Statistical process control of in-plane shape deformation for additive manufacturing

Luan, He; Post, Brian; Huang, Qiang

doi:10.1109/coase.2017.8256276

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…Finally, no effort has been conducted to determine if a global form deviation model could be applied, considering local deviations as a function of part location, type of surface, and polar coordinates in a single model, instead of using independent models for each combination of significant factors. This possibility could lead to a general compensation model, in the direction pointed out by previous works [ 32 , 33 ]. Although this idea falls out of the scope of present research, it is believed to be worthy of further research.…”

Section: Application Examplementioning

confidence: 89%

“…Tong et al [ 28 ] described form errors in material extrusion (MEX) processes as a function of 18 parametric errors and elaborate compensation models for the STL file, and for individual slices, achieving clear improvements on part geometry. The group of Professor Huang has done extensive research in shape deviation modelling and compensation [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. They proposed a polar deviation model, especially suited for cylindrical surfaces, where the deviations between the actual surface and the nominal one were evaluated in the normal direction to each surface point [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

“…The model was extended to a system-level approach to predict and compensate generic shapes [ 30 ] and used to evaluate the effect of a discrete compensation using a finite number of levels [ 31 ]. Their model has been also extended to generalized shapes [ 32 ] and previously untried shapes [ 33 ] to create a global deviation modelling strategy. Additionally, they have made attempts to extend 2D compensation to 3D compensation by dividing the problem into in-plane deformation (concerning the cross-section) and out-of-plane deformation [ 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

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A Design for Additive Manufacturing Strategy for Dimensional and Geometrical Quality Improvement of PolyJet-Manufactured Glossy Cylindrical Features

Beltrán¹,

Álvarez²,

Blanco³

et al. 2021

Polymers

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The dimensional and geometrical quality of additively manufactured parts must be increased to match industrial requirements before they can be incorporated to mass production. Such an objective has a great relevance in the case of features of linear size that are affected by dimensional or geometrical tolerances. This work proposes a design for additive manufacturing strategy that uses the re-parameterization of part design to minimize shape deviations from cylindrical geometries. An analysis of shape deviations in the frequency domain is used to define a re-parameterization strategy, imposing a bi-univocal correspondence between verification parameters and design parameters. Then, the significance of variations in the process and design factors upon part quality is analyzed using design of experiments to determine the appropriate extension for modelling form deviation. Finally, local deviations are mapped for design parameters, and a new part design including local compensations is obtained. This strategy has been evaluated upon glossy surfaces manufactured in a Vero™ material by polymer jetting. The results of the proposed example showed a relevant improvement in dimensional quality, as well as a reduction of geometrical deviations, outperforming the results obtained with a conventional scaling compensation.

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Section: Application Examplementioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Design for Additive Manufacturing Strategy for Dimensional and Geometrical Quality Improvement of PolyJet-Manufactured Glossy Cylindrical Features

Beltrán¹,

Álvarez²,

Blanco³

et al. 2021

Polymers

View full text Add to dashboard Cite

show abstract

“…Most of the investigated AM processes in the field of process monitoring have been taken as case studies to demonstrate proposed statistical models, as in the work of Iqbal et al [23], who attempted to involve auxiliary variables in the monitor-ing process rather than the response variable alone. In a similar manner, Luan et al [24] considered the stereolithography process conditions to monitor shape deformation. Quality control for different AM processes using different materials has also been evaluated.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

A Framework for Multivariate Statistical Quality Monitoring of Additive Manufacturing: Fused Filament Fabrication Process

et al. 2023

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Advances in additive manufacturing (AM) processes have increased the number of relevant applications in various industries. To keep up with this development, the process stability of AM processes should be monitored, which is conducted through the assessment of the outputs or product characteristics. However, the use of univariate control charts to monitor an AM process might lead to misleading results, as most additively manufactured products have more than one correlated quality characteristic (QC). This paper proposes a framework for monitoring the multivariate quality characteristics of AM processes, and the proposed framework was applied to monitor a fused filament fabrication (FFF) process. In particular, specimens were designed and produced using the FFF process, and their QCs were identified. Then, critical quality characteristic data were collected using a precise measurement system. Furthermore, we propose a transformation algorithm to ensure the normality of the collected data. After examining the correlations between the investigated quality characteristics, a multivariate exponential weighted moving average (MEWMA) control chart was used to monitor the stability of the process. Furthermore, the MEWMA parameters were optimized using a novel heuristic technique. The results indicate that the majority of the collected data are not normally distributed. Consequently, the efficacy of the proposed transformation technique is demonstrated. In addition, our findings illustrate the correlations between the QCs. It is worth noting that the MEWMA optimization results confirm that the considered AM process (i.e., FFF) is relatively stable.

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“…However, due to the sensor size and the scanning speed issue, the CT is difficult to be used for online (i.e., layer-wise) voids detection [9], quality monitoring [10], and process control [11] to mitigate the flaw. As an alternative, the optical camera can provide layer-wise images for the product during the process [12][13][14]. As shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Pyramid Ensemble Convolutional Neural Network for Virtual Computed Tomography Image Prediction in a Selective Laser Melting Process

Wang

Chen

Henkel³

et al. 2021

Journal of Manufacturing Science and Engineering

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Additive manufacturing (AM) is a type of advanced manufacturing process that enables fast prototyping to realize personalized products in complex shapes. However, quality defects existed in AM products can directly lead to significant failures in practice. Thus, various inspection techniques have been investigated to evaluate the quality of AM products, where X-ray computed tomography serves as one of the most accurate techniques to detect defects. Taking a selective laser melting process (SLM) as an example, voids can be detected by investigating CT images after the fabrication of products. However, limited by the sensor size and scanning speed issue, CT is difficult to be used for online (i.e., layer-wise) voids detection, monitoring, and process control to mitigate the defects. As an alternative, optical cameras can provide layer-wise images to support online voids detection. The intricate texture of the layer-wise image restricts the accuracy of void detection in AM products. Therefore, we propose a new method called pyramid ensemble convolutional neural network to efficiently detect voids and predict the texture of CT images by using layer-wise optical images. The proposed PECNN can efficiently extract informative features based on the ensemble of the multiscale feature-maps from optical images. Unlike deterministic ensemble strategies, this ensemble strategy is optimized by training a neural network in a data-driven manner to learn the fine-grained information from the extracted feature-maps. The merits of the proposed method are illustrated by both simulations and a real case study in SLM.

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Statistical process control of in-plane shape deformation for additive manufacturing

Cited by 8 publications

References 12 publications

A Design for Additive Manufacturing Strategy for Dimensional and Geometrical Quality Improvement of PolyJet-Manufactured Glossy Cylindrical Features

A Design for Additive Manufacturing Strategy for Dimensional and Geometrical Quality Improvement of PolyJet-Manufactured Glossy Cylindrical Features

A Framework for Multivariate Statistical Quality Monitoring of Additive Manufacturing: Fused Filament Fabrication Process

Pyramid Ensemble Convolutional Neural Network for Virtual Computed Tomography Image Prediction in a Selective Laser Melting Process

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