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This study aims to investigate the prediction of the nonlinear response of three-dimensional-printed polymeric lattice structures with and without structural defects. Unlike metallic structures, the deformation behavior of polymeric components is difficult to quantify through the classical numerical analysis approach as a result of their nonlinear behavior under mechanical loads.

Geometric models of periodic lattice structures were designed via PTC Creo. Imperfections in the form of missing unit cells are introduced in the replica of the lattice structure. The perfect and imperfect lattice structures have the same dimensions – 10 mm × 14 mm × 30 mm (w × h × L). The fused deposition modelling technique is used to fabricate the parts. The fabricated parts were subjected to physical compression tests to provide a measure of their transverse compressibility resistance. The ensuing nonlinear response from the experimental tests is deployed to develop a support vector machine surrogate model.

Results from the surrogate model’s performance, in terms of correlation coefficient, rose to as high as 99.91% for the nonlinear compressive stress with a minimum achieved being 98.51% across the four datasets used. In the case of deflection response, the model accuracy rose to as high as 99.74% while the minimum achieved is 98.56% across the four datasets used.

The developed model facilitates the prediction of the quasi-static response of the structures in the absence and presence of defects without the need for repeated physical experiments. The structure investigated is designed for target applications in hierarchical polymer packaging, and the methodology presents a cost-saving method for data-driven constitutive modelling of polymeric parts.

Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.

As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.

The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.