A Machine Vision Development Framework for Product Appearance Quality Inspection

Zhu, Qiuyu; Zhang, Yunxiao; Luan, Jianbing; Hu, Liheng

doi:10.3390/app122211565

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…The AR-based projection was reconstructed by [18] into a three-stage structure for pose estimation, tracking, and correction. The software and its development framework for visual detection were designed in [19], which specifies the resources involved in AVI. In addition, DL-based detection pipelines for specific parts can be found in [20,21].…”

Section: Generic Vision Inspection Frameworkmentioning

confidence: 99%

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

Zhao,

Kong,

et al. 2024

Sensors

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The industrial manufacturing model is undergoing a transformation from a product-centric model to a customer-centric one. Driven by customized requirements, the complexity of products and the requirements for quality have increased, which pose a challenge to the applicability of traditional machine vision technology. Extensive research demonstrates the effectiveness of AI-based learning and image processing on specific objects or tasks, but few publications focus on the composite task of the integrated product, the traceability and improvability of methods, as well as the extraction and communication of knowledge between different scenarios or tasks. To address this problem, this paper proposes a common, knowledge-driven, generic vision inspection framework, targeted for standardizing product inspection into a process of information decoupling and adaptive metrics. Task-related object perception is planned into a multi-granularity and multi-pattern progressive alignment based on industry knowledge and structured tasks. Inspection is abstracted as a reconfigurable process of multi-sub-pattern space combination mapping and difference metric under appropriate high-level strategies and experiences. Finally, strategies for knowledge improvement and accumulation based on historical data are presented. The experiment demonstrates the process of generating a detection pipeline for complex products and continuously improving it through failure tracing and knowledge improvement. Compared to the (, 69.802 mm) and 0.883 obtained by state-of-the-art deep learning methods, the generated pipeline achieves a pose estimation ranging from (, 153.584 mm) to (, 52.308 mm) and a detection rate ranging from 0.462 to 0.927. Through verification of other imaging methods and industrial tasks, we prove that the key to adaptability lies in the mining of inherent commonalities of knowledge, multi-dimensional accumulation, and reapplication.

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Section: Generic Vision Inspection Frameworkmentioning

confidence: 99%

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

Zhao,

Kong,

et al. 2024

Sensors

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“…Cutting tools are subjected to rigorous wear and tear during machining operations, leading to a decline in their performance and adversely affecting the quality of the final product. Timely and accurate assessment of tool wear is crucial for ensuring efficient machining operations, reducing downtime, and maximizing tool life [1].…”

Section: Introductionmentioning

confidence: 99%

“…The collected wear data can be used for trend analysis, predictive maintenance, and process optimization. Manufacturers can leverage this information to identify optimal tool life, improve tool selection, and optimize machining parameters, leading to enhanced productivity, reduced costs, and improved product quality [1].…”

Section: Introductionmentioning

confidence: 99%

Exploring the ViDiDetect Tool for Automated Defect Detection in Manufacturing with Machine Vision

Dziubek,

Rysiński,

Jancarczyk

2023

Applied Sciences

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Automated monitoring of cutting tool wear is of paramount importance in the manufacturing industry, as it directly impacts production efficiency and product quality. Traditional manual inspection methods are time-consuming and prone to human error, necessitating the adoption of more advanced techniques. This study explores the application of ViDiDetect, a deep learning-based defect detection solution, in the context of machine vision for assessing cutting tool wear. By capturing high-resolution images of machining tools and analyzing wear patterns, machine vision systems offer a non-contact and non-destructive approach to tool wear assessment, enabling continuous monitoring without disrupting the machining process. In this research, a smart camera and an illuminator were utilized to capture images of a car suspension knuckle’s machined surface, with a focus on detecting burrs, chips, and tool wear. The study also employed a mask to narrow the region of interest and enhance classification accuracy. This investigation demonstrates the potential of machine vision and ViDiDetect in automating cutting tool wear assessment, ultimately enhancing manufacturing processes’ efficiency and product quality. The project is at the implementation stage in one of the automotive production plants located in southern Poland.

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On-machine inspection and compensation for thin-walled parts with sculptured surface considering cutting vibration and probe posture

Hao,

Zhu,

Qin

et al. 2024

Int. J. Extrem. Manuf.

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On-machine inspection has a significant impact on improving high-precision and efficient machining of sculptured surfaces. Due to the lack of machining information and the inability to adapt the parameters to the dynamic cutting conditions, theoretical modeling of profile inspection usually leads to insufficient adaptation, which causes inaccuracy problems. To address the above issues, a novel coupled model for profile inspection is proposed by combining the theoretical model and the data-driven model. The key process is to first realize local feature extraction based on the acquired vibration signals. The hybrid sampling model, which fuses geometric feature terms and time-frequency domain feature terms, is modeled by the lever principle. Then, the weights of each feature term are adaptively assigned by a multi-objective multi-verse optimizer. Finally, an inspection error compensation model based on the attention mechanism considering different probe postures is proposed to reduce the impact of pre-travel and radius errors on inspection accuracy. The anisotropy of the probe system error and its influence mechanism on the inspection accuracy are analyzed quantitatively and qualitatively. Compared with the previous models, the proposed hybrid profile inspection model can significantly improve the accuracy and efficiency of on-machine sampling. The proposed compensation model is able to correct the inspection error with better accuracy. Simulations and experiments show the feasibility and validity of the proposed method. The proposed model and corresponding new findings contribute to high-precision and efficient on-machine inspection, and help to understand the coupling mechanism of inspection errors.

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A Machine Vision Development Framework for Product Appearance Quality Inspection

Cited by 3 publications

References 25 publications

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

A Common Knowledge-Driven Generic Vision Inspection Framework for Adaptation to Multiple Scenarios, Tasks, and Objects

Exploring the ViDiDetect Tool for Automated Defect Detection in Manufacturing with Machine Vision

On-machine inspection and compensation for thin-walled parts with sculptured surface considering cutting vibration and probe posture

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