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On Line Surface Roughness Measurement Using Labview And Vision Method For E Quality Control

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Conference

2010 Annual Conference & Exposition

Location

Louisville, Kentucky

Publication Date

June 20, 2010

Start Date

June 20, 2010

End Date

June 23, 2010

ISSN

2153-5965

Conference Session

NSF Grantees Poster Session

Page Count

13

Page Numbers

15.920.1 - 15.920.13

Permanent URL

https://peer.asee.org/16802

Download Count

715

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Paper Authors

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Richard Chiou Drexel University

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Michael Mauk Drexel University

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Yueh-Ting Yang Drexel University

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Robin Kizirian Drexel University

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Yongjin Kwon Ajou University

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Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

On-line Surface Roughness Measurement using LabVIEW and Vision Method for E-Quality Control

Abstract

The annual results of laboratory development under an NSF, CCLI sponsored project, “CCLI Phase II: E-Quality for Manufacturing (EQM) Integrated with Web-enabled Production Systems for Engineering Technology Education” (NSF Award # 0618665) is presented. This paper discusses an E-quality learning system developed to automatically measure and monitor the surface roughness of products by utilizing vision technology. Several methods have been developed to measure surface roughness in industry. These methods utilize a contact-based approach to perform the necessary measurements. Our system is developed based on a non-contact method that uses a smart machine vision camera and LabVIEW-based programming. The method for determining the roughness is based on the correlation of optical roughness parameters and the average surface roughness. After the surface roughness monitoring system has been built, it can be applied as an automated quality control system used for educational purposes. Students are able to inspect the pieces cut by CNC machines right after the lab. In addition, they are able to simulate the automatic quality control process which is utilized in the industry. All of the data that is fed back by the machine vision camera can be real-time monitored and recorded for statistical calculations and quality control. In order to introduce students to this emerging technology, the procedural steps are currently being worked out to introduce one or more undergraduate projects at sophomore and junior level engineering courses with a new system consisting of Digital Camera for Microscopes, LabVIEW, MATLAB and standard surface finish comparators.

Introduction

The Applied Engineering Technology (AET) program at Drexel University offers three concentrations: mechanical, industrial, and electrical. The degree program contains the traditional engineering science classes associated with the degree and/or concentrations, along with a large world-class engineering technology lab experience. These curricula also include a Computer Numerical Control (CNC) machining course. Manufacturing- related knowledge is also included in the Applied Engineering Technology program objectives and outcomes (thus important to the program’s ABET accreditation).

Students in the Applied Engineering Technology programs are required to complete a machining course MET 316 Computer Numerical Control that increases the student’s design-for-manufacturability (DFM) knowledge. Providing students with a hands-on approach when teaching machining classes in the ET curriculum enables students to become aware of how their design, dimensioning and tolerance calculating, and quality can drastically influence the downstream manufacturing processes. This is especially helpful for students in the mechanical and industrial concentrations as they have a high probability of designing parts that will require machining processes during their

Chiou, R., & Mauk, M., & Yang, Y., & Kizirian, R., & Kwon, Y. (2010, June), On Line Surface Roughness Measurement Using Labview And Vision Method For E Quality Control Paper presented at 2010 Annual Conference & Exposition, Louisville, Kentucky. https://peer.asee.org/16802

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