Spindle Speed Research Articles

Effect of machining parameters on average surface roughness during computer numerical controlled dry milling of high strength AISI 420 martensitic stainless steel

This study focuses on developing an empirical model for average surface roughness during computer numerical controlled (CNC) dry milling of AISI 420 martensitic stainless steel, utilizing response surface methodology (RSM). Experiments were designed with three levels of axial depth of cut, feed rate, and spindle speed to quantify their impact on surface roughness. The RSM-Box-Behnken design was employed to construct the empirical model. Model adequacy was validated through residual analysis and analysis of variance (ANOVA). Analysis of the main effects and interaction effects revealed that the primary influences on average surface roughness were the feed rate, spindle speed, and axial depth of cut, while interaction effects were less significant. Optimal cutting conditions were determined to be a spindle speed of 1500 rpm, a feed rate of 30 mm min−1, and an axial depth of cut of 0.3 mm. The model’s validity was further confirmed through additional validation tests.

The Effect of Depth of Cut and Spindle Speed on Cutting Parallelism Results on Aluminum 6061 CNC TU-3A Retrofit Machine

Machining process is an important part of manufacturing to form and finish high-precision components. Milling is a commonly used technique, and the advancement of CNC technology has enabled precise and consistent production. This study evaluated the effect of spindle speed and depth of cut on the surface parallelism (µm) of aluminum 6061 using a TU-3A retrofit CNC milling machine. A quantitative experimental method with factorial design of experiments (DOE) was applied, testing spindle speeds of 600 RPM, 800 RPM, and 1000 RPM and depths of cut of 1.0 mm, 1.5 mm, and 2.0 mm, while maintaining constant feed rates of 48 mm/min, 64 mm/min, and 80 mm/min. The results showed that spindle speed had no significant impact on surface parallelism (µm) (P-value = 0.924), although higher speeds showed a trend of better results. In contrast, depth of cut significantly affects parallelism (µm) (P-value = 0.000), with greater depth improving surface quality (µm). In addition, the interaction between spindle speed and depth of cut is also significant (P-value = 0.002), indicating that the combination of higher spindle speed with lower depth of cut produces better results. These findings suggest optimal machining parameters to improve surface parallelism (µm) in aluminum milling operations.

Experimental investigation of machine vibration rate and machine sound level in MQL turning of AISI 1525 steel employing mango oil as lubricant

AbstractThe detrimental effects of mineral oil on the environment and machine shops have led to a surge in the usage of vegetable oil as cutting fluid. The most popular vegetable oil lubricants are edible, and they have a lot of potential to rival human consumption eventually. The study examined using mango oil, an inedible vegetable oil, as a lubricant during AISI 1525 steel turning with tungsten inserts. The best machining parameters were ultimately found using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) after the experimental studies were analyzed using the Taguchi process. Surface and contour plots were employed to investigate how different cutting settings affected the rate of vibration and sound level of the machine. Mango kernel seed oil outperformed its mineral oil counterparts by 2.3% and 57.7%, respectively, in terms of machine vibration rate and machine sound level. Moreover, feed rate (0.10 mm/rev), depth of cut (0.75 mm), and spindle speed (350 rev/min) are the ideal cutting settings to reduce machine vibrations and sound intensity. Mango oil holds significant potential as a substitute for cutting fluid derived from petroleum. The significance of this research is to formulate lubricants for industrial use that are more ecologically friendly and sustainable.

