Data-driven Prediction of Surface Quality in Fused Deposition Modeling using Machine Learning

Abstract

In order to meet the growing demand for high-quality individualized products by end users, manufacturing companies need to establish new production technologies such as additive manufacturing. However, the industrial and automated application of these manufacturing technologies is currently impaired by low process stability and fluctuating product quality. This paper presents a novel approach for predicting product quality in Fused Deposition Modeling, based on process parameters, inline measurement data and a suitable machine learning algorithm. This should provide the basis for implementing process control and ensuring consistently high product quality.