Search Results (171)

The study is motivated by the application of dry finish milling for post-build processing of additive Ti6Al4V blanks, since the use of neither lubricant nor coolants has been attracting increasing attention due to its environmental benefits, non-toxicity, and the elimination of the need for additional cleaning processes. For end mills, wear patterns were investigated upon finish milling of the SLM Ti6Al4V samples under various machining conditions (by varying the values of radial depth of cut and feed values at a constant level of axial depth of cut and cutting speed). When using all the applied milling modes, the identical tool wear mechanism was revealed. Built-up edges mainly developed on the leading surfaces, increasing the surface roughness on the SLM Ti6Al4V samples but protecting the cutting edges. However, abrasive wear was mainly characteristic of the flank surfaces that accelerated peeling of the protective coatings and increased wear of the end mills. The following milling parameters have been established as being close to rational ones: Vc = 60 m/min, Vf = 400 mm/min, a_p = 4 mm, and a_e = 0.4 mm. They affected the surface roughness of the SLM Ti6Al4V samples in the following way: max cutting thickness—8 μm; built-up edge at rake surface—50 ± 3 μm; max wear of flank surface—15 ± 1 μm; maximum adherence of workpiece. Mode III provided the maximum MRR value and negligible wear of the end mill, but its main disadvantage was the high average surface roughness on the SLM Ti6Al4V sample. Mode II was characterized by both the lowest average surface roughness and the lowest wear of the end mill, as well as an insufficient MRR value. Since these two modes differed only in their feed rates, their values should be optimized in the range from 200 to 400 mm/min. Full article

Stainless steel is recognized for its excellent durability and anti-corrosion properties, which are essential qualities across various industrial applications. The machining of stainless steel, particularly under a dry environment to attain sustainability, poses several challenges. The poor heat conductivity and high ductility of stainless steel results in poor heat distribution, accelerating tool wear and problematic chip formation. To mitigate these challenges, the implementation of surface texturing has been identified as a beneficial strategy. This study investigates the impact of wave-type texturing patterns, developed on the flank surface of tungsten carbide ceramic tool inserts, on the machinability of AISI 316 stainless steel under dry cutting conditions. In this investigation, chip morphology and surface roughness were used as key indicators of machinability. Analysis of Variance (ANOVA) was conducted for chip thickness, chip thickness ratio, and surface roughness, while Taguchi mono-objective optimization was applied to chip thickness. The ANOVA results showed that linear models accounted for 71.92%, 83.13%, and 82.86% of the variability in chip thickness, chip thickness ratio, and surface roughness, respectively, indicating a strong fit to the experimental data. Microscopic analysis confirmed a substantial reduction in chip thickness, with a minimum observed value of 457.64 µm. The corresponding average surface roughness Ra value 1.645 µm represented the best finish across all experimental runs, highlighting the relationship between thinner chips and enhanced surface quality. In conclusion, wave textures on the cutting tool’s flank face have the potential to facilitate the dry machining of AISI 316 stainless steel to obtain favorable machinability. Full article

This study explores the machinability of Incoloy 800HT (high temperature) under a sustainable lubrication approach, employing a twin-nozzle minimum quantity lubrication (MQL) system with groundnut oil as a green cutting fluid. The evaluation focuses on key performance indicators, including surface roughness, tool flank wear, power consumption, carbon emissions, and chip morphology. Groundnut oil, a biodegradable and nontoxic lubricant, was chosen to enhance environmental compatibility while maintaining effective cutting performance. The Taguchi L₁₆ orthogonal array (three factors and four levels) was utilized to conduct experimental trials to analyze machining characteristics. The best surface quality (surface roughness, Ra = 0.514 µm) was obtained at the lowest depth of cut (0.2 mm), modest feed (0.1 mm/rev), and moderate cutting speed (160 m/min). The higher ranges of flank wear are found under higher cutting speed conditions (320 and 240 m/min), while lower wear values (<0.09 mm) were observed under lower speed conditions (80 and 160 m/min). An entropy-integrated multi-response optimization using the MOORA (multi-objective optimization based on ratio analysis) method was employed to identify optimal machining parameters, considering the trade-offs among multiple conflicting objectives. The entropy method was used to assign weights to each response. The obtained optimal conditions are as follows: cutting speed = 160 m/min, feed = 0.1 mm/rev, and depth of cut = 0.2 mm. Optimized outcomes suggest that this green machining strategy offers a viable alternative for sustainable manufacturing of difficult-to-machine alloys like Incoloy 800 HT. Full article

