In situ quality inspection with layer-wise visual images based on deep transfer learning during selective laser melting

Li, Jingchang; Zhou, Qi; Huang, Xufeng; Li, Menglei; Cao, Longchao

doi:10.1007/s10845-021-01829-5

Cited by 31 publications

(3 citation statements)

References 41 publications

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“…However, precisely delineating the boundary between the general and task-specific features is challenging. Various research endeavors substantiated the viability and applicability of TL across diverse domains, including medical (Albayrak, 2022; Wang et al , 2021; Yadlapalli et al , 2022), plant science (Joshi et al , 2021), mechanic (Mao et al , 2020) and additive manufacturing (Li et al , 2021). Nagorny et al (2020) used polarimetric images to train VGG16 and MobileNetV2 networks via TL approach for quality inspection of injection parts.…”

Section: Introductionmentioning

confidence: 99%

In-situ quality inspection system of injection parts based on transfer learning

Yang,

Shan,

Jin

et al. 2024

RIA

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Purpose This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology. Design/methodology/approach The proposed in-situ quality inspection system consists of an injection machine, USB camera, programmable logic controller and personal computer, interconnected via OPC or USB communication interfaces. This configuration enables seamless automation of the IM process, real-time quality inspection and automated decision-making. In addition, a MobileNet-based deep learning (DL) model is proposed for quality inspection of injection parts, fine-tuned using the TL approach. Findings Using the TL approach, the MobileNet-based DL model demonstrates exceptional performance, achieving validation accuracy of 99.1% with the utilization of merely 50 images per category. Its detection speed and accuracy surpass those of DenseNet121-based, VGG16-based, ResNet50-based and Xception-based convolutional neural networks. Further evaluation using a random data set of 120 images, as assessed through the confusion matrix, attests to an accuracy rate of 96.67%. Originality/value The proposed MobileNet-based DL model achieves higher accuracy with less resource consumption using the TL approach. It is integrated with automation technologies to build the in-situ quality inspection system of injection parts, which improves the cost-efficiency by facilitating the acquisition and labeling of task-specific images, enabling automatic defect detection and decision-making online, thus holding profound significance for the IM industry and its pursuit of enhanced quality inspection measures.

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Section: Introductionmentioning

confidence: 99%

In-situ quality inspection system of injection parts based on transfer learning

Yang,

Shan,

Jin

et al. 2024

RIA

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“…Therefore, the DL algorithms(J. Li et al, 2021;X. Li et al, 2020) have shown the great capability to extract useful information for AM part quality analysis.…”

Section: Introductionmentioning

confidence: 99%

A deep learning solution for real-time quality assessment and control in additive manufacturing using point cloud data

2023

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This work presents an in-situ quality assessment and improvement technique using point cloud and AI for data processing and smart decision making in Additive Manufacturing (AM) fabrication to improve the quality and accuracy of fabricated artifacts. The top surface point cloud containing top surface geometry and quality information is pre-processed and passed to an improved deep Hybrid Convolutional Auto-Encoder decoder (HCAE) model used to statistically describe the artifact's quality. The HCAE's output is comprised of 9*9 segments, each including four channels with the segment's probability to contain one of four labels, Under-printed, Normally-printed, Over-printed, or Empty region. This data structure plays a significant role in command generation for fabrication process optimization. The HCAE's accuracy and repeatability were measured by a multi-label multi-output metric developed in this study. The HCAE's results are used to perform a real-time process adjustment by manipulating the future layer's fabrication through the G-code modification. By adjusting the machine's print speed and feed-rate, the controller exploits the subsequent layer's deposition, grid-by-grid. The algorithm is then tested with two defective process plans: severe under-extrusion and over-extrusion conditions. Both test artifacts' quality advanced significantly and converged to an acceptable state by four iterations.

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“…In-situ process monitoring of the melt-pool, laser track, or build layer during the AM process is critical in detecting potential porosity and controlling part quality. Commonly measured signatures are thermal (Bakish, 1983;Nath & Mahadevan, 2021) or acoustic features (Eschner et al, 2020), and optical images (Bugatti & Colosimo, 2021;Li et al, 2021;McCann et al, 2021) before or after powder recoating or powder fusion (Grasso & Colosimo, 2017). However, In-situ process monitoring, and porosity detection in current literature (Paulson et al, 2020;Scime et al, 2020;Zhang et al, 2020) can only detect porosity that has already occurred but cannot predict potential porosity which might occur in the future, a much-desired ability to guide real-time process parameter optimization for better part quality.…”

Section: Introductionmentioning

confidence: 99%

A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures

Mao

Lin

et al. 2022

J Intell Manuf

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Part quality manufactured by the laser powder bed fusion process is significantly affected by porosity. Existing works of process–property relationships for porosity prediction require many experiments or computationally expensive simulations without considering environmental variations. While efforts that adopt real-time monitoring sensors can only detect porosity after its occurrence rather than predicting it ahead of time. In this study, a novel porosity detection-prediction framework is proposed based on deep learning that predicts porosity in the next layer based on thermal signatures of the previous layers. The proposed framework is validated in terms of its ability to accurately predict lack of fusion porosity using computerized tomography (CT) scans, which achieves a F1-score of 0.75. The framework presented in this work can be effectively applied to quality control in additive manufacturing. As a function of the predicted porosity positions, laser process parameters in the next layer can be adjusted to avoid more part porosity in the future or the existing porosity could be filled. If the predicted part porosity is not acceptable regardless of laser parameters, the building process can be stopped to minimize the loss.

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In situ quality inspection with layer-wise visual images based on deep transfer learning during selective laser melting

Cited by 31 publications

References 41 publications

In-situ quality inspection system of injection parts based on transfer learning

In-situ quality inspection system of injection parts based on transfer learning

A deep learning solution for real-time quality assessment and control in additive manufacturing using point cloud data

A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures

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