Application and Upgrading of On-line Monitoring System for Measurement and Processing of Electric Signals at Arc Stud Welding Process
As the application of on-line monitoring system is shown to be a good solution during monitoring of main welding parameters at different welding processes, this paper describes application and upgrade of on-line monitoring system developed at Mechanical Engineering Faculty in Slavonski Brod. Experimental part of the paper shows application of on-line monitoring system during welding of two types of studs with application of activating flux for ATIG process (VS-2E) on the base metal surface. Monitoring of main welding parameters (welding current and voltage) was conducted during welding of studs usually used in steam boiler production (stud diameter: 10 mm), and welding voltage was monitored on the studs (shear stud) of 22 mm diameters used in bridge construction. The goal of monitoring and analyse of main welding parameters is to evaluate the connection of electric arc parameters variations with the presence of activating flux.
On-line monitoring system - an application for monitoring key welding parameters of different welding processes
This paper describes the application of an on-line system for the monitoring, aquisition and processing of key welding parameters. The on-line monitoring system has been succesfully applied in practice to measure key welding parameters for different welding processes. relevant examples of the information that can be obtained from the system during a welding cycle are presented.
Surface roughness monitoring by singular spectrum analysis of vibration signals
Abstract This study assessed two methods for enhanced surface roughness (Ra) monitoring based on the application of singular spectrum analysis (SSA) to vibrations signals generated in workpiece-cutting tool interaction in CNC finish turning operations i.e., the individual analysis of principal components (I-SSA), and the grouping analysis of correlated principal components (G-SSA). Singular spectrum analysis is a non-parametric technique of time series analysis that decomposes a signal into a set of independent additive time series referred to as principal components. A number of experiments with different cutting conditions were performed to assess surface roughness monitoring using both of these methods. The results show that singular spectrum analysis of vibration signal processing discriminated the frequency ranges effective for predicting surface roughness. Grouping analysis of correlated principal components (G-SSA) proved to be the most efficient method for monitoring surface roughness, with optimum prediction and reliability results at a lower analytical-computational cost. Finally, the results show that singular spectrum analysis is an ideal method for analyzing vibration signals applied to the on-line monitoring of surface roughness.