Sensor fusion enabled machine learning for quality and material optimization in robotic additive manufacturing

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Metal additive manufacturing (AM) is an emerging technology that has seen exponential growth in the advanced manufacturing industry. This technology produces complex freeform shapes with precise metal deposition using robotic, or CNC, systems. A core element in quality assurance of metal AM processes is understanding the manufacturing process' effect on the deposited material's microstructure which directly affects the mechanical behavior, geometry, and quality of the printed part. Discovering these effects requires extensive effort and time, and is a laborious process limiting the maturity of technology. However, with the advancement of Industry 4.0, process automation and machine learning (ML) tools, there is an opportunity to accelerate our understanding of metal additive manufacturing processes.The applicants will leverage the cutting edge advances in robotic metal additive manufacturing (RM-AM), machine learning (ML), and deep learning (DL) to develop an automated, scalable, platform agnostic cyber-physical system to achieve two main goals pertaining to robotic metal additive manufacturing (metal-AM): (i) quality optimization of the parts produced through process parameter control, and (ii) predictive modeling of the mechanical properties of parts printed from materials of a given composition. ML techniques circumvent the need for the conventional time, and resource-intensive parameter assessment and optimization process carried out by human experts. This system will consist of a hardware layer to execute metal deposition integrated with sensor-fusion to monitor the geometry of the parts being manufactured. The sensors' data will be processed through an ML trained feedback control system that would send the appropriate control signals to the execution hardware to tune the process parameters to optimize part quality. The tangible deliverables of the project will be in form of an autonomous robotic metal additive manufacturing system, sensor fusion based multi source high frequency sensor suite for robotic metal additive manufacturing systems, and a set of AI based algorithms for model identification and mechanical and geometric property improvement for the additive manufacturing systems.