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

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.