Despite enormous progress in metallic additive manufacturing (AM) process, one of the most notable needs is the industry agreement for the development of proper in-situ sensing methods and implementation of in-process sensors (monitoring/controlling) for product acceptance. Even though the usage of different signal processing techniques has greatly improved the defect tracking-learning-detection and classification performance, seldom are these methods robust and reliable enough to meet in-space standards and customers satisfaction. In order to develop reliable NDE in-process sensing and monitoring technologies for AM processes used to produce critical components for in-space applications, smart optical monitoring system based on spectroscopy is adopted and following technical challenges will be addressed by performing; (1) Understand the mechanism on how different composition, phase transformation and manufacturing defects affect the characterization of the laser/arc induced plasma; (2) Design effective algorithms and a seamless hardware digital signal processing unit that are able interpret the plasma signal for manufacturing quality prediction with high accuracy and reliability; and (3) Design a in-process monitoring / control system to adjust the manufacturing parameters to guarantee manufacturing quality. The approach to tackle these challenges is to use a good balance between understanding how different manufacturing defects, compositions and phase transformations affect the plasma characterization and the use of effective algorithm to interpret the change of plasma to reflect the defects. The successful execution of the tasks proposed will help us develop a smart sensor far beyond the available state-of-the-art technologies to provide in-situ and reliable prediction of composition, phase transformation and manufacturing defect that meets in-space application standards.
It has a multifaceted value proposition. The first is the reduced manual inspection, along with the associated timesaving’s from eliminating these inspections, which provides the greatest cost reduction. A second source of value addition is the decreased cycle time associated with detecting defects earlier in the manufacturing process, enabling waste reduction by eliminating redundant work on defective parts. The final portion of the value proposition comes from elimination of potential liability costs by steadily improving product quality.
SenSigma has established connection with customers and plans to go into the market through successful field tests. We have already completed field tests at Caterpillar, GE, Trumpf, ABB Robot, and Lincoln Electric. We are currently for field tests with NRL. Potential customers are in metal manufacturing industries, including automotive, heavy equipment manufacturing, and aerospace industries.