Recent advancements in implicit 3D representations and generative models have markedly propelled the field of 3D object generation forward. However, it remains a significant challenge to accurately model geometries with defined sharp features under parametric controls, which is crucial in fields like industrial design and manufacturing. To bridge this gap, we introduce a framework that employs Large Language Models (LLMs) to generate text-driven 3D shapes, manipulating 3D software via program synthesis. We present 3D-PreMise, a dataset specifically tailored for 3D parametric modeling of industrial shapes, designed to explore state-of-the-art LLMs within our proposed pipeline. Our work reveals effective generation strategies and delves into the self-correction capabilities of LLMs using a visual interface. Our work highlights both the potential and limitations of LLMs in 3D parametric modeling for industrial applications.

As part of the 36th AAAI Conference on Artificial Intelligence (AAAI2022), 39 different workshops were held, covering a wide range of different AI topics. We hear from the organisers of the workshops on AI-Based Design and Manufacturing, and Graphs and more Complex structures for Learning and Reasoning, who provide a summary of their events. The first AI for Design and Manufacturing (ADAM) Workshop, conducted virtually as part of AAAI-22, was organized in order to bring together world experts in core AI, scientific computing, geometric modeling, design, and manufacturing. The primary objectives were to outline the major research challenges in this rapidly growing sub-field of AI; cross-pollinate collaborations between AI researchers and domain experts in engineering design and manufacturing; and sketch open problems of common interest. This one-day workshop consisted of two plenary talks, four keynote talks, and twenty-four lightning talks by authors of accepted papers.

Democratizing innovation means that tools for users and consumers to engage in product design for customization are available and accessible [1]. Democratizing innovation can also lead to entirely new paradigms of expanding the typical profile of a manufacturer to also include those who operate micro-factories, leading to the prospect of having customized products built anywhere and anytime. Computing technology has created orders of magnitude efficiency in the product life cycle but the skills required to design products have been largely confined to those skilled in the art and science of design and making of things. If barriers to lowering skills needed to engage in product design are reduced, an increased expansion of the innovation ability of the consumer base will emerge [2-5]. Products can be designed by anyone and not necessarily limited to those skilled in engineering and industrial design.