Forming mechanism of a novel shape-surface integrated incremental sheet forming process for fabricating parts with enhanced surface functionality

Metallic components with surface microfeatures are increasingly used in aerospace, automotive and other applications due to their drag-reducing properties. In this situation, we proposed a novel shape-surface integrated incremental sheet forming (S2-ISF) process to achieve the simultaneous fabrication of both surface microfeatures and desired geometric shape. It was found that an increase in the forming angle was most effective in improving the formation of microgroove array, followed by the step size, the spindle speed and the feed rate. Moreover, when the forming angle is constant, the step size and microgroove pitch increase linearly. Compared to the as-received sheet, the yield strength is increased by more than 20% after the S2-ISF process. Moreover, we observed that the increase of forming angle in the S2-ISF process has promoted grain deformation and grain fragmentation, which is favorable for the formed part to obtain more uniform and finer grains. The grain size distribution along the thickness direction shows the characteristics of ‘coarse in the middle and fine at both ends’. Finally, we found that the formed parts with different microgroove pitch from the S2-ISF process have the significant enhancement on the hydrophobicity and anti-friction property. When microgroove pitch is 155.5 μm, the surface contact angle of formed part improved from 70.65° to 101.39°, and the wear volume of formed part decreases from 4.89 × 10−3 mm3 to 3.65 × 10−3 mm3. This work provides a solution for the efficient and economical fabrication of three-dimensional thin-walled parts with microgroove array to enhance surface functionality.

Multi-objective optimization of EN19 steel milling parameters using Taguchi, ANOVA, and TOPSIS approaches

This study focuses on optimizing milling parameters for EN19 steel through the Taguchi method. Utilizing an L9 orthogonal array with three parameters each set at three levels, the study evaluated four quality characteristics in every run. The optimization process involved the use of Signal-to-Noise Ratio (SNR), Analysis of Variance (ANOVA), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to determine optimal conditions and identify critical parameters influencing surface roughness, temperature, cutting force, and material removal rate.ANOVA results underscore the significant impact of spindle speed, cutting fluids, and depth of cut (DOC). The TOPSIS analysis identified the optimal conditions as a spindle speed of 710 rpm, a DOC of 0.5 mm, and the use of Neem oil combined with graphene as the cutting fluid. Among these factors, the depth of cut was found to be the most influential, followed by spindle speed and cutting fluid. Confirmation tests corroborated the effectiveness of the optimization approach, affirming its potential to improve milling outcomes. This research offers valuable insights into enhancing machining efficiency and product quality in the milling of EN19 steel.

Energy efficiency identification and surface roughness prediction using cutting force signal for computer numerical controlled machine systems

The energy efficiency identification of machining process plays an indispensable part in achieving energy-efficient manufacturing and improving energy utilization as well as productivity and surface quality. However, there is a great difficulty to track energy efficiency in real-time based on one kind of traditional power signal. Because energy consumption is affected by many factors such as machine tool current performance, tool wear conditions and cutting parameters selection. This paper puts forward an energy efficiency recognition method as well as surface roughness prediction model based on the cutting force signals. The CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) algorithm is employed to decompose the cutting force signal into multiple IMF (intrinsic mode function) components; and characterization of energy efficiency of machining process is recognized through proportion of components based on PCA—Fast ICA algorithm. Then, a surface roughness prediction model is proposed using support vector regression (SVR) based on specific cutting energy consumption (SCEC). The orthogonal test is designed considering spindle speed, feed rate, depth of cutting and width of cutting in 3 levels to obtain the influence degree of cutting parameters on cutting force, specific energy consumption, and the surface roughness. The energy efficiency of 27 group experiments is classified into high, medium and low levels according to energy efficiency value. Finally, using the data of orthogonal test, energy efficiency state was identified. The result show that time–frequency of cutting force signals for high, medium and low energy efficiency could be extracted, and the average absolute error of surface roughness predict is 0.058. That illustrated that the proposed method could meet the industry requirement for energy efficiency monitoring and surface roughness prediction to achieve sustainable manufacturing.

