An Intelligent Metrology Architecture With AVM for Metal Additive Manufacturing

Abstract

The capability of measuring melt pool variation is the key evaluating metal additive manufacturing quality. To measure the variation, a metrology architecture with in situ melt pool measurement and an estimation module is required. However, it is a challenge to effectively extract significant features from the huge data collected by the in situ metrology for quality estimation requirement. The purpose of this letter is to propose an intelligent metrology architecture with an in situ metrology (ISM) module and an enhanced automatic virtual metrology (AVM) system. The ISM module can extract the melt pool features with a coaxial camera and a pyrometer. On the other hand, the AVM system is improved with a feature selection method to solve the issue of limited samples in the component modeling quality. The examples with different metals are adopted to illustrate how the system works for estimating surface roughness and density of components, and, in the future, the system can even serve as the feedback signal for adaptive control of the process parameters by layering in an additive manufacturing system. Index Terms-Metal additive manufacturing, intelligent metrology architecture, in-situ metrology, melt pool, AVM. I. INTRODUCTION M ETAL additive manufacturing (AM) recently gains popularity due to its enormous benefits over traditional manufacturing, e.g., the flexibility in design, customized product on demand, and small-scale manufacturing. Metal AM utilizes 3D design to build a component directly, layer-by-layer, using the laser beam to melt the metal powder. Due to process complexity, condition variations, and hardware constraints of the layer Manuscript

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“…width, length, and area) can be used as indicators for quality prediction. The researchers developed different quality prediction algorithms such as porosity, density, etc., via the neural network, partial least square, and convolution neural network (CNN) according to the thermographic-based in-situ images [5] [8]. Based on the MPI shape, the actual melting power is predictable via the CNN model [18].…”

“…1 [5]. The LPBF machine will melt the powder by the fiber laser on the current layer after coating a layer of powder on the previous layer.…”

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