3D scanning method for robotized inspection of industrial sealed parts

Santos, Kleber Roberto da Silva; Oliveira, Wesley Rodrigues de; Villani, Emilia; Dttmann, Augusto

doi:10.1016/j.compind.2022.103850

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…A more recent study carried out by Hyunwoo et al [7] proposes applying the same methodology to inspect the integrity of battery seals. Santos et al [8] proposes a 3D scanning process that involves a robotic arm and the use of a structured light 3D scanner to acquire a high-resolution point cloud of the surface of the sealed part. The point cloud data is then processed using a series of algorithms to remove noise and outliers, and to segment the data into regions corresponding to different features of the sealed part.…”

Section: A Current Methodologies To Inspect Sealsmentioning

confidence: 99%

Automatic Inspection of Seal Integrity in Sterile Barrier Packaging: A Deep Learning Approach

Diaz,

Farooq,

Corcoran

2024

IEEE Access

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The digitalisation of visual tasks through imaging techniques and Computer Vision has the potential of disrupting the manner in which Advanced Manufacturing processes are deployed. In this work we have collaborated with the manufacturing industry to investigate the effective usage of end-to-end convolution neural networks (CNNs) to enable advanced manufacturing processes by inspecting the seal integrity of sterile barrier packaging in highly regulated products, such as Medical Devices. For this purpose, a novel 'DS1' dataset of labelled images representative of production samples has been acquired in a representative industrial environment which is further open sourced for future research work. The core focus of this research work is to address the common challenges associated with performing quality inspections in advanced manufacturing environments with the aim of detecting defects with very high impact but very low recurrence rate, by incorporating a set of pretrained deep learning architectures. The performance of state-ofthe-art CNNs is validated on unseen test data when trained in small and imbalanced datasets of low image variation and low pixel complexity. The study indicates that while CNN performance drops when datasets are imbalanced, some architectures are more resilient and capable of successfully classifying defects in small datasets in the order of a few hundred samples wherein as little as 5% of the samples are defective. Furthermore, this study also discusses the marginal impact of training with basic data augmentations and the tendency for models to overfit when trained with manufacturing datasets such as "DS1".

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Section: A Current Methodologies To Inspect Sealsmentioning

confidence: 99%

Automatic Inspection of Seal Integrity in Sterile Barrier Packaging: A Deep Learning Approach

Diaz,

Farooq,

Corcoran

2024

IEEE Access

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“…These inspection robots are equipped with a myriad of sensors, encompassing RGB and infrared (IR) imaging, auditory capabilities, Light Detection and Ranging (LiDAR) technology, and gas hazard sensors [ 3 , 4 , 5 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Advanced Image Analytics for Mobile Robot-Based Condition Monitoring in Hazardous Environments: A Comprehensive Thermal Defect Processing Framework

Siami,

Barszcz,

Zimroz

2024

Sensors

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In hazardous environments like mining sites, mobile inspection robots play a crucial role in condition monitoring (CM) tasks, particularly by collecting various kinds of data, such as images. However, the sheer volume of collected image samples and existing noise pose challenges in processing and visualizing thermal anomalies. Recognizing these challenges, our study addresses the limitations of industrial big data analytics for mobile robot-generated image data. We present a novel, fully integrated approach involving a dimension reduction procedure. This includes a semantic segmentation technique utilizing the pre-trained VGG16 CNN architecture for feature selection, followed by random forest (RF) and extreme gradient boosting (XGBoost) classifiers for the prediction of the pixel class labels. We also explore unsupervised learning using the PCA-K-means method for dimension reduction and classification of unlabeled thermal defects based on anomaly severity. Our comprehensive methodology aims to efficiently handle image-based CM tasks in hazardous environments. To validate its practicality, we applied our approach in a real-world scenario, and the results confirm its robust performance in processing and visualizing thermal data collected by mobile inspection robots. This affirms the effectiveness of our methodology in enhancing the overall performance of CM processes.

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High speed neuromorphic vision-based inspection of countersinks in automated manufacturing processes

Salah,

Ayyad,

Ramadan

et al. 2023

J Intell Manuf

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Countersink inspection is crucial in various automated assembly lines, especially in the aerospace and automotive sectors. Advancements in machine vision introduced automated robotic inspection of countersinks using laser scanners and monocular cameras. Nevertheless, the aforementioned sensing pipelines require the robot to pause on each hole for inspection due to high latency and measurement uncertainties with motion, leading to prolonged execution times of the inspection task. The neuromorphic vision sensor, on the other hand, has the potential to expedite the countersink inspection process, but the unorthodox output of the neuromorphic technology prohibits utilizing traditional image processing techniques. Therefore, novel event-based perception algorithms need to be introduced. We propose a countersink detection approach on the basis of event-based motion compensation and the mean-shift clustering principle. In addition, our framework presents a robust event-based circle detection algorithm to precisely estimate the depth of the countersink specimens. The proposed approach expedites the inspection process by a factor of 10$$\times $$ × compared to conventional countersink inspection methods. The work in this paper was validated for over 50 trials on three countersink workpiece variants. The experimental results show that our method provides a standard deviation of 0.025 mm and an accuracy of 0.026 mm for countersink depth inspection despite the low resolution of commercially available neuromorphic cameras. Video Link: https://www.dropbox.com/s/pateqqwh4d605t3/final_video_new.mp4?dl=0.

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3D scanning method for robotized inspection of industrial sealed parts

Cited by 6 publications

References 11 publications

Automatic Inspection of Seal Integrity in Sterile Barrier Packaging: A Deep Learning Approach

Automatic Inspection of Seal Integrity in Sterile Barrier Packaging: A Deep Learning Approach

Advanced Image Analytics for Mobile Robot-Based Condition Monitoring in Hazardous Environments: A Comprehensive Thermal Defect Processing Framework

High speed neuromorphic vision-based inspection of countersinks in automated manufacturing processes

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