Real-Time Milling Chatter Detection and Control with Axis Encoder Feedback and Spindle Speed Manipulation

This paper introduces a complete real-time algorithm, where the chatter is detected and eliminated by spindle speed manipulation via the chatter energy feedback calculated from the axis encoder measurement. The proposed method does not require profound knowledge of the machining dynamics; instead, the entire algorithm exploits the fact that milling vibrations consist of forced vibrations at spindle speed harmonics and chatter vibrations that are close to one of the natural modes, with sidebands which are spread at the multiples of spindle speed frequency above and below the chatter frequency. The developed algorithm is able to identify the amplitude, phase and frequency of all the harmonics constituting the periodic forced and chatter vibrations. The key challenge is to select dominant chatter frequencies for the calculation of a robust and accurate chatter energy ratio feedback; this is achieved by utilizing the frequency estimation variance of EKF as a novel chatter indicator. Based on the chatter energy ratio feedback, the controller overrides the spindle speed in order to suppress the chatter energy below a particular threshold value. The varying spindle speed challenge is handled by updating the state transition matrices of the Kalman filters and real-time calculation of the band-pass filter coefficients, based on the derived discrete time transfer functions. The developed algorithm is tested on a Deckel FP5cc CNC which is in-house retrofitted and has a PC-based controller for the real-time application of the proposed algorithm. It is shown that the real-time chatter frequency and amplitude estimates are compatible with off-line FFT analysis, and chatter can be successfully eliminated by energy feedback.

Enhancing face-milling efficiency of wood–plastic composites through the application of genetic algorithm–back propagation neural network

ABSTRACT Wood–plastic composites (WPCs) have advanced physicochemical properties and are widely applied in various fields. How to achieve high-efficiency, high-quality machining of WPC is a pressing issue that WPC manufacturing enterprises need to address. To this end, this research focused primarily on improving the machinability of WPC with end milling experiments. In this work, a single-factor method was used to analyse the impact of axial milling depth, spindle speed, and tool rake angle on resultant forces and surface roughness. Main effects analysis was applied to explore the degree of influence of axial milling depth, spindle speed, and tool rake angle on cutting forces and surface roughness. Furthermore, three-dimensional characterisation techniques were utilised to analyse the surface morphology characteristics of WPC during end milling. Finally, a genetic algorithm–back propagation neural network was applied to develop prediction models for resultant force and surface roughness, and optimal milling conditions were identified as axial milling depth of 0.50 mm, spindle speed of 7841 r/min, and rake angle of 15°; these produced the lowest resultant force and surface roughness. The findings of this work are proposed as a guide for better cutting performance in the industrial production of WPC.

Effect of Pinless Friction Stir Processing on Microstructure and Properties of Surface Modification Layer of 2024 Aluminum Alloy

Abstract Friction stir processing (FSP) is an advanced material surface modification technology that is both green and energy-efficient. This technology plays a crucial role in regulating the surface microstructure of alloys and improving their material surface properties. It achieves this through the synergistic effect of non-equilibrium thermodynamics and surface mechanical deformation. In this work, the surface modification of an aluminum alloy was performed using pin-less FSP. And the modified surface was then analyzed using stress-strain curves, optical microscopy (OM), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and electrochemical tests to investigate the impact of spindle velocity on the modified layer's properties. Results of the study show after undergoing pin-free FSP modification treatment, the surface of the alloy appears bright and flat. The modified layer displays refined grains and numerous dispersed second-phase particles. Furthermore, the grains in the modified layer exhibit a gradient distribution from the surface to the matrix. Regarding the properties, Compared to the base material (BM), the yield strength (σ0.2) and tensile strength (σb) of the alloy-modified layer were increased by 34.8% and 29.4%, respectively. The maximum elongation (δ) of the modified coating reached 22.3%. The modified layer exhibits a tough-brittle mixed fracture pattern. Additionally, the modified layer's corrosion resistance significantly improves. The performance of the modified coating shows the most significant improvement when the spindle speed reaches 1000 rpm. The study aims to explore the effect of spindle speed on the properties of alloy-modified layers produced by FSP without a stirring pin. This research could be used to create superior metal matrix composites by adding reinforcement particles.

