SMART SENSOR FOR ENHANCEMENT OF A MULTI-SPINDLE AUTOMATIC LATHE THERMAL ERROR COMPENSATION MODEL

Software compensation is state-of-the-art technology used to reduce CNC machine tool thermal errors, and it belongs to a key intelligent functions of modern machine tools. However, a pretrained and nonadaptive model may not be accurate and robust enough for long-term application. This research presents a transfer function based thermal error compensation model updated via on-machine measurement. A mathematical model is implemented into the machine management software of a large horizontal machining centre to compensate for thermal errors in real time using C#/C++ programming language. The results show that after the thermal error compensation model is updated via on-machine measurement, the prediction accuracy, measured as peak-to-peak values, and the normalized root mean squared error are significantly improved. The prediction accuracy of the compensation model updated via on-machine measurement strongly depends on the sampling interval of the on machine measurements.

Thermal error is the major factor in restricting the accuracy of CNC machining. The modeling accuracy is the key of thermal error compensation which can achieve precision machining of CNC machine tool. The traditional thermal error compensation models mostly focus on the fitting accuracy without considering the robustness of the models, it makes the research results into practice is difficult. In this paper, the experiment of model robustness is done in different spinde speeds of leaderway V-450 machine tool. Combining fuzzy clustering and grey relevance selects temperature-sensitive points of thermal error. Using multiple linear regression model (MLR) and distributed lag model (DL) establishes model of the multi-batch experimental data and then gives robustness analysis, demonstrates the difference between fitting precision and prediction precision in engineering application, and provides a reference method to choose thermal error compensation model of CNC machine tool in the practical engineering application.

With the structure design and manufacture of CNC machine tool becoming more and more perfect, its geometry errors have been controlled, thermal error generated by thermal deformation of CNC machine tool is the main error, which has strongly influence on the CNC machine tool's process precision. Thermal error compensation is an important method for increasing the process precision of CNC machine tool, and the neural networks technology is the major tool in thermal error modeling and compensation. In this paper, some applications of neural networks in thermal error modeling and compensation are introduced, and they are demonstrated by examples, the results prove that researches on artificial neural networks and relative algorithms have good effects on CNC machine tool's thermal error modeling and compensation.

In CNC machine tools, transient temperature variation in the headstock assembly is the major contributors for spindle thermal error. The compensation of thermal error is critical for ensuring the accuracy of machine tool. The performance of an error compensation system depends largely on the accuracy and robustness of the thermal error model. In the present work, a robust thermal error model is developed for minimizing the error in lateral direction of the spindle which significantly influences the geometrical accuracy of the workpiece. Analysis-of-variance (ANOVA) is applied to the results of the experiments in determining the percentage contribution of each individual temperature key point against a stated level of confidence. Based on the analysis of existing approaches for thermal error modeling of machine tools, an approach of LASSO (least absolute shrinkage and selection operator) is proposed in order to avoid the multi collinearity problem. The proposed method is an innovative variable selection method to remove redundant or unimportant temperature key points in the linear thermal error model and minimize the residual sum of squares. The predictive error model is found to have better robustness and accuracy in comparison to the combination of grey correlation and step wise linear regression for error compensation of CNC lathe. Keywords: Analysis Of Variance (ANOVA), CNC Machine Tool, Grey Correlation Analysis (GCA), Headstock Assembly, LASSO Regression, Mean Absolute Deviation (MAD), Mean Square Error (MSE), Robustness, Standard Deviation (SD), Thermal Error.

Thermal errors can have significant effects on 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 paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ±4μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system.

An approach to identify and select optimal temperature-sensitive measuring points for thermal error compensation modeling in CNC machines: A case study using cantilever beam

Revolutionizing gear hobbing machine precision: Innovative thermal error compensation and intelligent error control with enhanced crystal structure algorithm and self-attention bidirectional minimal gated unit network

Due to the large size of the heavy duty machine tool-foundation systems, space temperature difference is high related to thermal error, which affects to system’s accuracy greatly. The recent highly focused deep learning technology could be an alternative in thermal error prediction. In this paper, a thermal prediction model based on a self-organizing deep neural network (DNN) is developed to facilitate accurate-based training for thermal error modeling of heavy-duty machine tool-foundation systems. The proposed model is improved in two ways. Firstly, a dropout self-organizing mechanism for unsupervised training is developed to prevent co-adaptation of the feature detectors. In addition, a regularization enhanced transfer function is proposed to further reduce the less important weights of the process and improve the network feature extraction capability and generalization ability. Furthermore, temperature sensors are used to acquire temperature data from the heavy-duty machine tool and concrete foundation. In this way, sample data of thermal error predictive model are repeatedly collected from the same locations at different times. Finally, accuracy of the thermal error prediction model was validated by thermal error experiments, thus laying the foundation for subsequent studies on thermal error compensation.

Thermally compensated 5-axis machine tools evaluated with impeller machining tests

Thermal errors are major contributor to dimensional errors of a part during precision machining. Error compensation is an effective method to reduce thermal errors. Accurate modeling of thermal errors is a prerequisite for thermal error compensation. In this paper, five key temperature points of a computer numerical control (CNC) machine tool were selected based on grey relational analysis method (GRAM). One thermal error model based on the five key temperature points was proposed using artificial fish swarm and ant colony algorithm-based back-propagation neural network (AFSACA-BPN). AFS is applied to generate initial pheromone value of ACA, which improves the computational efficiency of BPNs and prediction accuracy of thermal error modeling. One thermal error real-time compensation system was developed based on the proposed model. An experiment was carried out to verify the performance of the compensation system. Experiment results show that the diameter error of the workpiece reduced from 23 to 10 μm after compensation.

The thermal error model of the 5-axis grinding machine tool was acquired by the homogeneous coordinate transformation, including 17 thermal error components. The thermal volumetric error real time compensation model was built by using the multiple regression analysis. The thermal error compensation control system and the temperature sensing system were developed and used as real-time compensation for the 5-axis grinding machine tool.

The thermal distortion of YK3610 hobbing machine is analyzed. The concept of clustering analysis is proposed and implemented on the gear hobbing machine. The model was used in the experimental of thermal error compensation. The results show that the thermal error compensation control system can reduce thermal errors significantly and the prediction accuracy of the thermal error model is high enough.

Design and development of low cost smart turbidity sensor for water quality monitoring in fish farms

Rapid development of industrial applications imposes more challenges on traditional sensors that only provide raw signal. As a trend, smart sensor and smart sensor network sensors are getting more attention in industrial areas for the values they can bring into the system. One way or another, sensor data will ultimately be used for control purpose. Smart sensor and smart sensor networks will go beyond raw signal and/or data, and provide certain level of information and/or knowledge, which can significantly improve the overall control system performance and reduce reliability. This paper gives a brief introduction on smart sensor and smart sensor networks and their progresses. Some issues observed are also highlighted. By raising people's attention and interests, authors believe that researchers/developers from both academic and industrial societies can and should work together to address those issues and promote the development of smart sensor and smart sensor networks.

A temperature-sensitive points selection method for machine tool based on rough set and multi-objective adaptive hybrid evolutionary algorithm