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Measurement technology meets the classroom

Measurement technology meets the classroom

News 27.05.2026

Kistler Instrument Corporation, a global leader in dynamic test and measurement technology, and FimmTech, a California-based training organization that specializes in plastics injection molding, have entered a strategic partnership. Through this collaboration, cavity pressure sensors and process monitoring systems from Kistler are integrated directly into FimmTech's injection molding seminars, connecting measurement-based process control with practical, hands-on training.

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Kistler and FimmTech have joined forces to incorporate cavity pressure monitoring into injection molding training, equipping processors with the knowledge and tools necessary to reduce scrap, improve repeatability, and meet the demands of modern manufacturing.

Manufacturers face mounting pressure to reduce scrap, improve repeatability, reduce cost, and address workforce skill gaps – all while meeting increasing requirements for traceability and digitalization. For years, Kistler has provided solutions to address these challenges. Now, with the addition of a training portfolio that combines scientific molding methodology with measurement technology into one cohesive approach, molders can achieve these goals more efficiently.

“We have seen firsthand how processors sometimes struggle when they have the equipment but not enough process knowledge to use it effectively, or when they have the knowledge but lack the tools. This partnership provides both simultaneously for deeper process understanding and effective use of the technologies,” said Suhas Kulkarni, President at FimmTech. Additionally, he continues: “I think cavity pressure technology is a must-have technology, especially when it comes to precision molding, such as in medical products where there are several dimensions that need Cpks upwards of 1.33. Molders struggle with parts where the process windows are very small because of the nature of the part or mold design. The use of cavity pressure technology is the perfect solution here.”

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Real-time process data is captured directly inside the mold by cavity pressure sensors from Kistler, forming the foundation for data-driven quality control.

Measurement technology meets the classroom
The integration of cavity pressure sensors and process monitoring systems from Kistler into FimmTech's training seminars and technical workshops is at the heart of this collaboration. Participants work directly with professional Kistler equipment during hands-on sessions, applying measurement-based process control within the context of scientific molding principles.

“Cavity pressure monitoring has long been proven to reduce scrap and improve process stability, but our goal has always been to make it more than just a tool. We want it to become a fundamental part of how molders think about their process,” said Kim Pfluger, General Manager at Kistler North America. “Through this partnership, we are also strengthening relationships within the plastics processing community, and that matters as much as the technology itself.”

During these practical sessions, attendees observe real-time cavity pressure curves and learn how to interpret them to determine part quality. Using actual data, they practice switchover optimization, where they experience firsthand how small adjustments can affect the entire process and how to identify the optimal transfer point. By analyzing process variation and stability across multiple cycles, attendees gain an understanding of what a controlled, repeatable process looks like in practice. Since traceability is an increasingly critical requirement in modern manufacturing, participants also learn how to capture, store, and structure process data to meet documentation and compliance demands. Kistler also contributes technical knowledge and application insights, ensuring that the measurement data is not only visible but fully interpreted within the context of scientific molding principles.

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Suhas Kulkarni, President of FimmTech, sees the partnership with Kistler as delivering two key benefits: deeper process understanding and the effective use of technology.

A training program built on practical experience
The seminars are designed for anyone involved in the molding process, including process engineers, technicians, tooling engineers, quality personnel, program managers, and production managers. Although the methodology is prevalent in medical device manufacturing, it is applicable across industries wherever consistent, high-quality molded parts are needed.

FimmTech's instructors bring an average of approximately 35 years of hands-on industry experience, and the curriculum covers the full spectrum of injection molding: from scientific molding and experiment design to polymer materials, melt preparation, part and mold design, and quality control. The most popular format is a four-day seminar on the development of robust molding processes.

“Partnering with Kistler allows us to strengthen our commitment to a data-driven approach to process control. Integrating live cavity pressure monitoring into our seminars gives participants a visceral understanding of what is happening inside the mold, and this understanding is transformative. This is how we enable the next generation of molders to shift from reactive troubleshooting to predictive control,” said Kulkarni.

Before this partnership was formed, professionals from more than 100 companies across the U.S., Mexico, Canada, Europe, China, Costa Rica, New Zealand, and India attended FimmTech’s injection molding seminars. “The impact of this training shows up immediately on the shop floor. We regularly hear from attendees who went back to work and solved problems in minutes that used to take them all day. That is exactly what this kind of education should do,” said Kulkarni. Pfluger concludes, “This partnership reflects our shared commitment to driving long-term industry transformation toward data-driven quality assurance. The demand is there, and together, we are ready to meet it.”

All pictures source: Kistler

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