Experimental and statistical damage analysis in milling of S2‐glass fiber/epoxy and basalt fiber/epoxy composites

AbstractS2‐glass fiber reinforced plastics (S2‐GFRP) and basalt fiber reinforced plastics (BFRP) have emerged as crucial materials due to their exceptional mechanical properties, and milling of composite materials plays an important role in achieving desired properties. However, they have proven challenges due to relative inhomogeneity compared with metals, resulting unpredictability in quality of milling operations. The objective of this work is to investigate the effect of cutting parameters, tool geometry and tool surface materials on the surface quality of composites using burrs as a metric. S2‐GFRP and BFRP composites were produced by the vacuum infusion method. Helical and straight flute end mills were manufactured from high‐speed steel (HSS) and carbide rounds, and half of them were coated with titanium nitride using reactive magnetron sputtering technique. Taguchi L18 orthogonal array is used to determine the effect of tool material, tool angle, coating, cutting direction, spindle speed, and feed rate on the machining quality of S2‐GFRPs and BFRPs with respect to burr formations. Milling experiments were conducted under dry conditions and then the burrs were imaged to calculate the total area and length. Statistical analysis was also performed to optimize the machining parameters and tool type for ensuring the structural integrity and performance of the final composite parts. The results showed that the selection of tool material has the most significant impact on the burr area and length of the machined surface. The novel image analysis allows to analyze the extent of the burr size with a desirable operation speed for industrial applications.Highlights Aerospace grade S2‐Glass (S2‐GFRP) and basalt fiber reinforced plastics (BFRP) were manufactured. Carbide and HSS end mills were fabricated and coated with titanium nitride protective layer. FRPs were machined at various process parameters designed by Taguchi method. Distinctive image processing was firstly used to compute milling induced Burr area and length. Statistical analysis was performed to quantify the contribution of parameters and optimize milling.

Investigation on high-precision drilling of Cf/SiC composites with brazed diamond core-drill

Carbon fiber reinforced silicon carbide ceramic matrix (Cf/SiC) composites have promising applications as lightweight and hot end components due to their unique mechanical and high-temperature properties. However, high-precision drilling of Cf/SiC composites is still a difficult task, which is attributed to material's high hardness, anisotropy, and heterogeneity. In this work, a kind of core-drill with cemented carbide as shank and polycrystalline diamond (PCD) as grits, was prepared with vacuum brazing method. Experiments on drilling of 2D-Cf/SiC composite with the brazed core-drill were carried out. The effects of drilling parameters on the hole dimensional accuracy, surface roughness and defects were investigated. The results showed that with the increase of spindle speed and the decrease of feed rate, the dimensional accuracy was improved and surface roughness was reduced. Delamination, burr, and hole edge collapse were the main defects. The feed rate had a significant influence on the formation of defects. When the feed per revolution was equivalent to the diameter of a single carbon fiber, the defects were reduced effectively and the hole quality was improved substantially. In the investigated range of parameters, the optimal spindle speed and feed rate for high-precision drilling of Cf/SiC composites were 5000 r/min and 30 mm/min, respectively. Under this condition, the hole dimension accuracy was IT 10, and barely defects were observed on the machined surface. The surface roughness of hole sidewall reached a minimum value of 1.9 μm. This work provides a vital process for high-precision drilling on fiber reinforced ceramic matrix composites.

Numerical investigation on milling performance and damage response of UD-CFRP laminates

Carbon fiber-reinforced polymer (CFRP) is prone to machining defects, including burrs and tearing, during milling. These defects are closely associated with interface delamination while milling force plays a crucial role in their occurrence. In this paper, a nonlinear explicit finite element (FE) model is developed to predict the milling force and analyze the delamination damage occurring during the milling of unidirectional CFRP (UD-CFRP). The proposed FE model is validated through numerical comparison with experimental data for milling force. Moreover, a detailed study is conducted to examine the influences of spindle speed, feed rate and depth-of-cut on the side-milling performance and damage response of UD-CFRP. This study provides valuable insights into the effects of machining parameters on milling force and delamination damage in CFRP, thereby supporting the optimization of machining technology for this composite material system.

