The application of a two-step AI model to an automated pneumatic drilling process

Abstract

Real-world processes may be improved through a combination of artificial intelligence and identification techniques. This work presents a multidisciplinary study that identifies and applies unsupervised connectionist models in conjunction with modelling systems. This particular industrial problem is defined by a data set relayed through sensors situated on a robotic drill used in the construction of industrial storage centres. The first step entails determination of the most relevant structures in the data set with the application of the connectionist architectures. The second step combines the results of the first one to identify a model for the optimal working conditions of the drilling robot that is based on low-order models such as black box that approximate the optimal form of the model. Finally, it is shown that the most appropriate model to control these industrial tasks is the Box-Jenkins algorithm, which calculates the function of a linear system from its input and output samples.

A novel hybrid intelligent system for multi-objective machine parameter optimization

“…The goal is to obtain a model that meets the following premise [6]: a good model is one that makes good predictions and produces small errors when the observed data is applied. Classic SI refers to the parametrical literature, which has its origin in linear system analysis [7]. Nevertheless, increased computational capability and the availability of soft computing techniques have widened research into SI.…”

“…When using ANN, the purpose of an identification process is to determine the weight matrix based on the observations Z t , so as to obtain the relationships between the network nodes. The SI procedure comprises several steps: the selection of the models and their structure, the learning methods, the identification and optimization criteria and the validation method [6,7,46,47]. Validation ensures that the selected model meets the necessary conditions for estimation and prediction.…”

“…Validation ensures that the selected model meets the necessary conditions for estimation and prediction. Typically, validation is carried out using three different methods: the residual analysis eðt;ĥðtÞÞ (by means of a correlation test between inputs, their residuals and their combinations), the mean squared error (MSE) and the generalization error value [normalized sum of squared errors (NSSE)], and finally a graphical comparison between the desired outputs and the model outcomes through simulation [7,20].…”

“…System identification [4][5][6] has made it possible to model, simulate and predict the behavior of many industrial applications successfully and in different areas, such as control, robotics [7], energy processes [8], milling machine [9], high precision [1], power system security [10], etc. A novel and economically advantageous application is the optimization process in the field of Medical Therapeutics (Odonto-Stomatology), a booming industry [9,[11][12][13].…”

A Hybrid System for Dental Milling Parameters Optimisation

“…Classic SI refers to the parametrical literature, which has its origin in the linear system analysis [20]. Nevertheless, increased computational capability and the availability of soft computing techniques have widened research into SI.…”