Improving machined surface texture in avoiding five-axis singularity with the acceptable-texture orientation region concept

Lin, Zhiwei; Fu, Jianzhong; Shen, Hongyao; Xu, Guangyue; Sun, Yuan

doi:10.1016/j.ijmachtools.2016.05.006

Cited by 22 publications

(4 citation statements)

References 14 publications

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“… Feature extraction: Relevant features are extracted from the segmented regions or the entire image. These features can include geometric characteristics (e.g., shape, size, or aspect ratio), texture patterns, intensity profiles, or statistical measures [ 87 , 88 ]. Classification: ML algorithms or pattern recognition techniques can classify the extracted features and distinguish between normal and defective parts.…”

Section: Image-based Fault Monitoringmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Image-Based Fault Monitoring in Additive Manufacturing: Recent Developments and Future Directions

Kim,

Abisado,

Villaverde

et al. 2023

Sensors

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Additive manufacturing (AM) has emerged as a transformative technology for various industries, enabling the production of complex and customized parts. However, ensuring the quality and reliability of AM parts remains a critical challenge. Thus, image-based fault monitoring has gained significant attention as an efficient approach for detecting and classifying faults in AM processes. This paper presents a comprehensive survey of image-based fault monitoring in AM, focusing on recent developments and future directions. Specifically, the proponents garnered relevant papers from 2019 to 2023, gathering a total of 53 papers. This paper discusses the essential techniques, methodologies, and algorithms employed in image-based fault monitoring. Furthermore, recent developments are explored such as the use of novel image acquisition techniques, algorithms, and methods. In this paper, insights into future directions are provided, such as the need for more robust image processing algorithms, efficient data acquisition and analysis methods, standardized benchmarks and datasets, and more research in fault monitoring. By addressing these challenges and pursuing future directions, image-based fault monitoring in AM can be enhanced, improving quality control, process optimization, and overall manufacturing reliability.

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Section: Image-based Fault Monitoringmentioning

confidence: 99%

Section: Image-based Fault Monitoringmentioning

confidence: 99%

A Survey of Image-Based Fault Monitoring in Additive Manufacturing: Recent Developments and Future Directions

Kim,

Abisado,

Villaverde

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…It does not use color or brightness to reflect the homogeneity of images. It represents important information about the arrangement of the surface structures and their relationships with their surroundings [118,119].…”

Section: Classificationmentioning

confidence: 99%

State of the Art in Defect Detection Based on Machine Vision

Ren

Fang

Yan

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

356

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Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

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“…In the last two decades, intensive investigations have been carried out towards non-singular tool path generation of the conventional A/C type 5-axis machines where the singularity axis is fixed in space. Various concepts have been proposed for visually and quantitatively defining and detecting the singular domain, for instance, singular cone [7], singular ring [8,9], admissible orientation domain [10] and acceptable texture orientation region [11] in 2/3D representations. From a kinematic viewpoint, the methods in dealing with singularity problem can be roughly classified into two categories that can be performed in either the Cartesian space or the joint space.…”

Section: Introductionmentioning

confidence: 99%

Inverse kinematics of a 5-axis hybrid robot with non-singular tool path generation

Liu

Huang

2019

Robotics and Computer-Integrated Manufacturing

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Improving machined surface texture in avoiding five-axis singularity with the acceptable-texture orientation region concept

Cited by 22 publications

References 14 publications

A Survey of Image-Based Fault Monitoring in Additive Manufacturing: Recent Developments and Future Directions

A Survey of Image-Based Fault Monitoring in Additive Manufacturing: Recent Developments and Future Directions

State of the Art in Defect Detection Based on Machine Vision

Inverse kinematics of a 5-axis hybrid robot with non-singular tool path generation

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