Depth Of Cut Research Articles

Effects of Coated Carbide Insert on Surface Finish of Multiple Hardened EN31 Alloy Steel Using Regression, ANN, and RSM

Objectives: Surface finish plays an essential role in every machining process. The current study mainly focuses on multiple hardened EN31 alloy steel, using various machining parameters and optimizing using regression, ANN, and RSM. Methods: The experimentation was performed using a commercial CNC machine and a surface roughness tester, using machining parameters such as tool nose radius (re), cutting speed (v), feed (f), depth of cut (d), and hardness (H) of the material ranging from 0.4 to 1.2 mm, 160 to 260 m/min, 0.1 to 0.20 mm/rev, 0.05 to 0.15 mm, and 45 to 49 HRC, respectively. Findings: The study revealed that cutting speed and feed rate significantly affect the surface finish, followed by tool nose radius. With the help of optimization techniques, the surface roughness predicted through RSM was 99.91%, followed by Regression and ANN. The contour plot predicts Ra using the best possible combinations of machining parameters. Novelty: The current research compares the surface finish results of multiple hardened EN31 alloy steels predicted through regression, RSM, and ANN with experimental values. The generated correlations will undoubtedly assist researchers in determining surface finish for the defined ranges of machining parameters. Keywords: Hard Turning, Coated Carbide, EN31 Steel, Surface Roughness, Optimization, RSM

Generation mechanism and anti-friction effect evaluation of continuous micro-groove texture machined by ball-end milling process

Abstract The tool path in ball-end milling process is flexible and controllable considering the structure characteristics and moving way of the cutter. This paper aims to reveal the generation method of continuous surface micro-groove texture by using ball-end milling process and evaluate the corresponding anti-friction performance by combining simulation and friction experiments. Results indicate that continuous micro-grooved surface texture can be obtained by combining small feed per tooth and large radial cutting depth. Radial depth of cut and tool radius affect aspect ratio of micro-groove. The bearing capacity of the oil film shows an increasing and then decreasing trend with the increase of radial depth of cut and orientation angle. Radial depth of cut increased from 0.1 mm to 0.3 mm, resulting in a maximum 4.87% increase in the bearing capacity of oil film because of the enhanced wedge effect. However, further increase in radial depth of cut leads to increased vortex, weakening the wedge effect. The oil film bearing capacity is reduced so that the friction coefficient increases by a maximum of 510%. Moreover, as the orientation angle increases from 10° to 20°, the bearing capacity of the oil film increases by 22.7%. The minimum friction coefficient currently is 0.0215. However, when the micro-groove orientation angle is further increased, the countercurrent effect is enhanced and the dynamic pressure effect is weakened, which finally leads to a decrease in the bearing capacity of the oil film. The friction coefficient has increased by 30.2% maximum. This study is of great significance which provides a new method for guiding the friction reduction on the die surface.

Experimental investigation into effects of natural stone properties on performance of a chisel tool

ABSTRACT The main purpose of this study is to determine how the linear cutting experiments should be performed on natural stones to better understand the stone cutting and predict cutting performance parameters from easily obtainable rock properties. A compiled database including thirty-four different natural stone samples in metamorphic, sedimentary and igneous geological origins is used to investigate the relationships between specific energy, mean normal and cutting forces and eleven different macro and micro properties of natural stones collected from quarries around Turkey. Linear cutting tests are performed by a portable linear cutting machine with a simple chisel tool at four different depths of cut in unrelieved (non-interactive) cutting mode. The difference in this study from previous investigations is the wide variety of stone samples with different characteristics to better explain the cutting mechanisms of natural stones. The results indicate that Shore scleroscope hardness and texture coefficient can be used to predict cutting performance parameters of natural stones. Additionally, using shallower depths of cut in linear cutting tests could lead to a better explanation of specific energy of natural stones.