Tool wear modeling in CNC turning processes is critical for proactive maintenance and process optimization in intelligent manufacturing. However, traditional physics-based models lack adaptability, while machine learning approaches are often limited by poor interpretability. This study develops Kolmogorov–Arnold Networks (KANs) to address the trade-off between accuracy and interpretability in lathe tool wear modeling. Three KAN variants (KAN-A, KAN-B, and KAN-C) with varying complexities are proposed, using feed rate, depth of cut, and cutting speed as input variables to model flank wear. The proposed KAN-based framework generates interpretable mathematical expressions for tool wear, enabling transparent decision-making. To evaluate the performance of KANs, this research systematically compares prediction errors, topological evolutions, and mathematical interpretations of derived symbolic formulas. For benchmarking purposes, MLP-A, MLP-B, and MLP-C models are developed based on the architectures of their KAN counterparts. A comparative analysis between KAN and MLP frameworks is conducted to assess differences in modeling performance, with particular focus on the impact of network depth, width, and parameter configurations. Theoretical analyses, grounded in the Kolmogorov–Arnold representation theorem and Cybenko’s theorem, explain KANs’ ability to approximate complex functions with fewer nodes. The experimental results demonstrate that KANs exhibit two key advantages: (1) superior accuracy with fewer parameters compared to traditional MLPs, and (2) the ability to generate white-box mathematical expressions. Thus, this work bridges the gap between empirical models and black-box machine learning in manufacturing applications. KANs uniquely combine the adaptability of data-driven methods with the interpretability of physics-based models, offering actionable insights for researchers and practitioners. Full article

Micro-sandblasting pretreatment was applied to AlTiSiN-coated WC–Co tools to enhance cutting performance in 316 L stainless steel milling. An L₉(3³) Taguchi orthogonal array varied passivation pressure (0.1, 0.2, and 0.3 MPa), gun traverse speed (60, 80, and 100 m/min), and tool rotation speed (20, 30, and 40 r/min). Coating thickness varied only from 0.93 to 1.19 μm, and surface roughness remained within 0.044–0.077 μm, confirming negligible thickness and roughness effects. Under optimized conditions, coating adhesion strength and nano-hardness both exhibited significant improvements. A weighted-scoring method balancing these two responses identified the optimal pretreatment parameters as 0.1 MPa, 80 m/min, and 20 r/min. Milling tests at 85 m/min—using flank wear VBₘₐₓ = 0.1 mm as the failure criterion—demonstrated a cutting distance increase from 4.25 m (untreated) to 12.75 m (pretreated), a 200% improvement. Wear progressed through three stages: rapid initial wear, extended steady wear due to Al₂O₃ protective-film formation and Si-induced oxygen-diffusion suppression, and accelerated wear. Micro-sandblasting further prolonged the steady-wear phase by removing residual cobalt binder, exposing WC grains, and offsetting tensile residual stresses. These findings establish a practical, cost-effective micro-sandblasting pretreatment strategy that significantly enhances coating adhesion, hardness, and tool life, providing actionable guidance for improving the durability and machining performance of coated carbide tools in difficult-to-cut applications. Full article

The widespread use of mineral cutting fluids in metalworking poses challenges due to their poor wettability, toxicity, and non-biodegradability. This study explores cactus oil-based nanofluids as sustainable alternatives for metal cutting applications. Samples of cactus oil are prepared in plain form and with 0.025 wt.%, 0.05 wt.%, and 0.1 wt.% activated carbon nanoparticles (ACNPs) from recycled plastic waste. Plain cactus oil exhibited a 34% improvement in wettability over commercial soluble oil, further enhanced by 60% with 0.05 wt.% ACNPs. Cactus oil displayed consistent Newtonian behavior with a high viscosity index (283), outperforming mineral-based cutting fluid in thermal stability. The addition of ACNPs enhanced the dynamic viscosity by 108–130% across the temperature range of 40–100 °C. The presence of nano-additives reduced the friction coefficient in the boundary lubrication zone by a maximum reduction of 32% for CO2 compared to plain cactus oil. The physical and rheological results translated directly to the observed improvements in surface finish and tool wear during machining operations on H13 steel. Cactus oil with 0.05 wt.% ACNP outperformed conventional fluids, reducing surface roughness by 35% and flank wear by 57% compared to dry. This work establishes cactus oil-based nanofluids as a sustainable alternative, combining recycled waste-derived additives and non-edible feedstock for greener manufacturing. Full article