Analysis and optimization of machining parameters of AZ91 alloy nanocomposite with the Influences of nano ZrO2 through vacuum diecast process

The magnesium alloy composite is a vital material for automotive applications due to its features like high stiffness, superior damping resistance, high strength, and lightweight. Here, the motto of research is to establish the AZ91 alloy nanocomposite with the exposures of 0, 1, 3, and 5 volume percentages (vol%) of nano zirconium dioxide (ZrO2) particles (50nm) through fluid stir metallurgy route associated with 1x105 Pa vacuum die cast process. Exposures on structural morphology, hardness, and impact toughness of composite are analyzed and identified as the nano AZ91 alloy composite enclosed with 5vol% is homogenous particle dispersion, enhanced hardness (97.6HV), and optimum toughness of 21.2J/mm2. However, composite faces machining difficulties due to the hard abrasive particles with higher hardness, resulting in tool wear. This experiment predicts the optimum mill parameters during the end mill operation of magnesium alloy nanocomposite (AZ91/5vol%) by using a tungsten carbide coated end mill cutter to attain the maximum metal removal rate with low surface roughness and tool wear analyzed via the general linear model (GLM) ANOVA approach. The input conditions for end milling operation vary, like feed rate (0.1 -0.4mm/rev), depth of cut (0.05 -0.2mm), and spindle speed (250-1000rpm). During the ANOVA GLM approach, the L16 design experiment is fixed for further interaction analysis. The results predicted by the depth to cut and feed rate were dominant and played a major role in deciding the tool wear, surface roughness, and MRR.

Experimental investigation and parametric optimization of rotary ultrasonic machining of different bio-ceramic materials

Rotary ultrasonic machining (RUM) is a highly promising technique for machining bio-ceramic materials due to its ability to achieve high precision and superior surface quality. This research focuses on an experimental investigation and parametric optimization of RUM for different bio-ceramic materials, intending to optimize multiple machining responses simultaneously. The experimental setup involves utilizing a 3-axis CNC ultrasonic machine to machine three different workpieces with slot cutting. Various machining parameters, such as tool feed rate and tool rotating speed, are studied to determine their impacts. An orthogonal array design based on Taguchi optimization is used to execute the experiments effectively. Material removal rate (MRR) and surface roughness (SR) are monitored and statistically analyzed as a consequence of the responses. ANOVA demonstrates that the tool feed rate has a considerable impact on the output reactions, with material type and tool rotational speed also playing a role. For multiple response optimizations, the Taguchi-Grey method is used to achieve the best trade-off between MRR and Sr The results demonstrate that material type has the most substantial impact on surface roughness, followed by feed rate and spindle speed. In contrast, feed rate has the most significant effect on the material removal rate, followed by the material type and spindle speed. The optimal parameter settings for achieving the desired output parameter are determined. The confirmation experiments validate the effectiveness of the optimized parameters. The feed rate of 5 mm min−1, and spindle speed of 2500 rpm were discovered to be the optimal condition for achieving maximum MRR and minimum Ra. The MRR and surface roughness values were measured as 1.7266 mm3 min−1 and 1.5611 microns respectively.

A comparative investigation on the surface characteristics of AISI 304 and AISI 1045 steel influenced by turning combined with ball burnishing

