Influence Of Machining Parameters Research Articles

Intelligent identification of machining damage in ceramic matrix composites based on deep learning

This study proposed a method for identifying and quantitatively evaluating the machining damages of CMCs based on deep learning. Firstly, grinding tests of CMCs were conducted to create a dataset of machining damages. Then, six deep learning algorithms were trained using the dataset, and their comprehensive performance was compared. The results showed that YOLOv8 exhibited superior overall performance among the six algorithms. Besides, a professional software for identifying machining damage of CMCs was developed based on the optimal algorithm, and the influence of machining parameters on CMCs damages was investigated. Qualitative and quantitative evaluation results indicate that grinding speed is negatively correlated with the machining damage degree, and a higher grinding speed leads to less damages. In contrast, both feed rate and grinding depth are positively related to the machining damage. Furthermore, it is verified that the developed software is applicable to various conditions and has certain engineering application prospects.

Correction to: Influence of machining parameters on edge quality of particleboards: modeling and optimization

Correction to: Influence of machining parameters on edge quality of particleboards: modeling and optimization

Machinability of elliptical ultrasonic vibration milling γ-TiAl: Chip formation, edge breakage, and subsurface layer deformation

Superior strength and high-temperature performance make γ-TiAl vital for lightweight aero-engines. However, its inherent brittleness poses machining problems. This study employed Elliptical Ultrasonic Vibration Milling (EUVM) to address these problems. Considering the influence of machining parameters on vibration patterns of EUVM, a separation time model was established to analyze the vibration evolutionary process, thereby instructing the cutting mechanism. On this basis, deep discussions regarding chip formation, cutting force, edge breakage, and subsurface layer deformation were conducted for EUVM and Conventional Milling (CM). Chip morphology showed the chip formation was rooted in the periodic brittle fracture. Local dimples proved that the thermal effect of high-speed cutting improved the plasticity of γ-TiAl. EUVM achieved a maximum 18.17% reduction in cutting force compared with CM. The force variation mechanism differed with changes in the cutting speed or the vibration amplitude, and its correlation with thermal softening, strain hardening, and vibratory cutting effects was analyzed. EUVM attained desirable edge breakage by achieving smaller fracture lengths. The fracture mechanisms of different phases were distinct, causing a surge in edge fracture size of γ-TiAl under microstructural differences. In terms of subsurface deformation, EUVM also showed strengthening effects. Noteworthy, the lamellar deformation patterns under the cutting removal state differed from the quasi-static, which was categorized by the orientation angles. Additionally, the electron backscattering diffraction provided details of the influence of microstructural difference on the orientation and the deformation of grains in the subsurface layer. The results demonstrate that EUVM is a promising machining method for γ-TiAl and guide further research and development of EUVM γ-TiAl.

Cutting force reduction mechanism in ultrasonic cutting of aramid honeycomb

Aramid honeycomb has extensive applications in the aerospace and rail transportation industries. Ultrasonic cutting (UC) with the straight blade (SB) is an advanced technology used for the profile machining of honeycomb materials. Cutting force is an important factor affecting machining quality, and its accurate modelling is particularly important. However, SB is a flake tool, and chips of honeycomb are removed in block or strip forms during cutting, and its structure and material removal method are obviously different from conventional machining. Existing simplified cutting force models based on kinematic analyses and experiments are incapable of describing the cutting process. In this paper, a geometrical kinematics analysis of SB is conducted. Further, the modelling of tool-workpiece contact rate (TWCR) for UC with SB is established, which is utilized to analyze the tool-material contact state. Based on the principle of conservation of energy, the fracture force at the blade edge is analyzed. Based on the analysis of instantaneous friction force, the friction force model on blade surfaces is established. Multiple-cutting experiments are conducted to separate fracture force and friction force. The influence of machining parameters (ultrasonic amplitude, lead angle, cutting speed) on TWCR, cutting force, fracture force, and friction force have been theoretically and experimentally studied, and the reduction mechanism of the cutting force is revealed. Furthermore, the machining quality under different machining parameters is observed and the relationship with cutting force is analyzed. The paper not only provides theoretical guidance for the study of the UC mechanism but also helps to guide the UC process of aramid honeycomb.

