Functional Quantitative and Qualitative Models for Quality Modeling in a Fused Deposition Modeling Process

Additive manufacturing (AM) enables flexible part geometry and functionality, and reduces product development life cycle by direct layer-wise fabrication from CAD files. In the last decade, great achievements are made on AM materials, machines, processes, etc. However, the quality of the AM parts is still questionable for industrial specifications. On the one hand, AM part quality variables can be either quantitative, such as dimensional accuracy, or qualitative, such as binary indicators for voids, missing features, or surface roughness. On the other hand, both offline process setting variables and functional in situ process variables can be measured and modeled with both quantitative and qualitative (QQ) quality response variables. In this paper, the QQ quality response variables are modeled by offline process setting variables and in situ process variables via functional QQ models. The modeling of these in situ process variables provides the basis for real-time monitoring and control for AM processes. Simulation studies and experimental data from a fused deposition modeling process are performed to demonstrate the effectiveness of the proposed method. Note to Practitioners - Additive manufacturing (AM) processes have attracted much attention and showed many advantages over the traditional subtractive manufacturing processes. However, the product quality issues make AM intractable for high-quality parts in industrial applications. This paper aims to address the quality issues by modeling both quantitative quality variables, such as dimensional accuracy, and qualitative quality variables, such as the binary (go/no-go) indicator for surface conditions. Both offline process setting variables and in situ process variables are used in the model as predictors. Such a model is important for systematically quality evaluation of AM parts, and provides the basis for future process monitoring and control. The merits of the proposed method are demonstrated with simulation studies and a case study in a fused deposition modeling process.