Automatic quality inspection in additive manufacturing using semi-supervised deep learning

Manivannan, Siyamalan

doi:10.1007/s10845-022-02000-4

Cited by 13 publications

(2 citation statements)

References 47 publications

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“…where v j ik is the k th element of v j i , and e i is the standard basis vector, where the i th element is 1, and all other elements are 0. And (10) remains applicable in this context.…”

Section: Perron-inspired Regular Termsmentioning

confidence: 99%

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Industrial Product Surface Defect Detection via the Fast Denoising Diffusion Implicit Model

wang,

Yang,

Liu

et al. 2023

Preprint

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In the age of intelligent manufacturing, surface defect detection plays a pivotal role in the automated quality control of industrial products, constituting a fundamental aspect of smart factory evolution. Considering the diverse sizes and feature scales of surface defects on industrial products and the difficulty in procuring high-quality training samples, the achievement of real-time and high-quality surface defect detection through artificial intelligence technologies remains a formidable challenge. To address this, we introduce a defect detection approach grounded in the Fast Denoising Probabilistic Implicit Model. Firstly, we propose a noise predictor influenced by the spectral radius feature tensor of image. This enhancement augments the ability of generative model to capture nuanced details in non-defective areas, thus overcoming limitations in model versatility and detail portrayal. Furthermore, we present a loss function constraint based on the Perron root. This is designed to incorporate the constraint within the representational space, ensuring the denoising model consistently produces high-quality samples. Lastly, comprehensive experiments on both the Magnetic Tile and Market-PCB datasets, benchmarked against nine most representative models, underscore the exemplary detection efficacy of our proposed approach.

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“…where v j ik is the k th element of v j i , and e i is the standard basis vector, where the i th element is 1, and all other elements are 0. And (10) remains applicable in this context.…”

Section: Perron-inspired Regular Termsmentioning

confidence: 99%

“…There are three broad categories for CV-based SDD methods: image processingbased methods [7,8], supervised learning [9,10], and unsupervised learning [11,12]. SDD methods based on traditional image processing techniques have reached a significant level of maturity through years of iterative development.…”

Section: Introductionmentioning

confidence: 99%

Industrial Product Surface Defect Detection via the Fast Denoising Diffusion Implicit Model

wang,

Yang,

Liu

et al. 2023

Preprint

View full text Add to dashboard Cite

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Numerical analysis of fault detection in additive manufacturing based on sustainable automation techniques

Kong,

Wang,

2023

Int J Adv Manuf Technol

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A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management

et al. 2022

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Additive manufacturing (AM) techniques are maturing and penetrating every aspect of the industry. With more and more design, process, structure, and property data collected, machine learning (ML) models are found to be useful to analyze the patterns in the data. The quality of datasets and the handling methods are important to the performance of these ML models. This work reviews recent publications on the topic, focusing on the data types along with the data handling methods and the implemented ML algorithms. The examples of ML applications on AM are then categorized based on the lifecycle stages, and research focuses. In terms of data management, the existing public database and data management methods are introduced. Finally, the limitations of the current data processing methods and suggestions are given.

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Automatic quality inspection in additive manufacturing using semi-supervised deep learning

Cited by 13 publications

References 47 publications

Industrial Product Surface Defect Detection via the Fast Denoising Diffusion Implicit Model

Industrial Product Surface Defect Detection via the Fast Denoising Diffusion Implicit Model

Numerical analysis of fault detection in additive manufacturing based on sustainable automation techniques

A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management

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