Automatic assembly quality inspection based on an unsupervised point cloud domain adaptation model

Zhu, Xiaomeng; Manamasa, Himaja; Sánchez, Juan Luis Jiménez; Maki, Atsuto; Hanson, Lars

doi:10.1016/j.procir.2021.11.304

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…Vision techniques can be used to inspect manufactured products [87]. Three-dimensional vision techniques are also useful in inspection environments, such as the analysis of the quality of an assembly process [88]. Vision techniques can be combined with sound measurements using acoustic cameras, imaging devices with an included microphone array.…”

Section: Types Of Sensors and Variables Measuredmentioning

confidence: 99%

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

Tsanousa

Bektsis

Kyriakopoulos

et al. 2022

Sensors

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Manufacturing companies increasingly become “smarter” as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.

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Section: Types Of Sensors and Variables Measuredmentioning

confidence: 99%

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

Tsanousa

Bektsis

Kyriakopoulos

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The utilization of computer technology and 3D measurement technology has supplanted conventional approaches, such as outbound quality checking, overall deformation monitoring, and preassembly of steel structures. The execution of these duties has transitioned to a digital format facilitated by point clouds [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Extraction of Feature Information from Point Cloud of Large Volume Steel Truss Members

Liang,

Zhang,

Guo

et al. 2024

Advances in Civil Engineering

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In order to address the issues pertaining to the subjective nature and limited precision associated with selecting feature points in the point cloud of a large steel truss structure, this study proposes a batch automatic extraction approach for identifying key feature information, including boundaries, corner points, and bolt holes of large steel truss components. This method relies on the nested application of established processing algorithms such as Euclidean clusters, regional growth clusters, and random sampling consensus. In addition, a novel approach is suggested for validating the precision of feature information extraction through the utilization of standard theoretical models. The results of the experimental and large-scale lower chord tests demonstrate that our approach is not dependent on specialized software, exhibits excellent efficiency, and possesses an acceptable degree of automation. The findings of this study can provide accurate data support for reverse modeling, virtual trial assembly, and dimensional inspection of steel truss components.

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Automated assembly quality inspection by deep learning with 2D and 3D synthetic CAD data

Zhu,

Mårtensson,

Hanson

et al. 2024

J Intell Manuf

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In the manufacturing industry, automatic quality inspections can lead to improved product quality and productivity. Deep learning-based computer vision technologies, with their superior performance in many applications, can be a possible solution for automatic quality inspections. However, collecting a large amount of annotated training data for deep learning is expensive and time-consuming, especially for processes involving various products and human activities such as assembly. To address this challenge, we propose a method for automated assembly quality inspection using synthetic data generated from computer-aided design (CAD) models. The method involves two steps: automatic data generation and model implementation. In the first step, we generate synthetic data in two formats: two-dimensional (2D) images and three-dimensional (3D) point clouds. In the second step, we apply different state-of-the-art deep learning approaches to the data for quality inspection, including unsupervised domain adaptation, i.e., a method of adapting models across different data distributions, and transfer learning, which transfers knowledge between related tasks. We evaluate the methods in a case study of pedal car front-wheel assembly quality inspection to identify the possible optimal approach for assembly quality inspection. Our results show that the method using Transfer Learning on 2D synthetic images achieves superior performance compared with others. Specifically, it attained 95% accuracy through fine-tuning with only five annotated real images per class. With promising results, our method may be suggested for other similar quality inspection use cases. By utilizing synthetic CAD data, our method reduces the need for manual data collection and annotation. Furthermore, our method performs well on test data with different backgrounds, making it suitable for different manufacturing environments.

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Automatic assembly quality inspection based on an unsupervised point cloud domain adaptation model

Cited by 6 publications

References 15 publications

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends

Extraction of Feature Information from Point Cloud of Large Volume Steel Truss Members

Automated assembly quality inspection by deep learning with 2D and 3D synthetic CAD data

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