This study investigates the machining performance of AZ91D magnesium alloy under three different cooling environments: dry, minimum quantity lubrication (MQL), and nano-reinforced MQL (NanoMQL) with multi-walled carbon nanotubes. Turning experiments were conducted on a CNC lathe with systematically varied cutting parameters, including cutting speed (150–450 m/min), feed rate (0.05–0.2 mm/rev), and depth of cut (0.5–2 mm). The machining performance was evaluated through cutting force measurements, surface roughness analysis, and tool wear examination using SEM. The results demonstrate that the NanoMQL environment significantly outperforms both dry and conventional MQL conditions, providing a 42.2% improvement in surface quality compared to dry machining and a 33.6% improvement over conventional MQL. Cutting forces were predominantly influenced by the depth of cut and the feed rate, while cutting speed showed variable effects. SEM analysis revealed that the NanoMQL environment substantially reduced built-up edge formation and flank wear, particularly under aggressive cutting conditions. The superior performance of the NanoMQL environment is attributed to the enhanced thermal conductivity and lubrication properties of carbon nanotubes, which form a protective tribofilm at the tool–workpiece interface. This study provides valuable insights for optimizing the machining parameters of AZ91D magnesium alloy in industrial applications, particularly where high surface quality and tool longevity are required. Full article

The use of worn cutters not only reduces the machining accuracy but also increases the surface roughness. Therefore, it is important for enterprises to establish replacement rules for worn cutters. However, traditional wear regression studies require frequent shutdowns to measure tool wear as training samples. This undoubtedly increases the complexity of operations, making it difficult to apply in practical production. To address this issue, a novel method based on the wear transition percentage has been proposed to determine the optimal timing of replacing worn tools. This method does not require measuring tool wear and is suitable for different machining parameters. Firstly, the Vold–Kalman filter is employed to remove the rotation frequency and its harmonic components from the spindle current, resulting in spindle current clutter signals (SCCS) with low correlation with cutting parameters. Then, using convolutional neural networks (CNN) to learn the SCCS data features of severe wear and normal wear stages, a binary classification CNN model is obtained. Finally, the model is used to identify the full life SCCS data with different cutting parameters. The proportion of samples identified as normal wear to all samples during a certain period of time is used to calculate the wear transition percentage. The effectiveness of this method is verified by comparing it with the measured flank wear. Full article

This paper presents research findings on the turning of AZ31B magnesium alloy using ceramic-coated tungsten carbide tool inserts in a dry environment. Fifteen experiments were conducted according to the Box–Behnken design (BBD) for the straight turning of AZ31B magnesium alloy to investigate the variations in two important machinability indicators, i.e., material removal rate ‘MRR’ and mean roughness depth ‘R_Z’, with variations in cutting speed ‘C_S’, feed rate ‘f_r’, and depth of cut ‘DoC’. The cutting speed and feed rate had the maximum influence on the mean roughness depth and material removal rate, respectively. To address the challenge of optimizing conflicting machining responses, desirability function analysis (DFA) and grey relational analysis (GRA) were employed to identify the optimal turning parameters for conflicting machinability indicators or responses. These techniques enabled the simultaneous maximization of the material removal rate and the minimization of the mean roughness depth, ensuring an effective balance between productivity and surface quality. The optimal turning conditions—cutting speed of 90 m/min, feed rate of 0.2 mm/rev, and depth of cut of 1.0 mm—yielded the best multiperformance results with an MRR of 18,000 mm³/min and an R_Z of 2.21 µm. Scanning electron microscope (SEM) analysis of the chip and flank surface of the cutting tool insert used in the confirmation tests revealed the formation of band-saw-type continuous chips and tool wear caused by adhesion and abrasion. Full article

