First Steps through Intelligent Grinding Using Machine Learning via Integrated Acoustic Emission Sensors

Abstract

The surface roughness of the ground parts is an essential factor in the assessment of the grinding process, and a crucial criterion in choosing the dressing and grinding tools and parameters. Additionally, the surface roughness directly influences the functionality of the workpiece. The application of artificial intelligence in the prediction of complex results of machining processes, such as surface roughness and cutting forces has increasingly become popular. This paper deals with the design of the appropriate artificial neural network for the prediction of the ground surface roughness and grinding forces, through an individual integrated acoustic emission (AE) sensor in the machine tool. Two models were trained and tested. Once using only the grinding parameters, and another with both acoustic emission signals and grinding parameters as input data. The recorded AE-signal was pre-processed, amplified and denoised. The feedforward neural network was chosen for the modeling with Bayesian backpropagation, and the model was tested by various experiments with different grinding and neural network parameters. It was found that the predictions presented by the achieved network parameters model agreed well with the experimental results with a superb accuracy of 99 percent. The results also showed that the AE signals act as an additional input parameter in addition to the grinding parameters, and could significantly increase the efficiency of the neural network in predicting the grinding forces and the surface roughness.

Selected aspects of diagnosing material removal machining processes state

“…On the basis of the literature review, the physical phenomena that are most often used as the source of diagnostic signals were distinguished. They are: temperature [26], vibrations [27], forces [28][29][30] and acoustic emission (AE) [31][32] or audible energy sound [33]. These are the values that indirectly an indicate the condition of the machining processes.…”

Electrical Discharge Machining of Oxide Nanocomposite: Nanomodification of Surface and Subsurface Layers

“…It is possible to select the optimal value of the effective values ratio of the signals and current automatically by varying the pulse duration during electrical discharge machining and monitoring the parameters of the recorded signals. Other parameters of the process can be varied in a similar way, including the value of the discharge, by maintaining the constancy of the ratio of useful energy to consumed energy [36,82,83]. It can be seen that, in the latter case, the signal becomes more complex during electrical discharge machining of the nanocomposites compared with other processing methods [7,23].…”

Prediction of Burr Types in Drilling of Al-7075 Using Acoustic Emission and Convolution Neural Networks

“…BP is a learning algorithm that uses the error signal of the output layer to change the connection strengths between the input/ hidden layer and the hidden/output layer by backpropagating the error signal to the hidden layer. ANNs (machining learning) with BP learning algorithm have been utilized in many precision manufacturing applications including AE monitoring [34]- [37].…”