Bullen Ultrasonics, a company specializing in precision machining of advanced ceramics, glass, and specialty materials using proprietary ultrasonic and laser-based technologies, has received a $23,100 grant through the Ohio Smart Manufacturing Program to support its Autonomous Process Optimization initiative, which applies AI and machine learning to improve manufacturing operations. Bullen is working with Phenx, an industrial AI engineering company, on the project, with the two companies analyzing roughly 2 billion data points collected from Bullen's customized manufacturing equipment to identify ways to improve process stability, efficiency, and consistency.
Tim Beatty, president of Bullen Ultrasonics, said the company has spent several years investing in the infrastructure needed to capture, organize, and understand the data its equipment generates, believing it would eventually support better manufacturing decisions. He said this grant allows the company to turn that foundation into something actionable, applying AI to a specific manufacturing challenge with the potential to improve the value it delivers to customers.
The project began with an analysis of Bullen's historical production data. After encouraging early findings, Phenx built a digital twin, meaning a virtual model, of the manufacturing process being targeted for improvement. That digital twin lets Bullen and Phenx test different algorithms and process adjustments against real production data before making any changes on the actual factory floor. The current phase of the project is focused on validating the optimization algorithm within that digital twin; once that's complete, Bullen plans to pilot the algorithm on one of its physical machines and evaluate how it performs over an extended testing period.
Saurabh Sarkar, founder and CEO of Phenx, said Bullen had already done much of the difficult groundwork needed for a successful industrial AI project, including collecting high-quality data and developing deep knowledge of its custom machines and processes. He said combining that foundation with advanced modeling and machine learning makes it possible to identify patterns that would be extremely difficult to spot manually, and to safely test optimization strategies in a digital environment before introducing them into actual production.
If successful, the project could help Bullen reduce variation in its manufacturing processes, shorten production cycles, and improve how predictable and consistent its operations are. It could also serve as a model for applying similar AI-driven modeling and optimization techniques to other manufacturing processes down the line.
The grant funds the project's initial phases, with Bullen investing additional resources of its own into continued development, hardware integration, production testing, and longer-term validation.
The Ohio Smart Manufacturing Program is designed to help small and medium-sized manufacturers adopt advanced digital technologies to improve operational efficiency, productivity, and competitiveness. It's supported by the U.S. Department of Energy's State Manufacturing Leadership Program and administered in Ohio by the Ohio Department of Development. The University of Dayton Research Institute supported Bullen's participation by conducting technical discovery work and helping prepare the project for state approval.
Mark McCormick, senior business development lead at the University of Dayton Research Institute, described the institute as a key subrecipient supporting the Ohio Smart Manufacturing Program, providing technical project assessment and financial support to help companies like Bullen adopt digital technologies more quickly. He said Bullen's initiative is a strong example of how manufacturers can apply AI and machine learning to a clearly defined operational challenge with the potential to improve manufacturing performance and deliver measurable business results.
Bullen frames the initiative as part of a broader strategy of combining the expertise of its engineers, machinists, and manufacturing staff with emerging technology, with the goal of using AI to support human decision-making, speed up problem-solving, and expand its precision manufacturing capabilities.
