SBIR Phase I: Sensors for InLine Certification Capability for Additive Manufacturing

Sensigma Llc, Ann Arbor MI

Investigators

Abstract

This Small Business Innovation Research Phase 1 project will develop a spectroscopic sensor that detects and categorizes defects, predicts the composition and phase transformation, and monitors the manufacturing quality in real time for additive manufacturing industry. Current sensors for quality assurance are not widely employed in manufacturing processes because they lack reliability and functionality, do not work in real time, or are costly and complex to implement. The approach to design the smart optical monitoring system is through understanding the physical mechanism of laser/arc manufacturing processes, characterizing the laser/arc induced plasma and developing effective plasma signal processing algorithm to predict manufacturing quality. The intellectual merits lies in 1) a breakthrough technology for in-situ monitoring and control of phase transformation and composition by systematic diagnosis of the laser induced plasma, and 2) an advanced technology to detect and categorize manufacturing defects through understanding the effects of different defects on plasma and designing effective algorithms to interpret plasma signals. The smart optical monitoring sensor will contribute to the competitive advantage for American manufacturing industry by providing 'InLine Certification' capability for additive manufacturing, to minimize material wastage and lost labor time, and to increase long-term product quality. The broader impact/commercial potential of this project lies in its ability to save enormous amount of capital resource of materials and human time and to increase the competitive capacity of the industryby dramatically reducing the post-processing time to identify composition, microstructure and manufacturing qualities, leading to zero scrap. The enhanced scientific and technological understanding lies in the mechanism of how defects, composition and phase transformation affect the characteristics of the laser/arc induced plasma, and how to design effective algorithm based on this mechanism to in-situ interpret the plasma signals to predict defects, composition and phase transformation and further categorize different defects. The Broader Societal impact of this innovative spectroscopic sensor is its potential to influence the whole metal manufacturing and materials processing industries by providing the 'InLine Certification' capability. The capability of predicting composition, phase transformation and manufacturing defect will allow for the fabrication of near net shape and property (NNSP) components with heterogeneous structures and complex geometries for additive manufacturing industry. In-situ prediction of phase transformation using spectroscopic sensor is a high-risk, high payoff innovative technology with the potential to change the way material processing and manufacturing is practiced today.