Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera

Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation. The performance of a thermal error compensation system depends on the accuracy and robustness of the thermal error model and the quality of the inputs to the model. The location of temperature measurement must provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results. In this paper, a new intelligent compensation system for reducing thermal errors of machine tools using data obtained from a thermal imaging camera is introduced. Different groups of key temperature points were identified from thermal images using a novel schema based on a Grey model GM (0, N) and Fuzzy c-means (FCM) clustering method. An Adaptive Neuro-Fuzzy Inference System with Fuzzy c-means clustering (FCM-ANFIS) was employed to design the thermal prediction model. In order to optimise the approach, a parametric study was carried out by changing the number of inputs and number of membership functions to the FCM-ANFIS model, and comparing the relative robustness of the designs. According to the results, the FCM-ANFIS model with four inputs and six membership functions achieves the best performance in terms of the accuracy of its predictive ability. The residual value of the model is smaller than ± 2 μm, which represents a 95% reduction in the thermally-induced error on the machine. Finally, the proposed method is shown to compare favourably against an Artificial Neural Network (ANN) model

Measuring the Score Matching of the Pairwise Deoxyribonucleic Acid Sequencing using Neuro-Fuzzy

The proposed model for getting the score matching of the deoxyribonucleic acid (DNA) sequence is introduced; the Neuro-Fuzzy procedure is the strategy actualized in this paper; it is used the collection of biological information of the DNA sequence performing with global and local calculations so as to advance the ideal arrangement; we utilize the pairwise DNA sequence alignment to gauge the score of the likeness, which depend on information gathering from the pairwise DNA series to be embedded into the implicit framework; an adaptive neuro-fuzzy inference system model is reasonable for foreseeing the matching score through the preparation and testing in neural system and the induction fuzzy system in fuzzy logic that accomplishes the outcome in elite execution