Deep Vision in Smart Manufacturing: MODERN Framework for Intelligent Quality Monitoring and Diagnosis

Yicheng Kang; Yuling Jiao; Xin Geng; Mahesh Nagarajan

stat.MLAug 14, 2026

Deep Vision in Smart Manufacturing: MODERN Framework for Intelligent Quality Monitoring and Diagnosis

Authors: Yicheng Kang, Yuling Jiao, Xin Geng, Mahesh Nagarajan

Organizations: Farmer School of Business, Miami University, OH. USA · School of Mathematics & Statistics, Wuhan University, Hubei, China · Miami Herbert Business School, University of Miami, FL. USA · Sauder School of Business, The University of British Columbia, BC. Canada

Abstract

Smart manufacturing processes are often installed with a large number of sensors, imaging devices and computers, which not only enable instant communication across various modules of a production system but also aid in intelligent manufacturing management. In this paper, we introduce MODERN, a deep learning framework for quality monitoring and fault isolation, which integrates these enhanced capabilities into the practice of industrial quality control. Using the architecture of an inception residual neural network, we develop a control chart that monitors the likelihood of a product containing defects. We also propose a faulty region estimator that identifies the defective area using transfer learning. To extend our framework to cases where there are not sufficient training data, we suggest a transfer monitoring technique that requires only a small sample size and a hypothesis testing approach for quantitatively assessing the applicability of our method. Theoretically, we establish the minimax optimal convergence rate for both our defect likelihood estimation and fault diagnosis. Our results lead to a seemingly counter-intuitive managerial implication - it may not always be in a manufacturer's best interests to keep upgrading its monitoring equipment regardless of the cost. Empirically, we demonstrate the superior performance of our method in comparison with a state-of-the-art approach using both simulated experiments and real data.

Figures & tables

Panel of Figure 1 — Figure 1: An illustration for the insufficiency of the existing image-based quality monitoring.

Panel of Figure 2 — Figure 2: Graphical illustration of filter concatenation and residual connection in a generic inception module.

Figure 3: The complete schematic view of MODERN-Net architecture.

Panel of Figure 4 — Figure 4: Simulated IC (left) and OC (right) image

Shift size	Low	Medium	High
1	0.46	0.47	0.47
2	0.59	0.51	0.46
3	1.00	0.96	0.46

Table 1: The impact of image resolution on classification accuracy.

Class	ARL 1	sdARL 1	Prop.Early	Prop.On	SDSC	sdSDSC
1	20.64	0.2533	0.02	0.98	0.8293	0.0280
2	20.96	0.0400	0.01	0.99	0.7157	0.0270
3	21.00	0.0000	0.00	1.00	0.7974	0.0131
4	20.82	0.1218	0.03	0.97	0.7696	0.0348
5	21.00	0.0000	0.00	1.00	0.8719	0.0135
6	21.00	0.0000	0.00	1.00	0.6548	0.0425

Table 2: The numerical performance of the proposed method on the DAGM testing set.

Panel of Figure 5 — Figure 5: Performance of MODERN-Diagnosis: Estimated vs true faulty regions.

Fault Type	Method	ARL 1	sdARL 1	Prop.On	SDSC	sdSDSC
	MODERN	21.00	0.0000	1.00	0.6286	0.0084
1	MF	153.75	6.7987	0.01	0.0000	0.0000
	t-stat	$19.5258^{**}$			$74.8333^{**}$
	MODERN	21.00	0.0000	1.00	0.6208	0.0057
2	MF	150.58	6.3956	0.00	0.5762	0.0097
	t-stat	$20.2608^{**}$			$3.9642^{**}$

Table 3: Comparison between MODERN framework and the MF method in simulated experiments.

Panel of Figure 6 — Figure 6: Comparison between MODERN framework and MF method in electric commutator manufacturing.

Panel of Figure 7 — Figure 7: Comparison between MODERN framework and MF method in leather manufacturing.

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 8 — Figure 8: Sample DAGM images: examples of IC and OC images.

Panel of Figure 9 — Figure 9: The training progression of $\mathscr{F}(\cdot;\mathcal{V})$ (left) and $\mathscr{G}(\cdot;\mathcal{U})$ (right) as the number of epochs increases.

Panel of Figure 10 — Figure 10: Representative images in the AR simulated example.

Figure 11: The histogram of the applicability test in which $\mathscr{F}(\cdot;\widehat{\mathcal{V}})$ is applied to 500 IC images and the two types of OC images (500 each type) generated by the AR model.

Panel of Figure 12 — Figure 12: Fault isolation results give by MODERN-Diagnosis and the MF method in simulations.

Panel of Figure 13 — Figure 13: Representative images in the electric commutator manufacturing.

Figure 14: The histogram of the applicability test for the commutator images

Layer	Size	Kernel Size	Stride	Pad
Input Image	$512\times 512\times 3$
Convolution 1	$128\times 128\times 96$	$11\times 11\times 3$	4	2
Batch Norm 1	$128\times 128\times 96$
Leaky ReLU 1	$128\times 128\times 96$
Max Pooling 1	$63\times 63\times 96$	$3\times 3$	2	0
Convolution 2	$63\times 63\times 256$	$5\times 5\times 96$	1	2

Appendix

Table 4: The architecture of AlexNet.

	Specificity		Sensitivity
Class	MODERN	AlexNet	MODERN	AlexNet
1	100%	100%	100%	100%
2	100%	100%	100%	100%
3	100%	100%	100%	100%
4	100%	98.63%	100%	42.31%
5	100%	100%	100%	90.48%

Appendix

Table 5: Performance comparison with AlexNet using DAGM.

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CLAIRE: Compressed Latent Autoencoder for Industrial Representation and Evaluation -- A Deep Learning Framework for Smart Manufacturing

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Accurate fault detection in high-dimensional industrial environments remains a major challenge due to the inherent complexity, noise, and redundancy in sensor data. This paper introduces CLAIRE, i.e., a hybrid end-to-end learning framework that integrates unsupervised deep representation learning with supervised classification for intelligent quality control in smart manufacturing systems. It employs an optimized deep autoencoder to transform raw input into a compact latent space, effectively capturing the intrinsic data structure while suppressing irrelevant or noisy features. The learned representations are then fed into a downstream classifier to perform binary fault prediction. Experimental results on a high-dimensional dataset demonstrate that CLAIRE significantly outperforms conventional classifiers trained directly on raw features. Moreover, the framework incorporates a post hoc phase, using a game-theory-based interpretability technique, to analyze the latent space and identify the most informative input features contributing to fault predictions. The proposed framework highlights the potential of integrating explainable AI with feature-aware regularization for robust fault detection. The modular and interpretable nature of the proposed framework makes it highly adaptable, offering promising applications in other domains characterized by complex, high-dimensional data, such as healthcare, finance, and environmental monitoring.

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