University Park, PA, United States of America

Jan Petrich

USPTO Granted Patents = 1 

Average Co-Inventor Count = 9.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Jan Petrich: Innovator in Additive Manufacturing

Introduction

Jan Petrich is a notable inventor based in University Park, PA (US). He has made significant contributions to the field of additive manufacturing, particularly in the area of process monitoring. His innovative work focuses on enhancing the quality and reliability of parts produced through advanced manufacturing techniques.

Latest Patents

Jan Petrich holds a patent for an invention titled "In-situ process monitoring for powder bed fusion additive manufacturing (PBF AM) processes using multi-modal sensor fusion machine learning." This patent involves a method for monitoring the additive manufacturing process in real-time. The process captures computed tomography (CT) scans of parts being built and utilizes a neural network (NN) to analyze multi-modal sensor data. By employing spatial and temporal registration techniques, the data is aligned to specific coordinates on the build plate. The NN is trained to identify defects by correlating sensor data with defect labels, allowing for predictive analysis of potential defects during the manufacturing process. This innovative approach aims to improve the overall quality of parts produced through additive manufacturing.

Career Highlights

Jan Petrich is affiliated with the Penn State Research Foundation, where he continues to advance research in additive manufacturing technologies. His work has garnered attention for its practical applications in improving manufacturing processes and outcomes.

Collaborations

Jan has collaborated with esteemed colleagues, including Edward W. Reutzel and Abdalla R. Nassar, contributing to a dynamic research environment focused on innovation in manufacturing.

Conclusion

Jan Petrich's contributions to the field of additive manufacturing through his innovative patent demonstrate his commitment to advancing technology and improving manufacturing processes. His work is a testament to the potential of machine learning in enhancing the quality of produced parts.

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