Further Analysis of Machine Tool Dimensional Accuracy and Thermal Stability under Varying Floor Temperature

Machining is as old as humanity, and changes in temperature in both the machine’s internal and external environments can be of great concern as they affect the machine’s thermal stability and, thus, the machine’s dimensional accuracy. This paper is a continuation of our earlier work, which aimed to analyze the effect of the internal temperature of a machine tool as the machine is put into operation and vary the external temperature, the machine floor temperature. Some experiments are carried out under controlled conditions to study how machine tool components get heated up and how this heating up affects the machine’s accuracy due to thermally induced deviations. Additionally, another angle is added by varying the machine floor temperature. The parameters mentioned above are explored in line with the overall thermal stability of the machine tool and its dimensional accuracy. A Robodrill CNC machine tool is used. The CNC was first soaked with thermal energy by gradually raising the machine floor temperature to a certain level before putting the machine in operation. The machine was monitored, and analytical methods were deplored to evaluate thermal stability. Secondly, the machine was run idle for some time under raised floor temperature before it was put into operation. Data was also collected and analyzed. It is observed that machine thermal stability can be achieved in several ways depending on how the above parameters are joggled. This paper, in conclusion, reinforces the idea of machine tool warm-up process in conjunction with a carefully analyzed and established machine floor temperature variation for the approximation of the machine tool’s thermally stability to map the long-time behavior of the machine tool.

Multiple Response Optimization of Dimensional Accuracy of Nimonic Alloy Miniature Gear Machined on Wire Edm Using Entropy Topsis Andpareto Anova

The purpose of this research is to obtain the optimum cutting parameters to achieve the dimensional accuracy of Nimonic alloy miniature gear manufactured using Wire EDM.The cutting parameters investigated in this study are current,pulse on time(PON),pulse off time(POFF),wire tension(WT)and dielectric fluids.Ethylene glycol,nanopowder of alumina and oxygen are mixed to demineralized water to prepare novel dielectric fluids.Deviation in inner diameter,deviation in outer diameter,deviation in land and deviation in tooth width are considered to check the dimensional accuracy.Taguchi L_(16) is employed for experimental design and multiple response optimization is performed using Entropy TOPSIS and Pareto ANOVA.Results indicate that pulse on time is the most notable parameter for good dimensional accuracy followed by dielectric fluid,current,pulse off time and wire tension.Ethylene glycol mixed demineralized water is preferred for low dimensional deviation.The optimum WEDM parameters are pulse on time at 20μs,Ethylene glycol mixed demineralized water dielectric fluid,current at 3 A,pulse off time at 4μs,and wire tension at 18 N.

Modeling, analysis, and optimization of dimensional accuracy of FDM-fabricated parts using definitive screening design and deep learning feedforward artificial neural network

Additive manufacturing(AM)technologies such as fused deposition modeling(FDM)rely on the quality of manufactured products and the process capability.Currently,the dimensional accuracy and stability of any AM process is essential for ensuring that customer specifications are satisfied at the highest standard,and variations are controlled without significantly affecting the functioning of processes,machines,and product structures.This study aims to investigate the effects of FDM fabrication conditions on the dimensional accuracy of cylindrical parts.In this study,a new class of experimental design techniques for integrated second-order definitive screening design(DSD)and an artificial neural network(ANN)are proposed for designing experiments to evaluate and predict the effects of six important operating variables.By determining the optimum fabrication conditions to obtain better dimensional accuracies for cylindrical parts,the time consumption and number of complex experiments are reduced considerably in this study.The optimum fabrication conditions generated through a second-order DSD are verified with experimental measurements.The results indicate that the slice thickness,part print direction,and number of perimeters significantly affect the percentage of length difference,whereas the percentage of diameter difference is significantly affected by the raster-to-raster air gap,bead width,number of perimeters,and part print direction.Furthermore,the results demonstrate that a second-order DSD integrated with an ANN is a more attractive and promising methodology for AM applications.

