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

金属增材制造（AM）是一种新兴技术，在先进制造业中呈指数级增长。该技术使用机器人或CNC系统生产复杂的自由形状和精确的金属沉积。金属增材制造工艺质量保证的核心要素是了解制造工艺对沉积材料微观结构的影响，这直接影响打印部件的机械性能、几何形状和质量。发现这些影响需要大量的努力和时间，并且是一个限制技术成熟的费力过程。然而，随着工业4.0、过程自动化和机器学习（ML）工具的进步，有机会加速我们对金属增材制造工艺的理解。 申请人将利用机器人金属增材制造（RM-AM），机器学习（ML）和深度学习（DL）的最新进展，开发自动化，可扩展，平台不可知的网络物理系统，以实现与机器人金属增材制造有关的两个主要目标（金属-AM）：（i）通过过程参数控制优化所生产的部件的质量，以及（ii）由给定组成的材料打印的部件的机械性能的预测建模。ML技术规避了由人类专家进行的常规时间和资源密集型参数评估和优化过程的需要。该系统将包括一个硬件层，用于执行金属沉积，并与传感器融合集成，以监控正在制造的零件的几何形状。传感器的数据将通过ML训练的反馈控制系统进行处理，该系统将向执行硬件发送适当的控制信号，以调整工艺参数，从而优化零件质量。该项目的有形交付物将以自主机器人金属增材制造系统、用于机器人金属增材制造系统的基于传感器融合的多源高频传感器套件以及一套用于增材制造系统的模型识别和机械及几何性能改进的基于AI的算法的形式出现。

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.