PSI - Issue 68

Shahriar Afkhami et al. / Procedia Structural Integrity 68 (2025) 929–935 S. Afkhami et al. / Structural Integrity Procedia 00 (2025) 000–000

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1. Introduction Additive manufacturing (AM) as a prominent prototyping technology has evolved into a manufacturing method over the past three decades, and it has established its place in the concept of the fourth industrial revolution (Industry 4.0) paradigm: the digitization of manufacturing. This point is clearly reflected in the literature by acknowledging the pivotal role of AM in the practical realization of the fourth industrial revolution [1]. The prominent features of AM that distance this manufacturing technology from other typical fabrication approaches are its ability to manufacture customized components, reduce environmental impact, more sustainable manufacturing, and more simplified supply chain in comparison [2]. Further, the potential of using additive manufacturing with metallic raw materials is vast, enabling the fabrication of complex geometries in industrial metal components with functional structures as a single consolidated unit; this potential surpasses the limitations of traditional methods, where such components were only possible to fabricate as multiple individual parts interconnected with each other as a component system. [2,3]. Subsequently, metal AM has prominent applications in various areas and industries, including healthcare, automotive, aeronautics, and aerospace [1]. However, manufacturing costs of metal components via AM methods, especially laser powder bed fusion (L-PBF) as the most commonly used one for industrial applications, are comparatively high; subsequently, such techniques are reserved for parts where their performance has a significantly higher priority over their production costs [3]. Topology and design optimization is one of the unique applications of metal AM, and it is aimed at establishing a favorable combination of raw material usage and structural layout following a set of predetermined domains. In other words, this outcome can be reached via topology optimization by optimizing a set of objective parameters against a set of constraints. Some of these predefined constraints that must be considered while utilizing topology optimization in conjunction with design for additive manufacturing (DfAM) considerations are, e.g., minimizing the required support structure, avoiding overhanging issues (in general, overhanging angles lower than 45°), and avoiding heat accumulation [1,3]. As an ideal outcome, topology optimization increases the final product’s functionality and efficiency and makes it more sustainable [2,3]. Accordingly, topology optimization via AM can be considered a lightweight strategy to increase the strength-to-weight ratio of components in pursuit of greater efficiencies to consume less energy (in production and practice) for specific applications; this aim is achieved by leveraging the design freedom offered by AM and, of course, topology optimization, as one of its well-known descendants. Further, topology optimization can enhance the efficiency of AM in conducting automated repairs and restorations [1,4]. In summary, possible applications and advantages of topology optimization via AM include the production of integrated designs, individualization of products and components, the possibility of lightweight designs, and the prospect of more efficient components through unified complex designs [5]. Furthermore, topology and design optimization via AM is in strong harmony with the E3 concept (adding economic, ecological, and experience values to the final product) since significant environmental/ecological benefits can be reached, primarily by reducing material waste and usage via AM [5]. However, the optimization outcome is not always AM-friendly, and it is essential to consider limitations associated with the manufacturing procedure while using optimization techniques, especially to improve mechanical performance. In this regard, typical considerations to evaluate for the optimized design are yield, tensile, and fatigue strength values [3]. The high number of software packages capable of topology optimization while considering DfAM constraints points to the significant number of possible applications of design and topology optimization via AM. However, as indicated by Plocher and Panesar [1], most of the literature on design optimization via AM is focused on economically driven incentives and initiatives; hence, a need for more studies in similar fields but focused on performance-related motives is currently sensible. Furthermore, while utilizing AM and optimization via AM, a practical challenge for industrial applications, especially aerospace components, is the certification and standardization of such parts. Consequently, proposed novel designs in this paradigm seem more theoretical than practical; this issue highlights the necessity of experimental tests to practically evaluate the reliability of such novel (or optimized) designs [3]. Accordingly, the current study aims to investigate the influence of design optimization and material selection on the fatigue performance of a bicycle crank arm as an actual component. Three similar optimized designs with minor variations based on their raw material strength range (316L, Al5X1, and Ti64) were subjected to cyclic loads to compare their fatigue performance. Also, defect contents and surface roughness of the optimized designs made of different metals were analyzed to make the comparison more thorough. Finally, fractography analyses