Research Progress towards the Machining of Titanium Alloy Using CNC Milling: A Technical Review

Because of their extraordinary qualities, titanium alloys are very sought-after materials that can be applied to a wide range of sectors. Excellent mechanical and chemical qualities, including a high strength-to-weight ratio and resistance to corrosion, are present in it. The special properties of these alloys make machining them extremely difficult. As frequent tool wear occurs throughout the machining process, Computer Numerical Control (CNC) milling has become a potential method for machining titanium alloys due to its precision and versatility. This review article provides a comprehensive overview of the development of titanium alloy CNC milling, with an emphasis on the effects of cutting tool geometries and materials on machining efficiency. The process examines several aspects of cutting circumstances, including depth of cut, speed, feed rate, and lubrication techniques, and optimizes machining parameters and procedures to achieve the best results. Surface integrity and quality, surface roughness, residual stresses, and microstructural changes brought about by CNC milling are the main points of evaluation.

Characteristics of a novel elliptical ultrasonic vibration-assisted turning device

ABSTRACT 2D Ultrasonic Vibration-Assisted Turning (2D-UVAT) is a proven method to improve the traditional turning process. This process utilises bi-axial vibrations to enhance the smoothness of the machined surface and to suppress cutting temperature and cutting force. This study investigates the effects of employing a novel non-resonant vibration module, called the RNO vibrator, on the surface roughness, cutting temperature, and cutting force of aluminium alloy 6061-T6 machined using 2D-UVAT technique. The RNO vibrator operates by harnessing flexural hinges to disseminate and amplify the actuated vibrations originating from two orthogonal piezo actuator stacks. This vibrator was designed from a simple bar concept, expecting minimal adjustment on the tool post during installation. The experimental evaluation of the RNO vibrator’s performance was conducted by varying key machining parameters, including spindle speed, depth of cut, feed rate, and vibrational frequency. The study reveals that the introduced RNO vibrator substantially enhances machining quality by diminishing surface roughness and cutting temperature, while concurrently reducing cutting forces. Under the given conditions, the machined workpieces had a surface roughness between 0.41 and 1.25 µm and a cutting temperature ranging from 153.36°C to 180.5°C, while the cutting force was low, typically between 35.3 N and 145.5 N. The vibrator works better at high frequency, particularly at 20 kHz. Eventually, the study suggests that the RNO vibrator has a promising performance as a vibration module for the 2D-UVAT process.

Experimental Investigation and NSGA-III Multi-Criteria Optimization of 60CrMoV18-5 Cold-Work Tool Steel Machinability Under Dry CNC Hard Turning Conditions

This work concerns an experimental investigation dealing with the machinability of 60CrMoV18-5 cold-work tool steel under dry CNC hard turning conditions using a CBN cutting insert. A response surface experiment based on the central composite design was set to conduct dry CNC hard-turning experiments with three different levels for cutting conditions, cutting speed Vc (m/min), feed rate f (mm/rev), and depth of cut α (mm) while selecting main cutting force and surface roughness Ra as the two machinability responses. The results were analyzed by applying analysis of variance (ANOVA). The effect of cutting conditions on main cutting force and surface roughness was studied through contour plots. Full quadratic regression models were generated to model the relationships between inputs and outputs. Finally, the NSGA-III algorithm was applied to simultaneously optimize the selected machinability parameters by providing beneficial values for determining cutting conditions. The results have shown that surface roughness is mainly affected by feed rate and cutting speed, whereas main cutting force is affected by depth of cut and feed rate.

Performance analysis of turning operation parameters empirically on Delrin

Delrin is the best additional material for metals because of its inherent qualities, such as great wear resistance and tensile strength. The main aim of this work is to investigate how independent variables like feed rate, spindle speed, and depth of cut are significant for dependent variables such as surface roughness, temperature, stresses, and material removal rate on novel material Delrin. The independent variable ranges are selected based on tool and workpiece material combinations and machine tool specification as spindle speed 230 – 844 rpm, feed rate 0.5 – 1.5 mm/rev, and depth of cut 1 – 3 mm. Based on the L27 orthogonal array experimental plan 27 numbers of experiments are conducted by the design of experiment concepts. The theoretical investigation through response surface methodology is also conducted to establish how independent variables affected dependent variables when Delrin was being machined. The independent variable's significance is determined by the ANOVA table for all the considered responses. In addition to the considered flow of work, the experiential model is developed by the utilization of regression analysis. The developed models are confirmed by experimental data and the models have the best validation results with the experimental results.

