An Approach to Improved CNC Machining Using Vision-Based System

Al-Kindi, Ghassan; Zughaer, Hussien

doi:10.1080/10426914.2011.648249

Cited by 31 publications

(11 citation statements)

References 15 publications

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“…However, the system to create speckle pattern is costly and less useful for on-machine tool inspection of turned surfaces. Al-kindi and Zughaer [5] performed on-machine tool surface roughness evaluation of milled specimens using histogram based first order statistical texture analysis. However, the change of illumination is severely affecting the robustness of first order statistical texture analysis according to Elango and Karunamoorthy [6].…”

Section: Introductionmentioning

confidence: 99%

On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression

Dutta

Pal

Sen

2016

Precision Engineering

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Section: Introductionmentioning

confidence: 99%

On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression

Dutta

Pal

Sen

2016

Precision Engineering

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“…Klancnik and Senveter 17 developed a numerical control system for optical determination of workpiece origin using a QuickCam camera. Al-Kindi and Zughaer 18 provided a new CNC system integrated with vision-based feedback control. This was used for surface roughness measurement to improve the precision of manufactured parts.…”

Section: Introductionmentioning

confidence: 99%

“…Few studies have been conducted on direct detection of machining errors during the machining process. Furthermore, although machine vision has been introduced to machine tools, most studies mainly focused on monitoring and compensation of tool wear in curve grinding 7 and surface quality, 18 or evaluating the performance of machine tools. 19 On-machine visual detection of contour errors is still a big challenge for profile grinding of contour surfaces.…”

Section: Introductionmentioning

confidence: 99%

A vision-based processing methodology for profile grinding of contour surfaces

Fei-ya

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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On-machine direct detection of profile errors is vital to improve accuracy and efficiency in profile grinding. However, achieving such detection processes is difficult because of harsh machining conditions. This study presents a novel machine-vision-based processing methodology for the profile grinding of contour surfaces instead of the traditional optical-enlargement-based profile grinding which is manual dependent and low efficient. Grinding errors were efficiently detected online through machine vision. A specific vision system was coordinately designed with the profile grinding system to ensure distortionless measurement of workpiece contour and overcome the interferences of machining environment during profile grinding. A machining error detection principle was proposed based on the online captured workpiece contour image. Real-time error identification and compensation algorithms were developed through the synthetic error measurement. Simulations and experiments were conducted successively. The results indicated that profile errors were considerably reduced and measurement efficiency was improved, validating the effectiveness of the proposed methodology for profile grinding of contour surface. The findings can also provide a reference for the direct measurement of machining errors in other machines.

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“…The goal of the proposed controller is to obtain the required torques, thereby ensuring correct tracking of the desired image trajectory during the machining task. The control input r q   can be obtained from (6) as: (8) where r s  is the resolved acceleration in terms of extracted image features, and J + denotes the Moore-Penrose pseudoinverse of J. Two different tasks are defined as follows:…”

Section: Visual Control Of the 4-axis Robot Toolmentioning

confidence: 99%

“…Only few works have dealt with the introduction of vision in CNC machining processes. In [8], it is presented an approach to improve the performance of CNC machining by utilizing on-line vision-based monitoring. A stereo camera configuration is employed to monitor the milling task, but there is no direct control of the position using the vision system.…”

Section: Introductionmentioning

confidence: 99%

Dynamic visual servo control of a 4-axis joint tool to track image trajectories during machining complex shapes

Pomares

Perea

Jara

et al. 2013

Robotics and Computer-Integrated Manufacturing

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A large part of the new generation of computer numerical control systems has adopted an architecture based on robotic systems. This architecture improves the implementation of many manufacturing processes in terms of flexibility, efficiency, accuracy and velocity. This paper presents a 4-axis robot tool based on a joint structure whose primary use is to perform complex machining shapes in some non-contact processes. A new dynamic visual controller is proposed in order to control the 4-axis joint structure, where image information is used in the control loop to guide the robot tool in the machining task. In addition, this controller eliminates the chaotic joint behaviour which appears during tracking of the quasi-repetitive trajectories required in machining processes. Moreover, this robot tool can be coupled to a manipulator robot in order to form a multi-robot platform for complex manufacturing tasks. Therefore, the robot tool could perform a machining task using a piece grasped from the workspace by a manipulator robot. This manipulator robot could be guided by using visual information given by the robot tool, thereby obtaining an intelligent multi-robot platform controlled by only one camera.

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An Approach to Improved CNC Machining Using Vision-Based System

Cited by 31 publications

References 15 publications

On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression

On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression

A vision-based processing methodology for profile grinding of contour surfaces

Dynamic visual servo control of a 4-axis joint tool to track image trajectories during machining complex shapes

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