Burnishing is a popular superfinishing procedure in the manufacturing industry that induces plastic deformation to the finished product by the application of a highly polished and hardened deforming element known as a burnishing tool. Research work on the burnishing process is numerous. However, the reports containing the comparative investigations are lacking. In this work, the effect of ball burnishing parameters namely penetration depth, burnishing spindle speed, and number of passes has been compared and analyzed on AISI 1045 and AISI 304 steels with respect to surface roughness, hardness, wear resistance, and corrosion resistance. Results indicate that the combination of a feed rate of 0.12 mm/rev, spindle speed of 160 rpm, and penetration depth of 0.025 mm with four passes generated minimum surface roughness (AISI 1045). However, maximum surface hardness is obtained with a feed rate of 0.12 mm/rev, spindle speed of 160 rpm, and at 0.085 mm penetration depth under one pass (AISI 304). The lowest specific wear rate is achieved with a feed rate of 0.12 mm/rev at a spindle speed of 160 rpm and a penetration depth of 0.085 mm in one pass (AISI 304). The burnishing process influenced both materials, AISI 304 and AISI 1045, under varying parametric conditions. The maximum reduction in surface roughness is 68.1% for AISI 304 and 79.3% for AISI 1045, while surface hardness improves by 27% for AISI 304 and 22% for AISI 1045. Additionally, the burnishing process reduces the specific wear rate by 49.3% for AISI 304 and 45.4% for AISI 1045. The refinement of grains during the burnishing process enhances corrosion resistance compared to the unburnished surface.

Investigation on effect of cryogenic cooling on the hole-making of CFRP with conventional PCD tool and textured PCD tool

Carbon fiber reinforced polymer (CFRP) has obtained the widespread use in significant basic parts owing to its excellent properties. Different methods including the conventional PCD tool using dry machining (CPD), the conventional PCD tool with CO2 cryogenic cooling (CPC), and the textured PCD tool with CO2 cryogenic cooling (TPC), were introduced to investigated their effects on the holes-making performance. Results demonstrated that the obtained average entrance diameter and relative error of different spindle speeds using the CPC were about 4001 to 4005.4 μm and 0.025% to 0.65%, respectively. Dimensional accuracy and surface edge quality of entrance hole can be better improved by the CPC method. It was found that the cryogenic cooling method showed the remarkable effect in improving the anti-adhesion of tools than compared to the dry machining. Additionally, results indicated that the cryogenic cooling resulted in the significant fiber fractures and many pits inside the hole wall and the CPD method can help to improve the inner hole wall quality.

Thermo-mechanical coupled three-dimensional finite element simulation analysis of drilling thermoplastic braided carbon fiber composite and optimization of process parameters

Optimizing the process parameters of thermoplastic composites during drilling is paramount for mitigating tearing, delamination, and other forms of damage, while simultaneously enhancing the tensile strength and extending the long-term service life of the overall structure. This study focuses on exploring a thermal-mechanical coupling method for predicting dynamic mechanical progressive failure and determining optimal process parameters during the drilling of Braided Carbon Fiber Reinforced Polyether Ether Ketone (BCF/PEEK). Initially, a thermal conduction constitutive model of BCF/PEEK was developed based on the proposed thermal distribution ratio calculation method. Concurrently, a user-defined material subroutine VUMAT and a bilinear cohesive element model, were implemented on the ABAQUS/Explicit platform to simulate the delamination behaviors of drilling BCF/PEEK using a tapered drill-reamer. Subsequently, a comprehensive BCF/PEEK drilling experiment platform was constructed, and the simulation accuracy of the drilling finite element (FE) model was verified through temperature, thrust force, and hole-wall morphology analyses. Finally, response surface regression models were established for process parameters, and the optimal parameters were predicted and validated. The results indicate that minimum thrust force and temperature can be achieved using the tapered drill-reamer with a spindle speed set at 4878.79/3000 r/min and a feed speed of 34.04/30 mm/min, respectively, with a maximum error of only 8.78 %.

Predicting error for machining thin-walled blades considering initial error

A multistage machining process is employed to machine the blades with low stiffness. Nonetheless, machining errors can be transferred and accumulate throughout the multistage machining process, complicating the precise prediction of the final accuracy of thin-walled blades. Consequently, this paper introduces a machining accuracy prediction model for thin-walled blades that takes into account initial error. The machining error prediction model of thin-walled blades is developed using Gaussian process regression optimized by the sparrow search algorithm (SSA-GPR) with the initial contour error, depth of cut, feed per tooth, and spindle speed as inputs, and the machining error as the output. And the results show that the prediction accuracy of the SSA-GPR is 6.73 % higher than that of the Gaussian process regression (GPR), 13.73 % higher than that of the back propagation neural network (BPNN), and 32.32 % higher than that of the support vector machine regression (SVR). The influence of the initial error and milling parameters on the machining error is analyzed through the length-scales of the Gaussian kernel function. The findings indicate that the depth of cut, feed per tooth and initial error significantly affect the machining error, whereas the spindle speed has a minor impact on the machining error. Furthermore, the 3D graph based on the SSA-GPR shows that the increase of the initial error will increase the machining error of thin-walled blades. This research provides a theoretical foundation for the process optimization of thin-walled blades.

