Abrasion resistance of surface-modified steels used for artillery weapon barrels
A barrel is one of the most tribologically and thermally loaded parts of artillery weapons. The influence of the salt bath nitrocarburizing and hard-chrome plating of three steels used for artillery barrels on their abrasion resistance is studied in this paper. The tested steels are 42CrMo4, 30CrNiMo8, and 36CrNiMo4 in the initial quenched and tempered state. The test results from the ''dry sand/rubber wheel'' method, after 700 m of wear path, showed that the hard-chrome plating of 42CrMo4 and 30CrNiMo8 steels improved their abrasion resistance. In addition, reduced abrasive wear of 42CrMo4 steel samples was also achieved by nitrocarburizing. Hard chrome-plated samples exhibited good wear resistance due to their greater hardness and bigger thickness of the compound zone in comparison with that obtained on nitrocarburized samples.
Simulation Approach for Surface Roughness Interval Prediction in Finish Turning
Existing simulation models used in predicting the surface roughness of a workpiece in finish turning are based on an ideal circular cutting tool nose profile. This leads to a single predicted roughness value for a given set of input parameters. In this paper, a simulation approach that considers the random tool nose profile micro-deviations as well as the tool chatter vibration to predict a roughness interval is proposed. The nose profiles used in the simulation were extracted from images of the real cutting tool inserts using sub-pixel edge location. The chatter vibration signal was reconstructed from the measured signals and was superimposed onto the extracted nose profile. The roughness data were computed from 24 simulated workpiece surface profiles and used to determine the 95 % roughness prediction interval. Comparison with the experimental results showed that 100 %, 96 % and 96 % of the Rt, Ra and Rq roughness values obtained experimentally fell within the predicted roughness intervals. (Received in March 2015, accepted in September 2015. This paper was with the authors 1 month for 1 revision.)
Pre-evaluation on surface profile in turning process based on cutting parameters
Traditional online or in-process surface profile (quality) evaluation (prediction) needs to integrate cutting parameters and several in-process factors (vibration, machine dynamics, tool wear, etc.) for high accuracy. However, it might result in high measuring cost and complexity, and moreover, the surface profile (quality) evaluation result can only be obtained after machining process. In this paper, an approach for surface profile pre-evaluation (prediction) in turning process using cutting parameters and radial basis function (RBF) neural networks is presented. The aim was to only use three cutting parameters to predict surface profile before machining process for a fast pre-evaluation on surface quality under different cutting parameters. The input parameters of RBF networks are cutting speed, depth of cut, and feed rate. The output parameters are FFT vector of surface profile as prediction (pre-evaluation) result. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. It was found that a very good performance of surface profile prediction, in terms of agreement with experimental data, can be achieved before machining process with high accuracy, low cost, and high speed. Furthermore, a new group of training and testing data was also used to analyze the influence of tool wear on prediction accuracy.