The paper presents a research on machined surface roughness in face milling of aluminium alloy on a low power cutting machine. Based on the results of the central-composite plan of experiment with varying machining parameters (number of revolutions -spindle speed n, feed rate f and depth of cut a) and the roughness observed as output variable, two models have been developed: a regression model and a model based on the application of neural networks (NN model). The regression model (coefficient of determination of 0.965 or 0.952 adjusted) with insignificant lack of fit, provides a very good fit and can be used to predict roughness throughout the region of experimentation. Likewise, the model based on the application of neural networks approximates well the experimental results with the level of RMS (Root Mean Square) error in the phase of validation of 4.01 %.

Researches on machined surface roughness prediction in the face milling process of steel are presented in the paper. The data for modelling by the application of neural networks have been collected by the central composite design of experiment. Input variables are the parameters of machining (number of revolutions -cutting speed, feed and depth of cut) and the way of cooling, while the machined surface roughness is output variable.

No abstract

The paper gives an account of the machined surface roughness investigation based on the features of a digital image taken subsequent to the technological operation of milling of aluminium alloy Al6060. The data used for investigation were obtained by mixed-level factorial design with two replicates. Input variables (factors) are represented by the face milling basic machining parameters: spindle speed (at five levels: 2000; 3500; 5000; 6500; 8000 rev/min, respectively), feed per tooth (at six levels: 0.025; 0.1; 0.175; 0.25; 0.325; 0.4 mm/tooth, respectively) and depth of cut (at two levels: 1; 2 mm, respectively). Output variable or response is the most frequently used surface roughness parameter -arithmetic average of the roughness profile, Ra. Digital image of the machined surface is provided for every test sample. Based on experimental design and obtained results of roughness measuring, a base has been created of input data (features) extracted from digital images of the samples' machined surfaces. This base was later used for generating the fuzzy inference system for prediction of the surface roughness using the adaptive neuro-fuzzy inference system (ANFIS). Assessing error, i.e. comparison of the assessed value Ra provided by the system with real Ra values, is expressed with the normalized root mean square error (NRMSE) and it is 0.0698 (6.98 %).

An approach to solving the problem of machining time estimation in production of complex products within CNC machining systems is presented in the paper. Heuristic analysis of the process is used to define the attributes of influence to machining time. For the problem of estimating machining time the following "Neural Computing techniques" are used: Back-Propagation Neural Network, Modular Neural Network, Radial Basis Function Neural Network, General Regression Neural Network and Self-Organizing Map Neural Network. Real data from the technological process obtained by measuring are used to design the model used in investigation. The established model is used to carry out the investigation aimed at learning and testing different algorithms of neural networks and the results are given by the RMS error. The best results in the validation phase were achieved by Modular Neural Network (RMSE: 1.89 %) and Back-Propagation Neural Network (RMSE: 2.03 %) while the worst results were achieved by Self-Organizing Map Neural Network (RMSE: 10.05 %).

No abstract

Presented in this paper is the theoretical background for a novel method for determination of kinetic friction coefficient. The method is based on the equation of movement of a rigid body along an inclined plane and has not been previously discussed in literature. According to the theoretical results presented in this paper, mean kinetic friction coefficient can be determined based on the time it takes a rigid body to travel a distance along an inclined plane. Experimental results show that the mean kinetic friction coefficient increases with increasing mean sliding velocity, i.e. with decreasing sliding time. Increasing sliding velocity increases deviation of friction coefficients. This method also allows the determination of energy consumed due to friction along the travelled distance. Increasing mean kinetic friction coefficient and mean sliding velocity increases energy consumed due to friction. Practical calculation requires application of the distance law, which, in turn, requires accurate measurement of the sliding time and distance.

No abstract