Modeling inter-layer interactions for out-of-plane shape deviation reduction in additive manufacturing

Jin, Yuan; Qin, S. Joe; Huang, Qiang

doi:10.1080/24725854.2019.1676936

Cited by 17 publications

(5 citation statements)

References 36 publications

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“…The introduced predictive system improved the dimensional accuracy of FDM three-dimensional printed parts by compensating the original design of the model to minimize the deviation between the printed object and the theoretical one. Additionally, Jin et al (2019) aimed at compensating and modeling the out-of-plane deviation of two-dimensional freeform shapes taking into account the inter-layer interactions. A Bayesian approach was applied to infer the predictive model for freeform out-of-plane shapes.…”

Section: Learning-based Quality Control Methodsmentioning

confidence: 99%

Non-destructive quality control methods in additive manufacturing: a survey

Charalampous

Kostavelis

Tzovaras

2020

RPJ

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Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

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Section: Learning-based Quality Control Methodsmentioning

confidence: 99%

Non-destructive quality control methods in additive manufacturing: a survey

Charalampous

Kostavelis

Tzovaras

2020

RPJ

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“…Among other sources, they also studied the influence of extruder positioning error in a fused filament fabrication (FFF) machine on material deposition (Wang et al , 2016). To optimise the 3D compensation methodology, they conducted a study to determine the interaction that appears between adjacent layers (Jin et al , 2020) and established a convolution-based formulation to model the distortions due to this phenomenon (Huang et al , 2020). Some of the most recent works model deflections using Bayesian neural networks (Ferreira et al , 2019) or by models capable of predicting deflections, both in geometries with smooth curvatures and with abrupt changes (Wang et al , 2022).…”

Section: Introductionmentioning

confidence: 99%

A layerwise geometric error compensation procedure for additive manufacturing

Peña,

Rico,

Zapico

et al. 2024

RPJ

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Purpose The purpose of this paper is to provide a new procedure for in-plane compensation of geometric errors that often appear in the layers deposited by an additive manufacturing (AM) process when building a part, regardless of the complexity of the layer geometry. Design/methodology/approach The procedure is based on comparing the real layer contours to the nominal ones extracted from the STL model of the part. Considering alignment and form deviations, the compensation algorithm generates new compensated contours that match the nominal ones as closely as possible. To assess the compensation effectiveness, two case studies were analysed. In the first case, the parts were not manufactured, but the distortions were simulated using a predictive model. In the second example, the test part was actually manufactured, and the distortions were measured on a coordinate measuring machine. Findings The geometric deviations detected in both case studies, as evaluated by various quality indicators, reduced significantly after applying the compensation procedure, meaning that the compensated and nominal contours were better matched both in shape and size. Research limitations/implications Although large contours showed deviations close to zero, dimensional overcompensation was observed when applied to small contours. The compensation procedure could be enhanced if the applied compensation factor took into account the contour size of the analysed layer and other geometric parameters that could have an influence. Originality/value The presented method of compensation is applicable to layers of any shape obtained in any AM process.

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“…Only a few studies consider the effect of out-of-plane (z-plane) deviation. Huang and his research team conducted a series of studies using Spherical Coordinate System (SCS), where both in-plane (deviation in X-Y plane) and out-of-plane (deviation in Z-direction) deviations are modeled through a consistent statistical modeling framework [10,11]. However, the data-driven predictive modeling of layer wise surface finish for monitoring the cumulation of such out-of-plane deviation has not been well investigated.…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Out-of-Plane Deviation for the Quality Improvement of Additive Manufacturing

Wang

Shraida

Jin

2023

MSF

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Additive manufacturing (AM) is a new technology for fabricating products straight from a 3D digital model, which can lower costs, minimize waste, and increase building speed while maintaining acceptable quality. However, it still suffers from low dimensional accuracy and a lack of geometrical quality standards. Moreover, there is a need for a robust AM configuration to perform in-situ inspections during the fabrication. This work established a 3D printing-scanning setup to collect 3D point cloud data of printed parts and then compare them with nominal 3D point cloud data to quantify the deviation in all X, Y, and Z directions. Specifically, this work aims at predicting the anticipated deviation along the Z direction by applying a deep learning-based prediction model. An experiment with regard to a human “Knee” prototype fabricated by Fused Deposition Modeling (FDM) is conducted to show the effectiveness of the proposed methods.

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Modeling inter-layer interactions for out-of-plane shape deviation reduction in additive manufacturing

Cited by 17 publications

References 36 publications

Non-destructive quality control methods in additive manufacturing: a survey

Non-destructive quality control methods in additive manufacturing: a survey

A layerwise geometric error compensation procedure for additive manufacturing

Predictive Modeling of Out-of-Plane Deviation for the Quality Improvement of Additive Manufacturing

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