A hybrid deep learning model for robust prediction of the dimensional accuracy in precision milling of thin-walled structural components

The use of artificial intelligence to process sensor data and predict the dimensional accuracy of machined parts is of great interest to the manufacturing community and can facilitate the intelligent production of many key engineering components.In this study,we develop a predictive model of the dimensional accuracy for precision milling of thin-walled structural components.The aim is to classify three typical features of a structural component—squares,slots,and holes—into various categories based on their dimensional errors(i.e.,“high precision,”“pass,”and“unqualified”).Two different types of classification schemes have been considered in this study:those that perform feature extraction by using the convolutional neural networks and those based on an explicit feature extraction procedure.The classification accuracy of the popular machine learning methods has been evaluated in comparison with the proposed deep learning model.Based on the experimental data collected during the milling experiments,the proposed model proved to be capable of predicting dimensional accuracy using cutting parameters(i.e.,“static features”)and cutting-force data(i.e.,“dynamic features”).The average classification accuracy obtained using the proposed deep learning model was 9.55%higher than the best machine learning algorithm considered in this paper.Moreover,the robustness of the hybrid model has been studied by considering the white Gaussian and coherent noises.Hence,the proposed hybrid model provides an efficient way of fusing different sources of process data and can be adopted for prediction of the machining quality in noisy environments.

Sub-pixel high precision dimensional measurement method for aero-engine hollow turbine blade based on industrial computed tomography image

Aero-engine hollow turbine blades are work under prolonged high temperature,requiring high dimensional accuracy.Blade profile and wall thickness are important parameters to ensure the comprehensive performance of blades,which need to be measured accurately during manufacturing process.In this study,a high accuracy industrial computed tomography(ICT)measuring method was developed based on standard cylindrical pin and ring workpieces of different sizes.Combining ICT with cubic spline interpolation,a sub-pixel accuracy was achieved in measuring the dimension of component.Compared with the traditional and whole-pixel level image measurement method,the cubic spline interpolation algorithm has the advantages of high accuracy in image edge detection and thus high accuracy of dimensional measurement.Further,the technique was employed to measure the profile and wall thickness of a typical aerospace engine turbine blade,and an accuracy higher than 0.015 mm was obtained.

Iterative reverse deformation optimization design of castings based on numerical simulation of solidification thermal stress

In the casting process,in order to compensate for the solidification shrinkage to obtain higher dimensional accuracy of the casting,it is often necessary to modify the original design of castings,and a suitable compensation method has a decisive impact on the dimensional accuracy of the actual casting.In this study,based on solidification simulation,a design method of reverse deformation is proposed,and two compensation methods,empirical compensation and direct reverse deformation,are implemented.The simulation results show that the empirical compensation method has problems such as difficulty in determining the parameters and satisfaction of both the overall and local accuracy at the same time;while based on the simulation results for each node of the casting,the direct reverse deformation design achieves the design with shape.In addition,the casting model can be optimized through iterative revisions,so that higher dimensional accuracy can be continuously obtained in the subsequent design process.Therefore,the direct reverse deformation design is more accurate and reasonable compared to empirical compensation method.

Effect of gel time of 3D sand printing binder system on quality of sand mold/core

Two important factors affecting the performance of sand mold/core generated by 3D printing(3DP)are strength and dimensional accuracy,which are not only closely related to the reactivity of furan resin and the phase transition of silica sand,but also the curing agent system of furan resin.This paper studies the influence of gel time on the strength and dimensional accuracy of a 3DP sand mold/core,taking the furan resin system as an example and using a sand specimen generated by a 3DP inkjet molding machine.The experiment demonstrates that the gel time of 3 to 6 min for the sand mixture suits 3DP core-making most under the experimental condition.However,it should be noted that under the same resin condition,the strength of a no-bake sand mold/core is higher than that of a 3DP sand mold/core.The dimensional accuracy of the sand mold/core does not change significantly when the gel time is less than 15 min.Improving the activity of binder and developing ultra-strong acid with low corrosion shall be an effective way to improve the quality of the mold/core by 3D printing.