Evaluation of CNC lathe machine with fuzzy linguistic mcdm methods

CNC (Computerized Numerical Control) lathes have become integral to modern manufacturing and machining industries due to their ability to produce intricate parts with precision and efficiency. Not only do CNC lathes enhance productivity and accuracy, but they also minimize human error and enhance overall safety in the manufacturing process. Furthermore, the current market offers a wide array of diverse types of CNC lathes. Consequently, the evaluation and selection of CNC lathes pose a complex decision-making challenge as there are numerous types available, each with a variety of selection criteria for manufacturers to consider. It is crucial to make an informed choice, as improper evaluation and selection can have adverse effects on the overall performance of the production system. In this study, we propose using the fuzzy EDAS (Evaluation Based on Distance from Average Solution) model to evaluate and select CNC lathes. Initially, we employ the fuzzy analysis method, based on expert opinions, to establish a set of weights for the evaluation criteria. These criteria consist of seven factors: capital cost, spindle speed, distance between centers, rapid traverse rates in the X-axis and Z-axis, maximum machining diameter, and maximum machining length. Subsequently, the fuzzy EDAS object ranking model is utilized to evaluate and rank the CNC lathes, ultimately aiding in the selection of the most suitable machine for the manufacturer. The results obtained from our analysis reveal that the MICROTURN-300DX machine is the optimal choice, closely followed by the MICROTURN-300X machine. The study's findings serve as valuable guidelines for decision makers in selecting CNC lathes that align with the requirements of factory production. Moreover, the suggested approach can also be utilized to choose various other machine types as production demands become more intricate

A COMPARATIVE MODELING AND OPTIMIZATION OF SURFACE ROUGHNESS IN THE END MILLING OF Al 3003 SUBJECTED TO NON-EQUAL CHANNEL ANGULAR PRESSING (NECAP)

This paper highlights the surface roughness optimization of a specific material, Al 3003, which has been subjected to the non-equal channel angular pressing (NECAP) process. Considering spindle speed, feed rate, and depth of cut as input variables and surface roughness as an output variable, experiments have been conducted based on the L27 orthogonal array of the Taguchi method. Four prediction models, namely exponential and response surface methodology (RSM) as mathematical models, and artificial neural networks (ANNs) prediction models with different training algorithms (Bayesian Regularization (BR) and Levenberg–Marquardt (LM)), are proposed. Applying effectiveness and performance criteria, the prediction accuracy of the exponential model (90.35%), RSM (93.07%), BR (97.83%), and LM (97.54%) shows that all proposed prediction models are efficient enough. The ANN model trained with BR is found to be the best fit for predicting surface roughness. In order to optimize surface roughness, a newly introduced optimization method called the Intelligible-in-time Logics Algorithm (ILA) is employed. High spindle speed (1000[Formula: see text]rev/min), low feed rate (100[Formula: see text]mm/min) and depth of cut (0.5[Formula: see text]mm) have been the optimum cutting parameter combinations to obtain minimum surface roughness (0.4956[Formula: see text][Formula: see text]m). The results have been verified by confirmation tests and Particle Swarm Optimization (PSO) method. ILA and PSO predict the same optimum parameter combinations and minimum surface roughness, while ILA performs optimization in less time (114.4[Formula: see text]s), about 3.5 times faster than PSO. The paper’s findings strongly advocate the application of ILA in machining data optimization.