We investigated the orthogonal cutting process by using machine learning models to predict its performance. This study used the AZ91 magnesium alloy as the workpiece material, and machining was performed under the Minimum Quantity Lubrication (MQL) technique. The input parameters were the feed, cutting speed and MQL flow rate. Additionally, the outputs were flank tool wear, the chip contact length, peak distance, valley distance, pitch distance, chip segmentation ratio, compression ratio and shear angle. Studies on machine learning (ML) models being employed to evaluate the performance of the MQL-assisted orthogonal machining of AZ91 are very rarely found in the literature. This study explored machine learning (ML) as a data-driven alternative, evaluating decision tree regression, Bayesian Optimization, Random Forest Regression and XGBoost for predicting machinability. A comprehensive dataset of the cutting parameters and outcomes was utilized to train and validate these models, aiming to enhance the accuracy of the predictive analysis. The performance of each model was evaluated based on error metrics such as the mean squared error (MSE) and R-squared values. Among these models, XGBoost demonstrated a superior predictive accuracy, outperforming the other methods in terms of its precision and generalizability. These findings suggest that XGBoost provides a more robust solution for modeling the complexities of the orthogonal cutting process, offering valuable insights into process optimization. The analysis supports that the XGBoost model is the most accurate, with a 34.1% reduction in the mean squared error and a 17.1% reduction in the mean absolute error over these values for the Decision Tree. It also outperforms the Random Forest Regression model, achieving a 19.8% decrease in the mean squared error and a 7.1% decrease in the mean absolute error. Full article

This study aims to determine the extent to which coating composition and workpiece properties impact machinability and tool selection when turning Compacted Graphite Iron (CGI) under extreme roughing conditions. Two CGI workpieces, differing in pearlite content and graphite nodularity, were machined at a cutting speed of 180 m/min, feed rate of 0.18 mm/rev, and depth of cut of 3 mm. To assess the impact of tool properties across a wide range of commercially available tools, four diverse multilayered cemented carbide tools were evaluated: Tool A and Tool B with a thin AlTiSiN PVD coating, Tool C with a thick Al₂O₃-TiCN CVD coating, and Tool D with a thin Al₂O₃-TiC PVD coating. The machinability of CGI and wear mechanisms were analyzed using pre-cutting characterization, in-process optical microscopy, and post-test SEM analysis. The results revealed that CGI microstructural variations only affected tool life for Tool A, with a 110% increase in tool life between machining CGI Grade B and Grade A, but that the effects were negligible for all other tools. Tool C had a 250% and 70% longer tool life compared to the next best performance (Tool A) for CGI Grade A and CGI Grade B, respectively. With its thick CVD-coating, Tool C consistently outperformed the others due to its superior protection of the flank face and cutting edge under high-stress conditions. The cutting-induced stresses played a more significant role in the tool wear process than minor differences in workpiece microstructure or tool properties, and a thick CVD coating was most effective in addressing the tool wear effects for the extreme roughing conditions. However, differences in tool life for Tool A showed that tool behavior cannot be predicted based on a single system parameter, even for extreme conditions. Instead, tool properties, workpiece properties, cutting conditions, and their interactions should be considered collectively to evaluate the extent that an individual parameter impacts machinability. This research demonstrates that a comprehensive approach such as this can allow for more effective tool selection and thus lead to significant cost savings and more efficient manufacturing operations. Full article

Milling machines remain relevant in modern manufacturing, with tool optimization being crucial for cost reduction. Inserts for compound cutting tools can reduce the cost of operations by optimizing their lifespan. This study analyzes the flank wear of cutting tools in milling machines, with an emphasis on evaluating different approaches to predict their lifespan. It compares three distinct modeling approaches for predicting tool lifespan using algorithms: traditional ensemble methods (Random Forest, Gradient Boosting) and a deep learning-based LSTM network. Each model is evaluated independently, and this comparative analysis aims to determine which modeling strategy best captures the intricate interactions between various process variables affecting tool wear. This method ensures greater efficiency and accuracy than conventional techniques, providing a scalable, resource-efficient solution for reliable and insightful tool wear predictions. The results obtained from the dataset of an insert tool can be extrapolated to other milling inserts and demonstrate the progression of tool wear over time under varying cutting parameters, providing critical insights for optimizing milling operations. The integration of uncertainty awareness in the predictive outputs is a unique feature of this research and enhances decision-making for smarter manufacturing. This proactive approach enhances operational efficiency and reduces overall production costs. Furthermore, the data-driven, AI-centric methodology developed in this study offers a transferable approach that can be adapted to other machining processes, advancing state-of-the-art tool wear prediction. Full article

