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Abstract Democratized mechanical testing offers a promising solution for enabling the widespread adoption of recycled and renewably sourced feedstocks. Locally sourced, sustainable materials often exhibit variable mechanical properties, which limit their large-scale use due to tight manufacturing specifications. Wider access to mechanical testing at the local level can address this challenge by collecting data on the variable properties of sustainable feedstocks, allowing for the development of appropriate, uncertainty-aware mechanics frameworks. These frameworks are essential for designing custom manufacturing approaches that accommodate variable local feedstocks, while ensuring product quality and reliability through post-manufacturing testing. However, traditional mechanical testing apparatuses are too costly and complex for widespread local use by individuals or small, community-based facilities. Despite promising efforts over the past decade to develop more affordable and versatile testing hardware, significant limitations remain in their reliability, adaptability, and ease–of-use. Recent advances in artificial intelligence (AI) present an opportunity to overcome these limitations by reducing human intervention, enhancing instrument reliability, and facilitating data interpretation. AI can thus enable the creation of low-cost, user-friendly mechanical testing infrastructure. Future efforts to democratize mechanical testing are expected to be closely linked with advancements in manufacturing and materials mechanics. This perspective paper highlights the need to embrace AI advancements to facilitate local production from sustainable feedstocks and enhance the development of decentralized, low-/zero-waste supply chains. 

A high temperature multibeam-optical-stress sensor (HTMOSS) was used to characterize the coefficient of thermal expansion (CTE) and yield stress of 1-micron thick Lipon films.