Extensive evaluation of microhardness alterations induced in subsurface layers after hard machining of AISI 4340 steel and process optimization

ABSTRACT Hardened AISI 4340 steel is extensively utilised in engineering industries. Owing to the fact that thermo-mechanical loads are applied to surface layers during the hard machining process, severe changes occur at the surface of the machined workpiece. These changes can affect some mechanical properties of the material, including microhardness, and residual stresses generation in material. Therefore, it is significantly influenced by process conditions and there is not any study in literature to extensively evaluate and optimise different indications of microhardness alteration after hard machining of AISI 4340 Steel. In this study, the influence of machining parameters was firstly studied on the microhardness distribution at machining of hardened AISI 4340 steel by experimental tests. Then, the optimal surface microhardness and depth of the affected layer were assessed using the intelligent techniques. In this regard, the optimal process conditions were simultaneously determined using the combination of the Artificial Neural Networks (ANNs) and Non-dominated Sorting Genetic Algorithm (NSGA-II). The results indicated that, both depth of the affected layer and surface microhardness were obtained in optimal state when the cutting speed changes from 720 to 600 (Rpm) and feed rate and depth of cut are 0.05 (mm/rev) and 0.4 (mm), respectively.

Optimizing highly reflective CNC abrasive polishing on aluminum sheets

ABSTRACT The polishing industry’s demand for achieving a smooth, highly reflective surface, flat, particle-free metallic surface, crucial for imaging and optical applications has increased. As aluminum metallic mirrors are more in demand, an eco-friendly, cost-effective mechanical polishing procedure that can be carried out on a CNC milling machine is proposed. The proposed CNC Abrasive Polishing (CNC AP), performed on CNC machines achieves 3 nm roughness average with high reflectivity on aluminum sheets. The influence of machining parameters, such as including polishing wheel speed (S), depth of cut (DoC), feed rate (FR), and passes, to evaluate the surface quality (Ra), material removal rate, and reflectance were studied, using Response Surface Methodology. The experiments were conducted based on the L16 orthogonal array. A 3D optical profilometer, an atomic force microscope (AFM), and a scanning electron microscope (SEM) with EDS are among the advanced tools used for analyzing polished surfaces. UV-VIS spectrophotometer measurements indicated a substantial 35% to 96% improvement in the optical qualities. Additionally, a simple visual reflective grid method was used to assess the deformation behavior of the reflective Al sheets.

An Experimental and Numerical Study of the Laser Ablation of Bronze

The use of lasers in various precise material removal processes has emerged as a viable and efficient alternative to traditional mechanical methods. However, the laser ablation of materials is a complex, multi-parameter process where scanning paths need to be repeated multiple times. This repetition causes changes in the absorption and temperature distribution along the scanning path, thereby affecting the accuracy of the ablation. Therefore, it is crucial to thoroughly study these phenomena. This article presents an experimental and numerical study on the laser ablation of bronze (DIN: 1705) in a multi-track ablation process. Specifically, six consecutive passes using a ns laser at three different energy densities were conducted. After each pass, measurements of the ablation depth and pile-up height were taken at three distinct points along the track (start, middle, and end) to evaluate the efficiency and quality of the process. To gain a deeper understanding of the underlying physical mechanisms, a numerical simulation model based on the Finite Element Method (FEM) was developed. The effective absorptivity was defined through reverse engineering, and the material’s cooling rates were also estimated. This study’s findings provide significant insights into the influence of machining parameters on the ablation process and its progression with varying numbers of consecutive repetitions. A primarily linear correlation was deduced between the ablation depth, energy density, and number of repetitions, while the relationship with the pile-up height appeared to be more ambiguous and nonlinear. The estimated cooling rates ranged from 106 to 1010 [K/s]. Additionally, a heat accumulation phenomenon and a gradual temperature increase resulting from consecutive laser scans were also observed. A good agreement between the simulation results and experiments for the ablation depths was observed.

Influence of machining parameters on tool wear, residual stresses, and corrosion resistance after milling super duplex stainless steel UNS S32750

Influence of machining parameters on tool wear, residual stresses, and corrosion resistance after milling super duplex stainless steel UNS S32750

Investigation on the machinability of SiCp/Al composite by in-situ laser assisted diamond cutting

As a difficult-to-cut material, high-quality machining of SiCp/Al composite is a huge challenge for conventional machining techniques. In this study, an in-situ laser assisted diamond cutting technology was innovatively applied to SiCp/Al composite machining. The influence of machining parameters on machined surface roughness and morphology was analyzed. The wear mechanism of the diamond tools with conventional and in-situ laser assisted diamond cutting was revealed. Subsurface damage was investigated from residual stress and crystal defects. Experimental results show that the in-situ laser assisted diamond cutting technology effectively inhibits the generation of large-sized surface defects such as scratches and pits, and improves surface quality. Compared with conventional diamond cutting, the best surface roughness Sa is significantly decreasing by 89.5% to 10.2 nm. In addition, residual stress is effectively reduced by 42%. In in-situ laser assisted diamond cutting, the main tool wear form is flank wear caused by abrasive wear and chemical wear. During processing, an amorphous layer of about 40 nm is generated on the machined surface, accompanying subsurface defects such as particle fracture, micro-crack, dislocations, and stacking faults. The in-situ laser assisted diamond cutting is an effective and promising method for ultra-precision machining SiCp/Al composites.

