This study presents surface roughness modeling for machined parts based on cutting parameters (spindle speed, cutting depth, and feed rate) and machining vibration in the end milling process. Prediction models were developed using multiple regression analysis and an artificial neural network (ANN) modeling approach. To reduce the effect of chatter, machining tests were conducted under varying cutting parameters as defined in the stable regions of the milling tool. The surface roughness and machining vibration level are modeled with nonlinear quadratic forms based on the cutting parameters and their interactions through multiple regression analysis methods, respectively. Analysis of variance was employed to determine the significance of cutting parameters on surface roughness. The results show that the combined effects of spindle speed and cutting depth significantly influence surface roughness. The comparison between the prediction performance of the multiple regression and neural network-based models reveal that the ANN models achieve higher prediction accuracy for all training data with R = 0.96 and root mean square error (RMSE) = 3.0% compared with regression models with R = 0.82 and RMSE = 7.57%. Independent machining tests were conducted to validate the predictive models; the results conclude that the ANN model based on cutting parameters with machining vibration has a higher average prediction accuracy (93.14%) than those of models with three cutting parameters. Finally, the feasibility of the predictive model as the base to develop an online surface roughness recognition system has been successfully demonstrated based on contour surface milling test. This study reveals that the predictive models derived on the cutting conditions with consideration of machining stability can ensure the prediction accuracy for application in milling process.

This paper is focused on developing a compensation module for reducing the thermal errors of a computer numerical control (CNC) milling machine. The thermal induced displacement variations of machine tools are a vital problem that causes positioning errors to be over than 65%. To achieve a high accuracy of machine tools, it is important to find the effective methods for reducing the thermal errors. To this end, this study first used 14 temperature sensors to examine the real temperature fields around the machine, from which four points with high sensitivity to temperature rise were selected as the major locations for creating the representative thermal model. With the model, the compensation system for controlling the displacement variation was developed. The proposed model has been applied to the milling machine. Current results show that the displacement variations on the x- and y-axes and the position error at the tool center were controlled within 20 µm when the compensation system was activated. The feasibility of the compensation system was successfully demonstrated in application on the milling operation.

Artificial granite material has been used for construction of the high-precision machine due to superior damping ability. However, its lower material stiffness also lessens the application in fabrication of the machine tool. The purpose of this study was therefore aimed to verify the structure performance of the machine tool reformed with artificial granite material, instead of the casting iron. To gain insight into the optimized configuration of the vertical columns and spindle head stock, the static and dynamic characteristics of the machine models with cast iron and granite composites were predicted for comparison. With this, evaluations of the machine with different design were made to examine the experimental measurements on the prototype machine. According to the simulation results and experimental measurements, the static stiffness of the machine tool reformed with granite material is comparable to the original casting machine, differed by 8% approximately. However, granite machine shows superior dynamic stiffness, about 0.5–1.3 times of the conventional casting machine. This study verifies the feasibility and effectiveness in fabrication of the machine tool with the artificial composite material and provides the improvements for further fabrications.

This study was aimed at investigating the blast resistance of a stiffened door structure. For this, a series of finite element transient analysis were performed to predict the dynamic response of blast resistant doors. Current results show that the stiffeners have sustained the majority of the explosive loadings. The effect of stiffener size on the stress state was rated in terms of the ratio of stiffener stress to plate stress, which was enhanced with the increase of web thickness of stiffener. Additional, the highest stresses were shown to occur at the transverse stiffeners near the hinged sides with a maximum stress above the dynamic yield strength, indicating the local yielding at these points. However, the rotational deformations of hinged support were within the deformation limit required for medium level of protection. In final, a static compressive experiment was carried out on prototype door structure to measure the static stiffness according to the test criteria given in TM5-1300. The numerically predicted results were demonstrated to agree well with the experimental results, showing a good performance of the door structure against blast loads was achieved.