Ultrasonic-assisted ultra-precision turning of zinc-selenide with straight-nosed diamond tools

This study proposes a novel ultra-precision machining technology that uses ultrasonic vibration and a straight-nosed diamond tool to improve the processing of the brittle optical material zinc selenide (ZnSe). This research addresses the challenges posed by Poisson's effect in ultrasonic vibration-assisted single-point diamond turning, which causes bending vibration along the depth of cut, resulting in lower machining efficiency and surface quality. This study analyses the relationship between one-dimensional ultrasonic vibrations at the diamond tool edge and induced bending vibrations using both theoretical and experimental methods. By investigating ultrasonic vibration dynamics in the feed direction and at the straight cutting edge, the results showed that ultrasonic vibration helps to improve the ductile-brittle transition ratio of the cutting area and surface quality. These improvements are accomplished by regulating the cutting position at the tool cutting edge, adjusting cutting parameters, and optimizing ultrasonic parameters. The machined surface roughness of ZnSe is reduced by approximately 30-46% at higher feed rates under ultrasonic vibration with straight-nosed diamond tools. The findings demonstrate the potential of this novel technology to reduce tool wear and brittle fractures, resolving the challenge of ultra-precision machining for optical materials.

Enhancing Machining Efficiency: Real-Time Monitoring of Tool Wear with Acoustic Emission and STFT Techniques

Tool wear in machining is inevitable, and determining the precise moment to change the tool is challenging, as the tool transitions from the steady wear phase to the rapid wear phase, where wear accelerates significantly. If the tool is not replaced correctly, it can result in poor machining performance. On the other hand, changing the tool too early can lead to unnecessary downtime and increased tooling costs. This makes it critical to closely monitor tool wear and utilize predictive maintenance strategies, such as tool condition monitoring systems, to optimize tool life and maintain machining efficiency. Acoustic emission (AE) is a widely used technique for indirect monitoring. This study investigated the use of Short-Time Fourier Transform (STFT) for real-time monitoring of tool wear in machining AISI 4340 steel using carbide tools. The research aimed to identify specific wear mechanisms, such as abrasive and adhesive ones, through AE signals, providing deeper insights into the temporal evolution of these phenomena. Machining tests were conducted at various cutting speeds, feed rates, and depths of cut, utilizing uncoated and AlCrN-coated carbide tools. AE signals were acquired and analyzed using STFT to isolate wear-related signals from those associated with material deformation. The results showed that STFT effectively identified key frequencies related to wear, such as abrasive between 200 and 1000 kHz and crack propagation between 350 and 550 kHz, enabling a precise characterization of wear mechanisms. Comparative analysis of uncoated and coated tools revealed that AlCrN coatings reduced tool wear extending tool life, demonstrating superior performance in severe cutting conditions. The findings highlight the potential of STFT as a robust tool for monitoring tool wear in machining operations, offering valuable information to optimize tool maintenance and enhance machining efficiency.

Modeling and Prediction of Surface Roughness in Ball End Milling of Oxygen-Free High Conductivity Copper Using Adaptive Neuro Fuzzy Inference System

Oxygen Free High Conducting Copper (OFHC) is one of the reasons materials for numerous applications due to its high thermal conductivity, great machinability, and great quality In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is developed as a predicting model of the machining performance of OFHC measured for surface roughness in terms of process parameters, namely, the cutting feed rate or feed per tooth, axial depth of cut, radial depth of cut, and the cutting speed.

Simultaneous cutting and grinding of micropins using cylindrical microtools

Decreasing cutting force is always crucial in cutting processes. One method to achieve this is by minimizing tool wear, for which rotary cutting is a highly effective technique. The use of a cylindrical tool offers the additional benefit of easy tool fabrication, which is especially advantageous for a microtool capable of machining micropins. However, there have been no reported studies on rotary cutting using a cylindrical microtool. It should be noted that if a microtool is fabricated by electrical discharge machining, it is expected to engage in cylindrical grinding as well. This suggests that both rotary cutting and cylindrical grinding can be carried out simultaneously; however, there have also been no reported studies on simultaneous cutting and grinding. Therefore, the present study investigated whether this machining method can perform micropin machining, if it actually involves both cutting and grinding actions, and whether the combination of these actions enhances material removal capability. In turning and grinding experiments, where only one of the tool and workpiece was rotated, both actions were observed. Furthermore, it was confirmed that micropin machining can be performed by rotating both the tool and workpiece, resulting in a decrease in machining force to approximately one-quarter to one-half compared to that of turning. This indicates that simultaneous cutting and grinding was carried out, and the overlap of their actions enhanced material removal capability. In addition, the relationship between the machining conditions and machining force was investigated. At a small depth of cut, a tool with smaller surface roughness exhibited lower machining force than a tool with larger roughness, and the opposite was true at a large depth of cut. Finally, an ultrasmall-diameter micropin with a diameter less than 3 μm was successfully machined using conditions capable of reducing machining force.