Surface Quality Improvement for Ultrasonic-Assisted Inner Diameter Sawing with Six-Axis Force Sensors.

Ultrasonic-assisted inner diameter machining is a slicing method for hard and brittle materials. During this process, the sawing force is the main factor affecting the workpiece surface quality and tool life. Therefore, based on indentation fracture mechanics, a theoretical model of the cutting force of an ultrasound-assisted inner diameter saw is established in this paper for surface quality improvement. The cutting experiment was carried out with alumina ceramics (99%) as an exemplar of hard and brittle material. A six-axis force sensor was used to measure the sawing force in the experiment. The correctness of the theoretical model was verified by comparing the theoretical modeling with the actual cutting force, and the influence of machining parameters on the normal sawing force was evaluated. The experimental results showed that the ultrasonic-assisted cutting force model based on the six-axis force sensor proposed in this paper was more accurate. Compared with the regular tetrahedral abrasive model, the mean value and variance of the proposed model's force prediction error were reduced by 5.08% and 2.56%. Furthermore, by using the proposed model, the sawing processing parameters could be updated to improve the slice surface quality from a roughness Sa value of 1.534 µm to 1.129 µm. The proposed model provides guidance for the selection of process parameters and can improve processing efficiency and quality in subsequent real-world production.

Prediction of line heating deformation on sheet metal based on an ISSA–ELM model

A prediction method based on an improved salp swarm algorithm (ISSA) and extreme learning machine (ELM) was proposed to improve line heating and forming. First, a three-dimensional transient numerical simulation of line heating and forming was carried out by applying a finite element simulation, and the influence of machining parameters on deformation was studied. Second, a prediction model for the ELM network was established based on simulation data, and the deformation of hull plate was predicted by the training network. Additionally, swarm intelligence optimization, particle swarm optimization (PSO), the seagull optimization algorithm (SOA), and the salp swarm algorithm (SSA) were studied while considering the shortcomings of the ELM, and the ISSA was proposed. Input weights and hidden layer biases of the ELM model were optimized to increase the stability of prediction results from the PSO, SOA, SSA and ISSA approaches. Finally, it was shown that the prediction effect of the ISSA–ELM model was superior by comparing and analyzing the prediction effect of each prediction model for line heating and forming.

Fatigue analysis of SAE8620: Synergistic effect of thermal treatments and surface roughness

ABSTRACT In the present paper, the influence of machining parameters and thermal treatment cycles on the fatigue life of SAE8620 has been investigated. Experiments were designed as per L9 array and a set of nine fatigue test samples were prepared using CNC machine. Spindle speed (1900, 2100, and 2300 rpm), feed rate (0.15, 0.175, and 0.2 mm/min), and depth of cut (0.2, 0.4, and 0.6 mm) were the three machining parameters that were included for the analysis. The three different cooling rates were annealing (0.8℃/min), normalising (13℃/min), and water quenching (421℃/min). Surface roughness testing were carried out first, followed by fatigue tests. The Taguchi method was used to optimise the machining settings (spindle speed − 2100 rpm, feed rate − 0.175 mm/min and depth of cut − 0.4 mm). To determine the missing output values, the ANFIS modelling tool was employed and the predicted value was 1.07 µm. The optimal surface roughness (1.261 µm) and fatigue cycle (41149 cycles) were obtained by grey relation analysis for input parameters (spindle speed − 2300 rpm, feed rate − 0.175 mm/min and depth of cut − 0.4 mm). The so obtained optimised surface roughness values found to be close by 15.14%. It was found that fatigue life get enhanced due to synergistic effect of heat treatment (average 56%) and surface roughness (25%), respectively. Therefore, inference can be made that the fatigue life can be significantly influenced by the heat treatment and surface roughness.

Influence of Machine Parameters and Coagulant on the Textural Properties of Paneer (Indian Cottage Cheese)

Paneer is an acid coagulated concentrated indigenous Indian dairy product. The texture of paneer manufactured from a developed automatic machine was studied with respect to the processing parameters such as coagulation temperature (70–90°C), pressure (0.3–0.6 MPa), types of coagulant (vinegar and citric acid), and milk (cow, buffalo, and full cream milk). The paneer samples were analysed for texture profile analysis of hardness, adhesiveness, springiness, gumminess, chewiness, and cohesiveness. An inscribed central composite design was used to optimize the desired textural characteristics of paneer such as minimum hardness and maximum springiness of paneer. The optimization result concluded that the full cream milk at the coagulation temperature of 88°C with citric acid at pneumatic pressure of 0.3 MPa would result on minimum hardness of 105.84 N and the springiness value of 0.6073, and the result was validated.

