The Application of ANN and ANFIS Prediction Models for Thermal Error Compensation on CNC Machine Tools

Thermal errors can have significant effects on Computer Numerical Control (CNC) machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This thesis first reviews different methods of designing thermal error models, before concentrating on employing Artificial Intelligence (AI) methods to design different thermal prediction models. In this research work the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as the backbone for thermal error modelling. The choice of inputs to the thermal model is a non-trivial decision which is ultimately a compromise between the ability to obtain data that sufficiently correlates with the thermal distortion and the cost of implementation of the necessary feedback sensors. In this thesis, temperature measurement was supplemented by direct distortion measurement at accessible locations. The location of temperature measurement must also 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 thesis, a new intelligent system for reducing thermal errors of machine tools using data obtained from thermography data is introduced. Different groups of key temperature points on a machine can be identified from thermal images using a novel schema based on a Grey system theory and Fuzzy C-Means (FCM) clustering method. This novel method simplifies the modelling process, enhances the accuracy of the system and reduces the overall number of inputs to the model, since otherwise a much larger number of thermal sensors would be required to cover the entire structure. An Adaptive Neuro-Fuzzy Inference System with Fuzzy C-Means clustering (ANFIS-FCM) is then employed to design the thermal prediction model. In order to optimise the approach, a parametric study is carried out by changing the number of inputs and number of Membership Functions (MFs) to the ANFIS-FCM model, and comparing the relative robustness of the designs. The proposed approach has been validated on three different machine tools under different operation conditions. Thus the proposed system has been shown to be robust to different internal heat sources, ambient changes and is easily extensible to other CNC machine tools. Finally, the proposed method is shown to compare favourably against alternative approaches such as an Artificial Neural Network (ANN) model and different Grey models

Efficient estimation by FEA of machine tool distortion due to environmental temperature perturbations

Machine tools are susceptible to exogenous influences, which mainly derive from varying environmental conditions such as the day and night or seasonal transitions during which large temperature swings can occur. Thermal gradients cause heat to flow through the machine structure and results in non-linear structural deformation whether the machine is in operation or in a static mode. These environmentally stimulated deformations combine with the effects of any internally generated heat and can result in significant error increase if a machine tool is operated for long term regimes. In most engineering industries, environmental testing is often avoided due to the associated extensive machine downtime required to map empirically the thermal relationship and the associated cost to production. This paper presents a novel offline thermal error modelling methodology using finite element analysis (FEA) which significantly reduces the machine downtime required to establish the thermal response. It also describes the strategies required to calibrate the model using efficient on-machine measurement strategies. The technique is to create an FEA model of the machine followed by the application of the proposed methodology in which initial thermal states of the real machine and the simulated machine model are matched. An added benefit is that the method determines the minimum experimental testing time required on a machine; production management is then fully informed of the cost-to-production of establishing this important accuracy parameter. The most significant contribution of this work is presented in a typical case study; thermal model calibration is reduced from a fortnight to a few hours. The validation work has been carried out over a period of over a year to establish robustness to overall seasonal changes and the distinctly different daily changes at varying times of year. Samples of this data are presented that show that the FEA-based method correlated well with the experimental results resulting in the residual errors of less than 12 μm

A novel haptic model and environment for maxillofacial surgical operation planning and manipulation

This paper presents a practical method and a new haptic model to support manipulations of bones and their segments during the planning of a surgical operation in a virtual environment using a haptic interface. To perform an effective dental surgery it is important to have all the operation related information of the patient available beforehand in order to plan the operation and avoid any complications. A haptic interface with a virtual and accurate patient model to support the planning of bone cuts is therefore critical, useful and necessary for the surgeons. The system proposed uses DICOM images taken from a digital tomography scanner and creates a mesh model of the filtered skull, from which the jaw bone can be isolated for further use. A novel solution for cutting the bones has been developed and it uses the haptic tool to determine and define the bone-cutting plane in the bone, and this new approach creates three new meshes of the original model. Using this approach the computational power is optimized and a real time feedback can be achieved during all bone manipulations. During the movement of the mesh cutting, a novel friction profile is predefined in the haptical system to simulate the force feedback feel of different densities in the bone

