Surface roughness is an important quality in manufacturing, as it affects the product’s tribological, frictional and assembly characteristics. Turning stainless steel at low cutting speeds may result in a rougher surface due to built up edge formation, where as speed increases the surface roughness improves, due to the low contact time between the chip and the tool to allow bonding to occur.However, this increase in cutting speed produces higher tool wear rates, which increases the machining costs. Previous studies have indicated that savings in cost and manufacturing time are obtained when predicting the surface roughness, prior to the machining process. In this paper, experimental data are used to develop prediction models using Multiple Linear Regression and Artificial Neural Network methodologies. Results show that the neural network outperforms the linear model by a fair margin (1400%). Moreover, the developed Artificial Neural Network model has been integrated with an optimisation algorithm, known as Simulated Annealing (SA),this is done in order to obtain a set of cutting parameters that result in low surface roughness. A low value of surface roughness and the set of parameters resulting on it, are successfully yielded by the SA algorithm
This version is available at https://strathprints.strath.ac.uk/49507/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract. A surface engineering technique based on a Tungsten Inert Gas (TIG) torch was used to melt single tracks on the surface of a micro-alloyed steel with a hardness of 150 HV. The influence of three shielding gases, argon, helium and nitrogen, on the microstructure and hardness of the resolidified surfaces was analyzed. Effect of Shielding Gas on the Properties andIn all melting techniques, the heat generated by the source is normally conducted to the substrate ahead of the torch, and has been described as 'preheat'. This leads to a gradually higher substrate temperature, from the start to the finish of a melted surface track. The aim of this research was to analyze any inhomogeneities in the microstructure, due to 'preheat', which is rarely considered in the published literature. Three thermocouples were located along the melted track in order to record the temperature at three different points. An energy input of ~ 840J/mm was used in each experiment and the results show that the maximum temperature recorded by the last thermocouple, N o three (subjected to the preheat), for argon, helium and nitrogen gas was 590 ºC, 1120º C and 740 ºC respectively, where a difference of 150 ºC and 200 ºC was registered between the first and third thermocouples when using helium and nitrogen respectively. The corresponding hardness values were 170 HV, 162 HV and 225 HV, and the corresponding surface roughness values were 6 µm, 12 µm and 25µm. A decrease of almost 60% in the roughness value was observed between the initial and last stage of the melted track, when using argon as shielding gas.
The efficiency of a machining process can be measured by evaluating the quality of the machined surface and the tool wear rate. The research reported herein is mainly focused on the effect of cutting parameters and tool wear on the machined surface defects, surface roughness, deformation layer and residual stresses when dry milling Stellite 6, deposited by overlay on a carbon steel surface. The results showed that under the selected cutting conditions, abrasion, diffusion, peeling, chipping and breakage were the main tool wear mechanisms presented. Also the feed rate was the primary factor affecting the tool wear with an influence of 83%. With regard to the influence of cutting parameters on the surface roughness, the primary factors were feed rate and cutting speed with 57 and 38%, respectively. In addition, in general, as tool wear increased, the surface roughness increased and the deformation layer was found to be influenced more by the cutting parameters rather than the tool wear. Compressive residual stresses were observed in the un-machined surface, and when machining longer than 5 min, residual stress changed 100% from compression to tension. Finally, results showed that micro-crack initiation was the main mechanism for chip formation.
, T. (2017). Effect of shielding gas and energy input rate on the surface geometry and microstructure of a microalloyed steel surface melted with a TIG torch. Advances in Materials and Processing Technologies.
The paper presents new results of automated path planning for an industrial robot manipulator performing microplasma spraying of coatings on substrates with complex surface shapes. Path planning and automatic generation of the manipulator motion program are performed using data of a preliminary 3D surface scanning by a laser triangulation distance sensor installed on the same robot arm. The automatic manipulator working tool path planning algorithm is based on the choice of the starting segment of the working tool trace as a geodetic line on the surface. An algorithm for optimal spatial curve approximation by a sequence of line segments and arcs has been developed as a part of the automatic manipulator program generation system. The developed algorithms and their software implementation were experimentally tested through robotic microplasma spraying of a protective coating on the surface of a jaw crusher plate, which was then successfully operated for crushing mineral raw materials.
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