The project represents a shift from raw data collection to actionable engineering. By creating a virtual replica of its shop floor, Bullen can now stress-test algorithmic adjustments against historical production records without risking hardware or material waste. Tim Beatty, president of Bullen Ultrasonics, noted that the company spent years building the necessary data infrastructure to reach this point. The goal is to move beyond manual observation, using AI to detect subtle patterns in machine behavior that would otherwise remain invisible to human operators.
Saurabh Sarkar, CEO of Phenx, emphasized that the strength of the initiative lies in combining deep domain expertise with advanced modeling. The current phase involves validating these optimization algorithms within the digital twin environment. If these simulations hold up, the company plans to transition the technology to live hardware for an extended pilot period. This effort is supported by the University of Dayton Research Institute, which provided the technical assessment required to secure state funding. Success here could yield shorter production cycles and higher consistency across Bullen’s specialty work in ceramics and glass.

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