Applying Deep Learning and Generative AI in US Industrial Manufacturing: Fast-Tracking Prototyping, Managing Export Controls, and Enhancing IP Strategy

Abstract

The work examines how Deep Learning (DL) and Generative Artificial Intelligence (GenAI) can be strategically incorporated into the industrial manufacturing of the US to accelerate the process of product prototyping and improve levels of compliance with export control regulations and intellectual property (IP) strategy. With the manufacturing sector swiftly adopting the concept of digital transformation within the Industry 4.0 framework, the concepts of DL and GenAI technologies are reshaping the old forms of work processes, including automating the design-iteration process, cutting the production latency, and improving the process of innovation management. Nonetheless, their fast usage creates new issues with export-controlled technologies and ownership of IP among the outputs of an algorithm. The current paper utilizes a mixed-method design integrating model simulations based on data, case study, and policy framework analysis. Results indicate that GenAI-based prototyping has the potential to cut the design cycle by up to 40 percent while ensuring regulatory compliance by the incorporation of embedded model governance. Moreover, predictive maintenance accuracy can be increased with the help of DL, and patentable innovations can be facilitated using automated differentiation in the design. The paper also establishes new gaps in the policies regarding dual-use AI applications. It prescribes a systemized framework for synchronizing AI innovation with export control compliance and IP protection policies. The findings can be helpful to policy makers, industrial executives, and R&D strategists who want to use generative and deep learning systems responsibly in the US manufacturing environment.