Prediction of cutting forces using MRA, GMDH and ANN techniques in micro end milling of titanium alloy

Micro End Milling is an specific procedure for manufacturing biomedical, automotive, naval, aerospace and mining parts especially for super alloys. Surface finish is the necessity output parameter after machining these alloys in the said applications. The study of cutting forces is mandatory to know the required surface finish to be generated on the work material. The influence of machining parameters can be investigated by experimental and predicted results that are very useful for the practitioner engineers inorder to evaluate. In this manuscript, experimental investigation of cutting forces was done and a comparison of the modelling methods - multiple regression analysis (MRA), Group method data handling (GMDH) and Artificial Neural Network (ANN) were presented. In the manuscript, the impact of input factors in machining of Ti-6Al-4 V (Grade-5) titanium alloy using uncoated tungsten carbide tools was studied on cutting forces. From the experimental interpretations, it is found that while increasing machining input parameters cutting forces on workpiece subsequently increases. After the adherences of experiments, experimental values are in good agreement with the accuracy of neural network prediction.

Predicting Surface Residual Stress for Multi-Axis Milling of Ti-6Al-4V Titanium Alloy in Combined Simulation and Experiments

As one essential indicator of surface integrity, residual stress has an important influence on the fatigue performance of aero engines’ thin-walled parts. Larger compressive or smaller tensile residual stress is more prone to causing fatigue cracks. To optimize the state of residual stress, the relationship between the surface residual stress and the machining conditions is studied in this work. A radial basis function (RBF) neural network model based on simulated and experimental data is developed to predict the surface residual stress for multi-axis milling of Ti-6Al-4V titanium alloy. Firstly, a 3D numerical model is established and verified through a cutting experiment. These results are found to be in good agreement with average absolute errors of 11.6% and 15.2% in the σx and σy directions, respectively. Then, the RBF neural network is introduced to relate the machining parameters with the surface residual stress using simulated and experimental samples. A good correlation is observed between the experimental and the predicted results. The verification shows that the average prediction error rate is 14.4% in the σx direction and 17.2% in the σy direction. The effects of the inclination angle, cutting speed, and feed rate on the surface residual stress are investigated. The results show that the influence of machining parameters on surface residual stress is nonlinear. The proposed model provides guidance for the control of residual stress in the precision machining of complex thin-walled structures.

Numerical modeling and simulation of machining of 3D printed CFRP composite

Carbon Fiber Reinforced Polymer (CFRP) composites are gaining popularity in recent years for their high specific strength and superior performance under extreme conditions. Additive manufacturing (AM) or 3D printing of CFRP composites has added a new dimension to the existing research of CFRP composites. However, one of the caveats of 3D printed CFRP composites is that the dimensional accuracy and the surface finish achieved fail to meet the standard tolerance requirement. To achieve the desired accuracy further post-process machining is required. However, machining an anisotropic material such as 3D printed CFRP composites impose challenges. This study aims at investigating the machining behavior and post-processing capability of 3D printed CFRP using numerical modeling and simulation using ABAQUS/Explicit, a commercially available powerful finite element analysis (FEA) software package. Influence of machining parameters, such as feed and depth of cut (DOC) and 3D printing parameter, such as percentage of overlap between adjacent passes have been studied. The post-processing capability or machining behavior of 3D printed CFRP has been evaluated by dependent parameters, such as, cutting forces, strain, chip and burr formation, and the resultant surface topology. With the use of proper damage parameters, such as, ductile and shear damage and Johnson-Cook criterion for plasticity, a 3D model was developed in the FEA. Eight layers were stacked with the orientation of 45°-135°-45°-135°. The layers were joined using cohesive properties. Damage in the cohesive bonds was also defined using the quadratic damage criterion to model delamination. To capture the changes in elemental level, linear tetrahedral elements C3D4T, were used. A reference point on the bottom layer was used to capture the history of cutting forces. The cutting and thrust forces increased with the increase of DOC and feed. The rigidity, shape of the flutes and print direction influenced chip formation. Finally, the proposed model was used to observe the quality of surface finish.