High-speed turning of AISI 4340 alloy steel using carbide tools in a sustainable minimum quantity lubrication environment

Purpose This study aims to investigate the machining performance of CVD-coated carbide tools by considering most crucial machinability aspects: cutting force, tool life, surface roughness and chip morphology in high-speed hard turning of AISI 4340 alloy steel under a sustainable minimum quantity lubrication (MQL) environment. Design/methodology/approach The purpose of this study is to analyze the performance of coated carbide tools under MQL environment therefore, machining tests were performed in accordance with the Taguchi L9 orthogonal array, accommodating the three crucial machining parameters such as cutting speed (V = 300–400 m/min), feed rate (F = 0.1–0.2 mm/rev) and depth of cut (DOC = 0.2–0.4 mm). The measured or calculated values obtained in each experimental run were validated for normality assumptions before drawing any statistical inferences. Taguchi signal-to-noise (S/N) ratio and analysis of variance methodologies were used to examine the effect of machining variables on the performance outcomes. Findings The quantitative analysis revealed that the depth of cut exerted the most significant influence on cutting force, with a contributing rate of 60.72%. Cutting speed was identified as the primary variable affecting the tool life, exhibiting a 47.58% contribution, while feed rate had the most dominating impact on surface roughness, with an overall contributing rate of 89.95%. The lowest cutting force (184.55 N) and the longest tool life (7.10 min) were achieved with low machining parameters at V = 300 m/min, F = 0.1 mm/rev, DOC = 0.2 mm. Conversely, the lowest surface roughness (496 nm) was achieved with high cutting speed, low feed rate and moderate depth of cut at V = 400 m/min, F = 0.1 mm/rev and DOC = 0.3 mm. Moreover, the microscopic examination of the chips revealed a serrated shape formation under all machining conditions. However, the degree of serration increased with an incremental raise with cutting speed and feed rate. Research limitations/implications The study is limited to study the effect of machining parameters within the stated range of cutting speed, feed rate and depth of cut as well as other parameters. Practical implications Practitioners may consider to adopt this machining technique to create more sustainable working environment as well as eliminate the disposal cost of the used metal cutting fluid. Social implications By applying this machining technique, diseases caused by metal cutting fluid to the mechanist will be significantly reduced, therefore creating better lifestyles. Originality/value Hard turning is commonly carried out with advanced cutting tools such as ceramics, cubic boron nitride and polycrystalline cubic boron nitride to attain exceptional surface finish. However, the high cost of these tools necessitates exploration of alternative approaches. Therefore, this study investigates the potential of using cost-effective, multilayer-coated carbide tools under MQL conditions to achieve comparable surface quality. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0013/

Chip formation analysis during dry turning of C45 steel

Machining is a material removal process that creates surfaces through chip formation using a cutting tool. The morphology of the chips and the mechanisms governing their formation directly affect the quality of machined parts. This experimental study examines chip formation mechanisms in the dry turning of C45 steel under various cutting conditions, utilizing coated carbide inserts. Machining performance was evaluated based on key chip morphology parameters, including shape, length, thickness, and volume. The results show that increasing cutting speed (Vc) produces shorter, thicker chips without significantly altering their predominantly helical shape. A higher feed rate (f) maintains the tangled ribbon form of the chips but leads to longer chip production. Additionally, changes in the depth of cut (ap) create instabilities that result in varied chip morphologies, including long tubular, short helical, and elementary forms. As the depth of cut increases, chips become shorter and thinner. These findings provide valuable insights into the material's deformation and fracture mechanisms, enhancing our understanding of chip formation and its impact on the efficiency of dry turning operations on C45 steel.

