Generative Design and Integrated 3D Printing Manufacture of Cross Joints

Abstract

The integrated process of design and fabrication is invariably of particular interest and important to improve the quality and reduce the production cycle for structural joints, which are key components for connecting members and transferring loads in structural systems. In this work, using the generative design method, a pioneering idea was successfully realized to attain a reasonable configuration of the cross joints, which was then consecutively manufactured using 3D printing technology. Firstly, the initial model and generation conditions of a cross joint were constructed by the machine learning-based generative design algorithm, and hundreds of models were automatically generated. Then, based on the design objective and cost index of the cross joint, three representative joints were selected for further numerical analysis to verify the advantages of generative design. Finally, 3D printing was utilized to produce generative joints; the influences of printing parameters on the quality of 3D printing are further discussed in this paper. The results show that the cross joints from the generative design method have varied and innovative configurations and the best static behaviors. 3D printing technology can enhance the accuracy of cross joint fabrication. It is viable to utilize the integrated process of generative design and 3D printing to design and manufacture cross joints.

A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis

“…Deep learning applications for computer-aided design (CAD) and computeraided engineering (CAE), crucial processes in new product creation, have garnered attention recently. Studies [31], [49], [52], [60] integrating deep learning with CAD/CAE is more practical than other engineering domains because CAD data has been used in many deep learning research for tasks like segmentation and classification [46], and the development of metamodels in CAE research has long depended on machine learning [20]. During the conceptual design stage, a metamodel or surrogate model is essential for quickly evaluating the engineering performance of multiple design contenders.…”

Utilizing Generative Design Algorithms for Innovative Structural Engineering Solutions

“…It gives a foundation that explains the research problem. The conceptual foundations of the design of generative algorithms have a solid foundation in the fundamental principles of computational architecture and optimization [23]. This cross-disciplinary strategy includes concepts and methodologies from the fields of artificial intelligence (AI), machine learning (ML), and evolutionary computing.…”

Investigating and Characterizing the Systemic Variability When Using Generative Design for Additive Manufacturing

“…Wang et al in 2021 and Han et al in 2022 used Autodesk's GD tool to explore alternative avenues for structural joint design [21,22]. Both groups used the increased design freedoms afforded by AM to realize GD outcomes that minimized mass and reduced maximum displacement and principal stresses.…”

“…This investigation acknowledges the variation possible between solvers; however, it again ignores study setup conditions. Despite all the studies discussed in this sub-section citing the use of AM as the most appropriate choice for producing GD parts, only Junk and Rothe, Wang et al, and Han et al followed up by using AM to realize their designs [2,21,22]. Of these, only Junk and Rothe then mechanically tested the part to determine whether the design performed well post-production.…”