Analysis on fused deposition modelling performance

Fused deposition modelling (FDM) is one of rapid prototyping (RP) technologies which uses an additive fabrication approach.Each commercially available FDM model has different types of process parameters for different applications.Some of the desired parts require excellent surface finish as well as good tolerance.The most common parameters requiring setup are the raster angle,tool path,slice thickness,build orientation,and deposition speed.The purpose of this paper is to discuss the process parameters of FDM Prodigy Plus (Stratasys,Inc.,Eden Prairie,MN,USA).Various selected parameters were tested and the optimum condition was proposed.The quality of the parts produced was accessed in terms of dimensional accuracy and surface finish.The optimum parameters obtained were then applied in the fabrication of the master pattern prior to silicone rubber moulding (SRM).These parameters would reduce the post processing time.The dimensional accuracy and surface roughness were analyzed using coordinate measuring machine (CMM) and surface roughness tester,respectively.Based on this study,the recommended parameters will improve the quality of the FDM parts produced in terms of dimensional accuracy and surface roughness for the application of SRM.

Processing of a Novel Zn Alloy Micro-Tube for Biodegradable Vascular Stent Application

In recent years, zinc based alloys as a new biodegradable metal material aroused intensive interests. However, the processing of Zn alloys micro-tubes （named slender-diameter and thin-walled tubes） is very difficult due to their HCP crystal structure and unfavorable mechanical properties. This study aimed to develop a novel technique to produce micro-tube of Zn alloy with good performance for biodegrad- able vascular stent application. In the present work, a processing method that combined drilling, cold rolling and optimized drawing was proposed to produce the novel Zn-5Mg-1Fe （wt%） alloy micro- tubes. The micro-tube with outer diameter of 2.5 mm and thickness of 130 μm was fabricated by this method and its dimension errors are within 10 μm. The micro-tube exhibits a fine and homogeneous microstructure, and the ultimate tensile strength and ductility are more than 220 MPa and 20% respectively. In addition, the micro-tube and stents of Zn alloy exhibit superior in vitro corrosion and expansion performance. It could be concluded that the novel Zn alloy micro-tube fabricated by above method might be a promising candidate material for biodegradable stent.

Optimization of fused deposition modeling process parameters using a fuzzy inference system coupled with Taguchi philosophy

Fused deposition modeling （FDM） is an additive manufacturing technique used to fabricate intricate parts in 3D, within the shortest possible time without using tools, dies, fixtures, or human intervention. This article empiri- cally reports the effects of the process parameters, i.e., the layer thickness, raster angle, raster width, air gap, part orientation, and their interactions on the accuracy of the length, width, and thicknes, of acrylonitrile-butadiene- styrene （ABSP 400） parts fabricated using the FDM tech- nique. It was found that contraction prevailed along the directions of the length and width, whereas the thickness increased from the desired value of the fabricated part. Optimum parameter settings to minimize the responses, such as the change in length, width, and thickness of the test specimen, have been determined using Taguchi＇s parameter design. Because Taguchi＇s philosophy fails to obtain uniform optimal factor settings for each response, in this study, a fuzzy inference system combined with the Taguchi philosophy has been adopted to generate a single response from three responses, to reach the specific target values with the overall optimum factor level settings. Further, Taguchi and artificial neural network predictive models are also presented in this study for an accuracy evaluation within the dimensions of the FDM fabricated parts, subjected to various operating conditions. The pre- dicted values obtained from both models are in good agreement with the values from the experiment data, with mean absolute percentage errors of 3.16 and 0.15, respectively. Finally, the confirmatory test results showed an improvement in the multi-response performance index of 0.454 when using the optimal FDM parameters over the initial values.