Tool selection is relevant because a wide variety of materials exhibit different machinability behaviors. Tool life during manufacturing is commonly associated with productivity. Insert developers have been using coatings on cutting tools to enhance their performance, with chemical vapor deposition (CVD) and physical vapor deposition (PVD) being the two most used techniques. This study analyzed the cutting tool wear mechanism by machining AISI D2 steel using two different inserts of TiAlN/TiN PVD and TiCN/Al₂O₃ CVD as layers deposited on a carbide substrate. The two inserts were tested at three different cutting speeds, namely, low, medium, and high; these values were below the data suggested by the supplier catalog. The flank wear and rake face were analyzed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectrometry (EDX). The adhesion material, edge deformation, and abrasion were the main wear mechanisms before catastrophic damage occurred at the three different cutting speeds in the PVD cutting tool. Nevertheless, increasing the cutting speed reduced the tool life from 84% to 61% at high values compared to the medium values of PVD and CVD, respectively, where the medium value resulted in a balance between the material removal rate and tool life. The wear mechanism of the CVD tool was BUE and chipping; nevertheless, its craters were larger than those of the PVD. Compared to those configured for PVD, the CVD insert demonstrated the ability to machine D2 steel at twice the cutting speed with a workpiece surface roughness of 0.3 µm, in contrast to a variation of 0.6 to 0.15 µm with the PVD tool. Full article

(AlCrNbSiTiMo)N high-entropy alloy films with different nitrogen contents were deposited on tungsten carbide substrates using a radio-frequency magnetron sputtering system. Two different types of targets were used in the sputtering process: a hot-pressing sintered AlCrNbSiTi target fabricated using a single powder containing multiple elements and a vacuum arc melting Mo target. The deposited films were denoted as R_N0, R_N33, R_N43, R_N50, and R_N56, where R_N indicates the nitrogen flow ratio relative to the total nitrogen and argon flow rate (R_N = (N₂/(N₂ + Ar)) × 100%). The as-sputtered films were vacuum annealed, with the resulting films denoted as HR_N0, HR_N33, HR_N43, HR_N50, and HR_N56, respectively. The effects of the nitrogen content on the composition, microstructure, mechanical properties, and tribological properties of the films, in both as-sputtered and annealed states, underwent thorough analysis. The R_N0 and R_N33 films displayed non-crystalline structures. However, with an increase in nitrogen content, the R_N43, R_N50, and R_N56 films transitioned to FCC structures. Among the as-deposited films, the R_N43 film exhibited the best mechanical and tribological properties. All of the annealed films, except for the HR_N0 film, displayed an FCC structure. In addition, they all formed an MoO₃ solid lubricating phase, which reduced the coefficient of friction and improved the anti-wear performance. The heat treatment HR_N43 film displayed the supreme hardness, H/E ratio, and adhesion strength. It also demonstrated excellent thermal stability and the best wear resistance. As a result, in milling tests on Inconel 718, the R_N43-coated tool demonstrated a significantly lower flank wear and notch wear, indicating an improved machining performance and extended tool life. Thus, the application of the R_N43 film in aerospace manufacturing can effectively reduce the tool replacement cost. Full article

The aim of this study was to develop a concept for an angular positioning method for a round cutting insert in a torus cutter body dedicated to the multi-axis milling process under high-speed machining cutting conditions. The method concept is based on a developed wear model using a non-linear estimation method adopting a quasi-linear function. In addition, a tool life model was developed, taking into account the cutting blade work angle parameter, the laser marking method for the round cutting insert, and a wear measurement methodology. The developed tool wear model provides an accuracy of 90% in predicting the flank wear of the cutting blade. The developed procedure for angular positioning of the round cutting insert enables the entire cutting edge to be fully utilized, extending the total tool life. In addition, the measured largest defect values between the worn cutting edge and the nominal outline of the round cutting insert indicate the location of notching-type wear. Full article