Surface Roughness Measurement of Parts Manufactured by FDM Process using Light Sectioning Vision System

2016 ◽  
Vol 99 (4) ◽  
pp. 429-433 ◽  
Author(s):  
A. S. Kelkar ◽  
N. N. Kumbhar ◽  
A. V. Mulay
2018 ◽  
Vol 144 ◽  
pp. 03008
Author(s):  
Ravi Kiran ◽  
H. J. Amarendra ◽  
Shashank Lingappa

Measurement of surface roughness is one of the quality control processes, usually carried out off line. Contact type surface roughness measurement method is commonly used in quality control. The processes consume lot of time with human interaction. In order to reduce or to eliminate non value added time, effective quality inspection tool and automation of the processes has to be utilized. An attempt has been made to automate the process with integration of vision camera in capturing the image of the component surface. The image process technique has the advantage of analyzing the single captured image for multiple area measurement. Hence, the in-line quality control of each component surface roughness measurement is ensured. The automation process involves component movement, image capturing, image processing, and decision making, using sensors, actuators and microcontroller. The proposed in-line quality control of surface roughness with vision system has been successfully developed. The designed automated system has fulfilled the objectives in respect of the scope of the present work.


Author(s):  
Richard Y. Chiou ◽  
Yongjin James Kwon ◽  
Yueh-Ting Yang ◽  
Robin Kizirian ◽  
Tzu-Liang Bill Tseng

This paper presents a new method of surface roughness measurement developed for use in a web-enabled production environment. This method employs an Internet-based vision system to measure and analyze the pattern of scattered light from a surface of an object to derive roughness parameters. The roughness parameters are obtained for a number of metal specimens which are machined to different roughnesses. A correlation curve is established between optical intensity and the corresponding average surface roughness. The quality measurement data is monitored through the web browser while surface roughness parameters are inspected using a machine vision system. This allows the system to operate over the Internet since the machine vision system is integrated with LabVIEW-based graphic user interface (GUI).


2017 ◽  
Vol 137 (3) ◽  
pp. 147-152 ◽  
Author(s):  
Tetsuo Fukuchi ◽  
Norikazu Fuse ◽  
Mitsutoshi Okada ◽  
Tomoharu Fujii ◽  
Maya Mizuno ◽  
...  

2013 ◽  
Vol 465-466 ◽  
pp. 764-768 ◽  
Author(s):  
Tanel Aruväli ◽  
Tauno Otto

The paper investigates in-process signal usage in turning for indirect surface roughness measurement. Based on theoretical surface roughness value and in-process signal, a model is proposed for surface roughness evaluation. Time surface roughness and in-process signal surface roughness correlation based analysis is performed to characterize tool wear component behavior among others. Influencing parameters are grouped based on their behavior in time. Moreover, Digital Object Memory based solution and algorithm is proposed to automate indirect surface roughness measurement process.


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