Laser Institute of America (LIA) Honors SenSigma with the Prestigious William M. Steen Award at ICALEO

SenSigma was awarded the William M. Steen Award at LIA’s 2021 ICALEO Conference for its innovative developments in Laser Material Processing using their patented Smart Optical Monitoring System (SOMS).

WILLIAM M. STEEN AWARD

The William M. Steen Award is given to companies who develop systems and ideas that benefit the laser manufacturing industry.

The Smart Optical Monitoring System (SOMS), when installed on a welding robot, observes in real time the plasma generated during a welding or material deposition operation to create precision welds and high quality 3D Metal parts with minimal defects. By performing a multi-spectrum analysis, it identifies and categorizes different types of defects, and then essentially “teaches” the host welding equipment to avoid those defects and create better welds or material depositions with the desired composition and phase transformation in-situ.

The Innovation of SOMS is the foundational concept and patent that SenSigma uses for research, new patents in additive manufacturing, and have recently commercialized the product to be used by companies all over the world and can be applied to a multiple processes in laser manufacturing such as 3D Metallic Printing (Additive Manufacturing), Welding/Joining, Laser Cladding and Alloying, and Laser Cutting of Metals and Plastics.

SenSigma has been awarded multiple grants in the furthering research of laser manufacturing using SOMS and have began commercializing the technology by selling the product globally. SOMS benefits manufacturers as it utilizes both monitoring and control in laser manufacturing to certify as you build to decrease defects in-situ to maintain manufacturing quality creating high quality 3D metal parts and precision welds and additionally save companies monetary losses in material wastage, loss of valuable labor time, capital resources, excess scrap, poor long term product quality, and liability of defective components. By using its patented spectroscopy and machine learning process, SOMS can better understand and classify defects as a whole by analyzing its plasma and chemistry which vision, thermal, acoustic and photo-diode based monitoring systems lack.