EFFECT OF PROCESS PARAMETERS IN MICRO-HOLE MACHINING ON α–β Ti-ALLOY USING ECμM WITH NONAQUEOUS ELECTROLYTE OF (CH2OH)2–NaBr

[Formula: see text]–[Formula: see text] titanium alloy (Ti-6Al-4V) is being used in diverse applications, such as aircraft, chemical, automotive, and biomedical industries due to its excellent attractive properties. But machining of titanium and its alloy is a tedious task through conventional material removal processes. Removing the material at the micro level to create micro-sized features like a hole, slot and intricate projection in the titanium alloy is more complicated. In this paper, micro-sized through-hole machining on the [Formula: see text]–[Formula: see text] titanium alloy has been performed using electrochemical micro-machining (EC[Formula: see text]M) with nonaqueous ethylene glycol (CH2OH)2–sodium bromide (NaBr) electrolyte and the influence of machining parameters, such as electrolyte concentration (EC), machining voltage (MV), tool feed rate (TFR) and duty cycle (DC) during micro-hole machining on the [Formula: see text]–[Formula: see text] titanium alloy is analyzed. The material removal rate (MRR), radial overcut (ROC), conicity ([Formula: see text]), and surface roughness (SR) of the micro-sized through hole have been ascertained and analyzed. The selected machining parameters are optimized using Taguchi based Grey relation analysis and DEAR methods.

Optimization of the process parameters for micro-milling thin-walled micro-parts using advanced algorithms

The surface integrity and machining accuracy of thin-walled micro-parts are significantly affected by micro-milling parameters mostly because of their weak stiffness. Furthermore, there is still a lack of studies focusing on parameter optimization for the fabrication of thin-walled micro-scale parts. In this paper, an innovative approach is proposed for the optimization of machining parameters with the objectives of surface quality and dimension accuracy, which integrates the Taguchi method, principal component analysis method (PCA), and the non-dominated sorting genetic algorithm (NSGA-II). In the study, surface arithmetic average height Sa, surface root mean square height Sq, and 3-D fractal dimension Ds are selected to evaluate surface quality. Then micro-milling experiments are conducted based on the Taguchi method. According to the experimental results, the influence of machining parameters on the processing quality has been investigated based on the cutting force and machining stability analysis, and the significance of machining parameters can be determined by range analysis. Besides, regression models for the responses are developed comparatively, and the PCA method is employed for dimension reduction of the optimization objective space. Finally, two combinations of machining parameters with the highest satisfaction are obtained through NSGA-II, and verification experiments are carried out. The results show that the surface quality and dimension accuracy of the thin-walled micro-scale parts can be simultaneously improved by using the proposed approach.

Research on Milling Characteristics of Titanium Alloy TC4 with Variable Helical End Milling Cutter

The application of variable helical end mills to the milling of titanium alloys can suppress the regeneration effect during the machining process and ensure the stability of milling. However, due to their special geometry, their milling characteristics are also different. In this paper, the process of milling titanium alloy with a variable helix end mill is taken as the research object, and the milling force, milling stability and machining effect during the machining process are deeply studied. Firstly, the feed per tooth and cutting thickness model of the variable helical end mill were established, the milling force prediction model of the variable helical end mill was deduced, and the instantaneous milling force and its variation law were obtained by solution. Secondly, the finite element analysis model of the variable helix end mill for machining titanium alloy was established, and the influence of the variable helix angle structure on the milling force was obtained. Then, the dynamic equation of milling with a variable helix end mill was established, the stability lobe diagram of variable helix milling is was and drawn, and the influence of variable helix angle on milling stability was analyzed. Lastly, a variable helix end mill milling experiment was designed to verify the accuracy of the theoretical model and finite element simulation; the influence of the variable helix angle structure on the surface roughness was analyzed, and the influence of machining parameters on the milling force when using a variable helix end mill was investigated.

Sustainable Lubrication/Cooling Systems for Efficient Turning Operations of γ-TiAl Parts from the Aeronautic Industry

This paper presents the study of the finishing, repair, and maintenance turning operations of gamma titanium aluminide (γ-TiAl) parts from the aeronautic industry, with the aim to evaluate different sustainable lubrication/cooling environments, including a newly developed synthetic ester water-based metalworking fluid (EcoMWF) to replace mineral-based MWF (MWF). The systems considered in this work are dry, cold-compressed air, minimum quantity lubrication (MQL), cryogenic, and flood on turning of a new and relatively low explored titanium alloy, γ-TiAl. Therefore, the influence of machining parameters and insert type on tool wear, surface roughness, roundness, and cutting temperature have been investigated for each environment. Results detailed in this study showed a significant influence of the lubrication/cooling systems on the machinability of γ-TiAl. The study also revealed that the sustainability of turning γ-TiAl could be improved under the cryogenic system and the new EcoMWF, keeping the same machining performance as common mineral-based MWF.Graphical abstract