A new approach for dynamic modelling of energy consumption in the grinding process using recurrent neural networks

Abstract

Grinding is a critical machining process because it produces parts of high precision and high surface quality. Due to the semi-artisan production of the wheel, it is not possible to know in advance the performance of the wheel. One of the most useful parameters to characterize the grinding process is the specific grinding energy, which varies with the wear of the grinding wheel during its lifecycle. Thus, it would be useful to model the specific grinding energy in order to get information about the performance of the wheel before buying it. Unlike the typical applications of time series forecasting, in this work, a totally different issue is presented: the prediction of new and complete time series bounded in time without initial or historic values. In this context, an analysis of the effect of the time characteristics and the number of points of the time series on the prediction capabilities of the ANN is presented. The results of the analysis show that 200 points are enough to predict a complete time series up to 2000 mm 3 /mm of specific volume of material removed. Actually, it is shown that modelling the evolution of the grinding specific energy up to 2000 mm 3 /mm is possible. The net shows good capability to generalize to new grinding conditions, with errors below 23.65 %, and to new wheel characteristics, with errors below 20.01 %, which are satisfactory from the grinding process perspective.

Estimation of lactate threshold with machine learning techniques in recreational runners

“…Probably, one of the main reasons for the bad generalization capabilities of the model is the lack of input data which could explain the inter-individual variability of the glucose metabolism. In this regard, Arriandiaga et al [33] proposed an approach to deal with this type of problems. They proposed a novel methodology to estimate complete time series in a specific time interval from multiple and distinct input time-series.…”

“…In this case, as represented in figure 8, a layer-recurrent neural network (LRNN) architecture has been selected. The LRNN is an Elman-inspired recurrent neural network which has flexibility to configure the number of hidden layers and the transfer function of each layer [33].…”

Nonlinear Autoregressive Network with the Use of a Moving Average Method for Forecasting Typhoon Tracks

“…Relatively, the time factor is explicit represented in the dynamic neural network model, by using feedback loop to cause time delays. Further to mention that the dynamic neural network is not only treat nonlinear multivariate behaviour, but also include learning of time-dependent behaviour such as various transient phenomena and delay effects [21][22][23][24][25]. Nevertheless, both neural network models can be applied to analyse the present research topic in typhoon path prediction problem.…”

Multi-Objective Parameter Optimization Dynamic Model of Grinding Processes for Promoting Low-Carbon and Low-Cost Production

“…The high-speed rotation of the grinding wheel and the long processing time can lead to large carbon emissions from the machine tool, and this can be accompanied by a high processing cost and greater use of cutting fluid in the process. According to the analysis of the energy consumption of the grinding process [1][2][3] and the mathematical model of CNC (computer numerical control machine tools) [4][5][6][7][8][9], a grinding parameter optimization model based on carbon emissions and cutting costs is established.…”