Numerical Investigation of Thermal Behavior of CNC Machine Tool and Its Effects on Dimensional Accuracy of Machined Parts

The dimensional accuracy of machined parts is strongly influenced by the thermal behavior of machine tools (MT). Minimizing this influence represents a key objective for any modern manufacturing industry. Thermally induced positioning error compensation remains the most effective and practical method in this context. However, the efficiency of the compensation process depends on the quality of the model used to predict the thermal errors. The model should consistently reflect the relationships between temperature distribution in the MT structure and thermally induced positioning errors. A judicious choice of the number and location of temperature sensitive points to represent heat distribution is a key factor for robust thermal error modeling. Therefore, in this paper, the temperature sensitive points are selected following a structured thermomechanical analysis carried out to evaluate the effects of various temperature gradients on MT structure deformation intensity. The MT thermal behavior is first modeled using finite element method and validated by various experimentally measured temperature fields using temperature sensors and thermal imaging. MT Thermal behavior validation shows a maximum error of less than 10% when comparing the numerical estimations with the experimental results even under changing operation conditions. The numerical model is used through several series of simulations carried out using varied working condition to explore possible relationships between temperature distribution and thermal deformation characteristics to select the most appropriate temperature sensitive points that will be considered for building an empirical prediction model for thermal errors as function of MT thermal state. Validation tests achieved using an artificial neural network based simplified model confirmed the efficiency of the proposed temperature sensitive points allowing the prediction of the thermally induced errors with an accuracy greater than 90%.

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.

Analysis and optimization of drilling parameters for tool wear and hole dimensional accuracy in B_4C reinforced Al-alloy

Cutting parameters were evaluated and optimized based on multiple performance characteristics including tool wear and size error of drilled hole. Taguchi＇s L27, 3-level, 4-factor orthogonal array was used for the tests. It is shown that generally abrasive wear and built up edge （BUE） formation were seen in the tool wear, and the comer wear was also of major importance. Flank wear of the cutting tool was found to be mostly dependent upon particle mass fraction, followed by feed rate, drill hardness and spindle speed, respectively. Among the tools used, TiAlN coated carbide drills showed the best performance with regard to the tool wear as well as hole size. Grey relational analysis indicated that drill material was the more influential parameter than feed rate and spindle speed. The results revealed that optimal combination of the drilling parameters could be used to obtain both minimum tool wear and diametral error.

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.

Precision bending of high-quality components for volume applications

Recent new technology developments were presented in the field of industrial bending operations,including flexible stretch forming and 3D rotary stretch forming.Attempts were made to give an overview of different mechanisms that influence dimensional accuracy,including local cross-sectional deformations such as suck-in and volume conservation effects,along with global deformations such as springback.An analytical model was developed to determine the particular influence of different material,geometry and process parameters on dimensional variability of bent components.The results were discussed in terms of overall process capability(Cp) and associated process windows.The results show that different governing mechanisms prevail in various bending operations,meaning that attention has to be placed on controlling those process parameters that really are important to part quality in each specific case.Several strategies may be defined for reducing variability.One alternative may be to design more robust process and tool technology that reduce the effect of upstream parameters on dimensional variability of the formed part.The results show that optimal tool design and technology may in specific cases improve the dimensional accuracy of a formed part.Based on the findings discussed herein,it is concluded that advances in industrial bending operations require focus on improving the understanding of mechanical mechanisms,including models and parameter development,new technology developments,including process,tool,measurement and control capabilities,and process discipline at the shop floor,combined with a basic philosophy of controlling process parameters rather than part attributes.

A new adaptive bending method using closed loop feedback control

As the competition from companies in low cost countries increases,the need for more automated production which reduces labour cost while improving product quality is required.A new rotary compression bending set-up with automated closed-loop feedback control is thus being developed.By transferring in-process measurement data into an algorithm for predicting springback and bend angle prior to the unloading sequence,the dimensional accuracy is improved.This work focuses on the development of this steering model.Since the method used does not increase cycle time,it is attractive for high-volume industrial applications.More than 150 bending tests of AA6060 extrusions were conducted to determine the capability of the technology.The results show that by activating the automated closed-loop feedback system,the dimensional accuracy of the bent parts is more than three times better than that obtained by traditional compression bending.Since the steering model permits the direct use of additional process data,such as instant wall thickness and cross sectional distortions,it is believed that extension of the measurement capabilities would improve the accuracy of the methodology even further.

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.

A Virtual Erection Simulation System for a Steel Structure Based on 3-D Measurement Data

he virtual erection simulation system was explained for a steel structure including ship and ocean plant blocks. The simulation system predicted the erection state to optimize any gap or overlap of blocks based on 3-D measurement data. The blocks were modified （cut） on the basis of the simulation result on the ground before erecting them by crane. The re-cutting process was not required and the blocks were erected into a mother ship speedily. Therefore, the erection time is reduced, increasing the dock turnover.

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.

Study on Programming of CNC Turning and Processing Techniques

Due to its high efficiency, high precision and high flexibility, CNC lathe is widely used in the machinery manufacturing industry increasingly, and becomes one of CNC machine too[s that most widely used. However, to give full play to the role of CNC lathes, the key is programme, that is, preparing the reasonable and efficient processing procedures depending on the features and precision parts. This paper discussed the problems of programming and processing techniques of the CNC lathe parts.

Dimension Accuracy and Surface Integrity of Creep Feed Ground Titanium Alloy with Monolayer Brazed CBN Shaped Wheels

Titanium alloy tenon is creep feed ground with monolayer brazed cubic boron nitride （CBN） shaped wheels. The dimension accuracy of the tenon is assessed and the results indicate that it completely meets the requirement of blade tenon of aero-engine. Residual stresses, surface roughness, microstructure and microhardness are measured on ground surfaces of the specimen, which are all compared with that ground with vitrified CBN wheels. Under all the circumstances, compressive residual stress is obtained and the depth of the machining affected zone is found to be less than 40 μm. No phase transformation is observed at depths of up to 100 lain below the surface, though plastic deformation is visible in the process of grain refinement. The residual stress and microhardness of specimens ground with brazed CBN wheels are observed to be lower than those ground with vitrified ones. The arithmetic mean roughness （Ra） values obtained are all below 0.8μm.

Experimental Research on the Fabrication of Modular Devices for Drilling Using PLA for Model Parts

The objective of the work is to determine the influence of the PLA melting temperature during 3D printing on the dimensional accuracy of the model parts. Two modular drilling devices were also made using PLA model parts. The model parts were 3D printed using FDM technology and the ZMorph 2.0 hybrid 3D printer. The accuracy of 3D printing of the model part influences the realization of modular devices. In recent years, technology has evolved a lot, and the need to have the most efficient manufacturing equipment has increased. This is the reason for the development of 3D printers using FDM technology for plastic parts. The software used by these 3D printers used in FDM technology is very sophisticated, as they allow the manufacture of very precise 3D prototypes, identical to the designed 3D model, through modern additive manufacturing techniques. The quality and mechanical strength of the prototypes obtained using 3D printers is very good. The materials used by the 3D printers manufactured by FDM are cheap and accessible. These 3D printers are used to make three-dimensional objects (gears, flanges, bearings, covers, casings, mechanisms, figurines, interior and exterior design elements, architectural models, medical models).

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.