Optimizing dry milling of stir-cast and heat-treated AZ80 magnesium alloy using multiple criteria optimization technique: an experimental study.

The primary aspects of this research are to evaluate surface roughness, cutting force, and material removal rate and optimize it with dry milling process parameters for heat-treated and stir-cast AZ80 magnesium alloy. Multiple methodologies are utilized in the research, which includes the Integration of design of experiments-response surface methodology for experimental design with the technique for order of preference by similarity to ideal solution for multi-criteria optimization. In order to evaluate the effect of process parameters on the response, the experimental design manipulates the depth of cut, feed rate, and cutting speed in a systematic manner. An evaluation of the machined surface's quality is conducted via surface roughness measurements. Likewise, insights into the forces exerted during milling can be obtained through continuous monitoring of cutting forces. The calculation of material removal rate is predicated on weight reduction. The interaction between the depth of cut and feed rate has a significant impact on the critical-to-quality characteristics of the alloy, which has contribution percentage greater than 25%. This finding validates that despite the heat-treated alloy having a similar composition to the as-cast alloy (where the closeness coefficient is 0.9843), the optimal process parameters of the former are not applicable to the latter. Nevertheless, the technique used to prepare the specimen has no bearing on the material removal rate, which is a process parameter-specific effect.

Digital dynamic modeling and topology optimized design of shell face mills

This paper presents digital modeling of shell face mills in milling cylinder heads. The cutter's structural dynamics and its mode shapes are predicted using a Finite Element system. The geometries of the cutter body and inserts are imported from their Computer Aided Design (CAD) models. The insert edge is discretized into small segments to model its varying normal rake and inclination angles, which affect the cutting mechanics. The cutter is dynamically assembled with the target machine tool spindle using the receptance coupling method. A general dynamic cutting force model, which considers the varying edge geometry and inserts’ run-outs, is developed and used to predict cutting forces and chatter stability diagrams. The proposed model is experimentally verified to demonstrate the feasibility of the systematic application of physics-based digital design and analysis of tools for the mass machining of specific parts. The cutter body shape is optimized to increase the stiffness of the bending mode shape that caused chatter via topology optimization, which led to five-fold increase in the absolute stable depth of cut.

Effect of Laser Power and Diamond Tool Parameters for Micro Laser-Assisted Ductile Mode Material Removal on Fused Silica

Abstract Fused silica is an essential material used in various applications due to its excellent optical and mechanical properties. However, processing it can be challenging due to its high hardness and brittleness, leading to sub-surface damage during grinding and polishing operations. Micro laser-assisted ductile mode material removal is a promising technique for achieving high levels of precision and accuracy in material removal. This technique leads to controlled and precise removal of material without inducing cracks or fractures. Despite its advantages, there are several challenges associated with the process, such as selecting the appropriate laser power and diamond tool geometry. In this study, we employed a Universal Mechanical Tester equipped with modified OPTIMUS, a laser-assisted machining technology to investigate the impact of laser and diamond tool geometry on scratch cut quality. Introducing and increasing the laser power demonstrated an improvement of around 251.7% in cut ductility, leading to a decrease in the severity of sub-surface damage (SSD). Altering the rake angle from -25° to -45° resulted in a 45.3% reduction in the critical depth of cuts (DOCs). However, when laser was employed, the critical DOCs increased around 34.4% in ductile mode material removal on fused silica samples, which underscores the criticality of the laser in this process.

Portable Machine Tools by Small Piezoelectric Robots for Scalable and Waviness‐Adaptive Fabrication of Surface Microstructures

Surface functional microstructures exhibit extensive application requirements in an array of breakthrough areas. One critical problem that restricts their industrial application is the lack of scalable fabrication techniques due to the limitation of conventional machine tools. This study proposes a scalable surface texturing technique using a portable small (30 × 19 × 22 mm) three‐leg robot that walks and works on the workpiece surface. Due to the elliptical tool vibration, microgrooves can be created on the workpiece surface periodically; meanwhile, the machining force drives the robot to walk forward. Surface texturing experiments are conducted on aluminum and copper workpieces to explore the machining performance of the small robot. The robot can reach a maximum moving velocity of 6.3 mm s−1 and can produce microstructures with a spacing of 4–14 μm on workpiece surfaces. Owing to its unique working principle, the small robot can maintain a constant depth of cut, demonstrating its capacity to adapt to the surface waviness of the workpiece. Finally, the motion straightness of the robot is greatly improved by combining it with the auxiliary track, and multiline microstructures are obtained. In short, the developed small robot presents a promising solution to the challenge of scalable surface texturing.

