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Additive manufacturing (AM) technologies are gaining immense popularity in the manufacturing sector because of their undisputed ability to construct geometrically complex prototypes and functional parts. However, the reliability of AM processes in providing high-quality products remains an open and challenging task, as it necessitates a deep understanding of the impact of process-related parameters on certain characteristics of the manufactured part. The purpose of this study is to develop a novel method for process parameter selection in order to improve the dimensional accuracy of manufactured specimens via the fused deposition modeling (FDM) process and ensure the efficiency of the procedure.

The introduced methodology uses regression-based machine learning algorithms to predict the dimensional deviations between the nominal computer aided design (CAD) model and the produced physical part. To achieve this, a database with measurements of three-dimensional (3D) printed parts possessing primitive geometry was created for the formulation of the predictive models. Additionally, adjustments on the dimensions of the 3D model are also considered to compensate for the overall shape deviations and further improve the accuracy of the process.

The validity of the suggested strategy is evaluated in a real-life manufacturing scenario with a complex benchmark model and a freeform shape manufactured in different scaling factors, where various sets of printing conditions have been applied. The experimental results exhibited that the developed regressive models can be effectively used for printing conditions recommendation and compensation of the errors as well.

The present research paper is the first to apply machine learning-based regression models and compensation strategies to assess the quality of the FDM process.

In recent years, additive manufacturing (AM) technology has been acknowledged as an efficient method for producing geometrical complex objects with a wide range of applications. However, dimensional inaccuracies and presence of defects hinder the broad adaption of AM procedures. These factors arouse concerns regarding the quality of the products produced with AM and the utilization of quality control (QC) techniques constitutes a must to further support this emerging technology. This paper aims to assist researchers to obtain a clear sight of what are the trends and what has been inspected so far concerning non-destructive testing (NDT) QC methods in AM.

In this paper, a survey on research advances on non-destructive QC procedures used in AM technology has been conducted. The paper is organized as follows: Section 2 discusses the existing NDT methods applied for the examination of the feedstock material, i.e. incoming quality control (IQC). Section 3 outlines the inspection methods for in situ QC, while Section 4 presents the methods of NDT applied after the manufacturing process i.e. outgoing QC methods. In Section 5, statistical QC methods used in AM technologies are documented. Future trends and challenges are included in Section 6 and conclusions are drawn in Section 7.

The primary scope of the study is to present the available and reliable NDT methods applied in every AM technology and all stages of the process. Most of the developed techniques so far are concentrated mainly in the inspection of the manufactured part during and post the AM process, compared to prior to the procedure. Moreover, material extrusion, direct energy deposition and powder bed processes are the focal points of the research in NDT methods applied in AM.

This literature review paper is the first to collect the latest and the most compatible techniques to evaluate the quality of parts produced by the main AM processes prior, during and after the manufacturing procedure.

Objects fabricated using additive manufacturing (AM) technologies often suffer from dimensional accuracy issues and other part-specific problems. This study aims to present a framework for estimating the printability of a computer-aided design (CAD) model that expresses the probability that the model is fabricated correctly via an AM technology for a specific application.

This study predicts the dimensional deviations of the manufactured object per vertex and per part using a machine learning approach. The input to the error prediction artificial neural network (ANN) is per vertex information extracted from the mesh of the model to be manufactured. The output of the ANN is the estimated average per vertex error for the fabricated object. This error is then used along with other global and per part information in a framework for estimating the printability of the model, that is, the probability of being fabricated correctly on a certain AM technology, for a specific application domain.

A thorough experimental evaluation was conducted on binder jetting technology for both the error prediction approach and the printability estimation framework.

This study presents a method for predicting dimensional errors with high accuracy and a completely novel approach for estimating the probability of a CAD model to be fabricated without significant failures or errors that make it inappropriate for a specific application.