlated with relative density to regulate stiffness and strength. Experimental validation using Acrylic Styrene Acrylonitrile (ASA) filament demonstrates the effectiveness of the proposed design model. The research emphasizes the importance of understanding internal mechanics by introducing a novel design approach to control geometry and topology arrangement in shaping lattice properties. By introducing a semi-controlled mechanism, the study seeks to improve the reliability and uniformity of mechanical responses in lattice structures. The findings highlighted the benefits of semi-controlled design approaches in achieving tailored mechanical properties. Specimens were compression tested in quasi-static uniaxial loading and showed that structures created with parabolic distribution dimensioned by ${\mathrm{h}}_{\mathrm{p}}=0.5{\mathrm{h}}_{\mathrm{v}}$ originated the most reliable and most vital mechanical response compared with other design models, including typical Voronoi distribution. The improved mechanical response in between proposed design models constantly progressed by about 15% on average consecutively, starting from the parabolic distribution dimensioned by ${\mathrm{h}}_{\mathrm{p}}=1.0{\mathrm{h}}_{\mathrm{v}}$ as the weakest ranked, up to the best one, dimensioned by ${\mathrm{h}}_{\mathrm{p}}=0.5{\mathrm{h}}_{\mathrm{v}}$, even better than the typical Voronoi distribution. The proposed design model has introduced an entirely novel approach that significantly enhances the product’s volume tessellation using routines that guarantee the validity of geometric and topologic entities. Uniaxial compression tests on lattice blocks highlighted the effect of the proposed approach on the mechanical properties of these structures, having shown particularly crucial repeatability and stability. Overall, the paper contributes to advancing the field of lightweight lattice structures through the novel design methodology and material characterization.

Lifting table has been designed and developed through the concept of Learning Factory (LF) at the University of Mostar. The idea for lifting table design has come from the local industry needs for a lifting platform that should lift a man and/or load at a certain height. For safety reasons, design is checked under the loading using a method of finite element analysis. The paper predicts and explains methodology for structural analysis used in presented case study. Results of FEM analysis are basis for making ways and guidelines to optimize current design in order to get optimal parameters for weight, stability, capacity, mobility and layout of the lifting table.