Surface Quality Evaluation of 3D-Printed Carbon-Fiber-Reinforced PETG Polymer During Turning: Experimental Analysis, ANN Modeling and Optimization

This work presents an experimental analysis related to 3D-printed carbon-fiber-reinforced-polymer (CFRP) machining. A polyethylene-terephthalate-glycol (PETG)-based composite, reinforced with 20% carbon fibers, was selected as the test material. The aim of the study was to evaluate the influence of cutting conditions used in light operations on the generated surface quality of the 3D-printed specimens. For this purpose, nine specimens were fabricated and machined under a wide range of cutting parameters, including cutting speed, feed, and depth of cut. The generated surface roughness was measured with a mechanical gauge and the acquired data were used to develop a shallow artificial neural network (ANN) for prediction purposes, showing that a 3-6-1 structure is the best solution. Following this, a genetic algorithm (GA) was utilized to minimize the response, revealing that the optimal combination is 205 m/min speed, 0.0578 mm/rev feed, and 0.523 mm depth of cut, contributing to the fabrication of low friction parts and shafts with a high quality surface, as well as to the reduction of resource waste. A validation study supported the accuracy of the developed model, by exhibiting errors below 10%. Finally, a set of enhanced images were taken to assess the machined surfaces. It was found that 1.50 mm depth of cut is responsible for the generation of defects across the circumference of the specimens. Especially, combined with 150 m/min cutting speed and 0.11 mm/rev feed, more flaws are produced.

Optimizing Cylindrical Grinding Proficiency and Surface Finish for EN24 Steel Alloy with the Use of Taguchi Analysis and Artificial Neural Network

Grinding is a material removal procedure used at the end of manufacturing to obtain high dimensional precision and a desirable surface polish. External cylindrical grinding is a type of grinding procedure that used to grind cylindrical things such as engine shafts, connecting rods, spindles, and axles. This study examines how control settings affect response parameters using EN24 alloy steel on a cylindrical grinding machine. Response parameters are material removal rate (MRR) and surface roughness (Ra), whereas control parameters are workpiece speed (v), feed rate (f), and depth of cut (d). Taguchi DOE is employed with L16 orthogonal array to optimize control parameter levels, followed by cylindrical grinding tests.An artificial neural network (ANN) model was created to validate and predict cylindrical grinding reaction parameters. To train the network, experimental data was used, and ANN predictions were compared to real data. Data collection has also completed.In the purposed work minimum Ra is 0.52 µm and maximum MRR is 0.6588 g/sec found and results indicate that feed rate and workpiece speed are the most dominating parameters of cylindrical grinding. The experimental results can be employed by various manufacturing and industrial firms and they can select suitable combinations of input parameters for desired Ra and MRR

Experimental study of the surface finishing of CNC magnetic abrasive finishing based on ANN

ABSTRACT CNC Magnetic Abrasive Finishing (CNC MAF) is famous for aluminium sheet processing. This study used an artificial neural network (ANN) analytical model with a Bayesian regularisation algorithm (BR) to examine how machine parameters (rotation speed, feed rate, depth of cut, and several passes) affect surface roughness during CNC MAF of aluminium sheet. Experimental and quantitative data proved its prediction ability. ANN model to select the best machine parameters for low surface roughness. The perfect numerical solution was experimentally tested, and surface morphology and roughness were used to evaluate processing quality under optimal machine parameters. The ANN model is also compared to Taguchi L16 for prediction and optimisation. Compared to ANN, prediction and optimal surface finishing mean absolute error are 96% lower. The results show that the ANN model is better at studying machine configuration during CNC MAF of Al sheet. SEM and 3D Optical profilometer were also used to analyze the Al sheet. The UV-VIS Spectrophotometer measures Al polished sample reflectance at 200–700 nm.