Temperature-Sensitive Point Selection and Thermal Error Model Adaptive Update Method of CNC Machine Tools

The thermal error of CNC machine tools can be reduced by compensation, where a thermal error model is required to provide compensation values. The thermal error model adaptive update method can correct the thermal error model by supplementing new data, which fundamentally solves the problem of model robustness. Certain problems associated with this method in temperature-sensitive point (TSP) selection and model update algorithms are investigated in this study. It was found that when the TSPs were selected frequently, the selection results may be different, that is, there was a variability problem in TSPs. Further, it was found that the variability of TSPs is mainly due to some problems with the TSP selection method, (1) the conflict between the collinearity among TSPs and the correlation of TSPs with thermal error is ignored, (2) the stability of the correlation is not considered. Then, a stable TSP selection method that can choose more stable TSPs with less variability was proposed. For the model update algorithm, this study proposed a novel regression algorithm which could effectively combine the new data with the old model. It has advantages for a model update, (1) fewer data are needed for the model update, (2) the model accuracy is greatly improved. The effectiveness of the proposed method was verified by 20 batches of thermal error measurement experiments in the real cutting state of the machine tool

Active and intelligent control onto thermal behaviors of a motorized spindle unit

Motorized spindle unit is the core component of a precision CNC machine tool. Its thermal errors perform generally serious disturbance onto the accuracy and accuracy stability of precision machining. Traditionally, the effectiveness of the compensation method for spindle thermal errors is restricted by machine freedom degrees. For this problem, this paper presents an active, differentiated, and intelligent control method onto spindle thermal behaviors, to realize comprehensive and accurate suppressions onto spindle thermal errors. Firstly, the mechanism of spindle heat generation/dissipation-structural temperature-thermal deformation error is analyzed. This modeling conveys that the constantly least spindle thermal errors can be realized by differentiated and active controls onto its structural thermal behaviors. Based on this principle, besides, the active control method is developed by a combination of extreme learning machine (ELM) and genetic algorithm (GA). The aim is to realize the general applicability of this active and intelligent control algorithm, for the spindle time-varying thermal behaviors. Consequently, the contrasting experiments clarify that the proposed active and intelligent control method can suppress accurately and synchronously all kinds of spindle thermal errors. It is significantly beneficial for the improvements of the accuracy and accuracy stability of motorized spindle units

An experimental study of process variables in turning operations of Ti 6Al 4V and Cr Co spherical prostheses

[EN] Ti 6Al 4V and Cr Co alloys are extensively used in manufacturing prostheses due to their biocompatibility, high strength-to-weight ratio and high resistance to corrosion and wear. However, machining operations involving Ti 6Al 4V and Cr Co alloys face a series of difficulties related to their low machinability which complicate the process of controlling the quality levels required in these parts. The main objective of this paper is to study the influence of cutting parameters, machine tool control accuracy and metrology procedures on surface roughness parameters and form errors in contouring operations of Ti 6Al 4V and Cr Co workpieces. The machining performance of the two biocompatible materials is compared, focusing the study on part quality at low feed per revolution and the stochastic nature of plastic deformations at this regime. The results showed a better surface roughness control for Ti 6Al 4V, whereas for Cr Co alloys, the performance presents high variability. In the case of form errors (sphericity), contouring errors and metrology procedures are important factors to be considered for quality assurance. In addition, the study analyses the correlation of the machining performance with different sensor signals acquired from a low cost non-intrusive multi-sensor, showing a high correlation of signals from acoustic emission sensors and accelerometers in the machining of spherical features on Ti 6Al 4V parts. The findings of this research work can be taken into account when designing prostheses components and planning their manufacturing processes.This work was partially supported by Fundacio Caixa-Castello Bancaixa under the research project INV-2009-39. The authors are grateful to Miguel Angel Aymerich and Arcadi Sanz, who assisted in the experimental part. The authors would also like to extend their acknowledgments to Lafitt Company for its collaboration. 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