Development and evaluation of non-contact automatic tool setting method for grinding internal screw threads

Wang, Yulin; Zhang, Chuanjian; He, Yan; Tao, Ling; Feng, Huanqing

doi:10.1007/s00170-018-2258-5

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…Linear Encoders are sensor which are available as incremental or absolute reader and encompasses various detection techniques, namely, Mechanical, Optical, Magnetic and Capacitive types [29]. Linear Interferometer is an instrument used for most precise distance measurement using optical interference fringes [30]. However, according to [29], the advancement in technology in the field of micro and nanotechnology opens up new applications in assembling and manipulating components; hence, verification of motion in micro/nano range becomes as important as its activation and measurement.…”

Section: Use Of Machine Learning In Quality Testingmentioning

confidence: 99%

Artificial Intelligence Based Quality Assurance of Surface Finish of Parts in Assembly Line

Akpan,

Effeng,

Orazulume

et al. 2023

ETJ

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Surface finish of machine parts is of considerable importance as it is included in the design and manufacturing of products to meet customers’ satisfaction. Above all it may be a major requirement for lapping, close and tight contact to prevent leakages or to minimize stress concentration. In light of the above a system was designed and the algorithm written in python programming language to determine the surface finish of parts in an assembly line. To realize this a dataset of 1800 images of metal surfaces was obtained. The defects were classified into six possible groups: patches, scratches, pitted, rolled and inclusion with 300 images of metal surfaces for each defect.. 1620 images were used in training set and 180 images in the test set. The images were trained and augmented so as to extract the textural features from the images. The images yielded to training. Training accuracy and error were obtained which validates the performance of the dataset during training. The model was also validated by testing it with a different dataset, and its performance established. The accuracy of the system was obtained by dividing the number of correct predictions by the total number of predictions made. The accuracy of the system was 98%, showing the efficiency of the system. It was also established that Artificial Intelligence based method of surface assurance, was more efficient than the contact process – which involve use of profilometer. The accuracy shows the level of conformance of the surface finish test of the parts during assembly line with the laid down specifications during product design. This system is recommended for commercialization and application in industries.

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Section: Use Of Machine Learning In Quality Testingmentioning

confidence: 99%

Artificial Intelligence Based Quality Assurance of Surface Finish of Parts in Assembly Line

Akpan,

Effeng,

Orazulume

et al. 2023

ETJ

View full text Add to dashboard Cite

show abstract

“…The tool setting problem has been intensively investigated [27,28] to improve machining precision. Typical contact-type tool setting methods include on-machine touch probes [28] and force sensors [29,30], whereas examples of non-contact methods include acoustic emission sensors [31], digital microscopes [32], and digital holography [33].…”

Section: Introductionmentioning

confidence: 99%

Wheel Setting Error Modeling and Compensation for Arc Envelope Grinding of Large-Aperture Aspherical Optics

Sun

Chen

et al. 2022

Chin. J. Mech. Eng.

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Precision grinding is a key process for realizing the use of large-aperture aspherical optical elements in laser nuclear fusion devices, large-aperture astronomical telescopes, and high-resolution space cameras. In this study, the arc envelope grinding process of large-aperture aspherical optics is investigated using a CM1500 precision grinding machine with a maximum machinable diameter of Φ1500 mm. The form error of the aspherical workpiece induced by wheel setting errors is analytically modeled for both parallel and cross grinding. Results show that the form error is more sensitive to the wheel setting error along the feed direction than that along the lateral direction. It is a bilinear function of the feed-direction wheel setting error and the distance to the optical axis. Based on the error function above, a method to determine the wheel setting error is proposed. Subsequently, grinding tests are performed with the wheels aligned accurately. Using a newly proposed partial error compensation method with an appropriate compensation factor, a form error of 3.4 μm peak-to-valley (PV) for a Φ400 mm elliptical K9 glass surface is achieved.

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“…Though the tool setting problem has been intensively studied duo to its significance (Maeng & Min, 2020;Y. Wang, Zhang, He, Tao, & Feng, 2018), only a few literature on wheel setting can be found for the grinding of aspheric optics.…”

Section: Introductionmentioning

confidence: 99%

Wheel Setting Error Modelling and Compensation for Arc Envelope Grinding of Large-aperture Aspherical Optics

Li¹,

Sun²,

Chen³

et al. 2021

Preprint

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Precision grinding is the key process to meet the heavy demands of large-aperture aspherical optical elements in the fields of laser nuclear fusion devices, large-aperture astronomical telescopes and high-resolution space cameras. In this study, the arc envelope grinding process of large-aperture aspherical optics was investigated using the CM1500 precision grinding machine with a maximum machinable diameter of Φ1500 mm. The analytical form error models of aspherical workpiece induced by wheel setting errors were established for both parallel grinding and cross grinding. Result show that the form error is much more sensitive to the wheel setting error along the feed direction than that along the lateral direction. It is a bilinear function of the feed-direction wheel setting error and the distance to optical axis. Based on the above analysis, a method to determine the wheel setting error is proposed. The grinding tests were then performed with the wheel accurately aligned. By using a newly proposed partial error compensation method with an appropriate compensation factor, a form error of 3.4 µm PV for a Φ400 mm elliptical K9 glass surface was achieved.

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Development and evaluation of non-contact automatic tool setting method for grinding internal screw threads

Cited by 8 publications

References 21 publications

Artificial Intelligence Based Quality Assurance of Surface Finish of Parts in Assembly Line

Artificial Intelligence Based Quality Assurance of Surface Finish of Parts in Assembly Line

Wheel Setting Error Modeling and Compensation for Arc Envelope Grinding of Large-Aperture Aspherical Optics

Wheel Setting Error Modelling and Compensation for Arc Envelope Grinding of Large-aperture Aspherical Optics

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