Adaptive Manufacturing via Real-Time Failure Detection and Response

This work presents a conceptual framework for adaptive manufacturing based on real-time failure detection and response. Rather than treating failures during fabrication as defects to be eliminated, the proposed approach reframes them as dynamic informational signals that can guide adaptive process control within a closed-loop manufacturing system. In conventional defect-zero manufacturing paradigms, increasing precision often leads to heightened sensitivity to localized disturbances, stress concentration, and unpredictable failure modes. The framework introduced here addresses this limitation by continuously monitoring failure-related signals during fabrication and adjusting process parameters in response to the evolving structural state. Importantly, this framework does not rely on predictive defect placement, exhaustive failure modeling, or static optimization. Artificial intelligence, where employed, functions strictly as a decision-support component within the control loop. This work is released under the Creative Commons Zero (CC0) license to prevent restrictive patent enclosure of broadly applicable manufacturing concepts and to encourage open experimentation and responsible industrial integration. License Statement:This work is released into the public domain under the Creative Commons Zero (CC0) license.It is made freely available to ensure unrestricted use, modification, and implementation.This work is released to prevent restrictive patent enclosure of broadly applicable manufacturing concepts, and to encourage open experimentation, validation, and responsible integration across academic, industrial, and AI-driven manufacturing communities. Notes:ChatGPT (OpenAI) and Gemini (Google) are listed to acknowledge their roles as AI-assisted research partners in exploratory dialogue, structuring, and refinement of the manuscript. All conceptual decisions, interpretations, and responsibility for this work remain solely with the human author.