Tool Wear Research Articles

Laser applications in arts and culture: An example on the construction of tiered umbrellas

Tiered umbrella or in Thai called Chat means a high decoration or decoration of honor, a type of umbrella that is stacked up in layers along a single axis. At present, in Thailand, the tiered umbrellas are seen in three important places including (1) on the top of the pagoda of various temples, (2) inside the ordination hall of temples, and (3) inside the Grand Palace for the king and the royal family. In the past, a tiered umbrella was made from brass because its color was similar to gold. Constructing each set of tiered umbrellas requires a skilled technician and takes a lot of time. At present, laser cutting of metals has been widely used in the production section of the industry. This is due to the advantages of laser cutting that include narrow kerf width, smooth cutting edges, high cutting speed, reduced material waste, no tool wear, and easy automation and manipulation even for complex shapes. In this work, an example for the construction of seven-tiered umbrellas from stainless steel sheets using a high-power CO2 laser was reported. The results show that the total working time for the construction of a seven-tiered umbrella is about 113 h, which is much less time compared to 3–6 months taken by craftsmen. Therefore, this method can preserve the construction of tiered umbrellas in Thailand even though craftsmen are scarce at present.

Effect of drill gash rake angle on drillability in plain carbon and low alloy steels

In this study, the effects of the gash rake angle (γa), which is an important component of the drill geometry, on the thrust force and drilling torque occurring in the drilling of ordinary carbon steels (AISI 4140 and Ck 45) were examined through experimental and FEM-supported numerical simulation studies on surface roughness, tool surface roughness, wear and diameter deviation values were investigated experimentally. In the experiments, 6.8 mm diameter solid cementite carbide, coated and internal cooling channel drills with +10°, 0°, −10° gash rake angle were used. Three different feed rates and cutting speed levels were selected as cutting parameters. Determination of material-specific gash rake angle and cutting parameters for different materials was carried out by Taguchi-assisted variance analysis of experimental data. In experimental studies, the gash rake angle was 65.9% effective on the thrust force for AISI 4140 tempered steel, while it was the second effective parameter with 29.22% for Ck 45 manufacturing steel. While the feed rate is the more effective parameter in the drilling moments that occur when drilling different materials, it has been observed that γa also has a significant effect. It has been determined that chisel edge wear, main cutting edge wear and crater wear occur in the drill when drilling AISI 4140 tempered steel and Ck 45 manufacturing steel. The data obtained from the finite element-assisted chip removal analyses support the experimental results and provide guiding results in the selection of material-specific gash rake angle.

Evaluation of entropy-coupled multi-criteria decision-making methods for enhancing machinability

Optimizing machining parameters is crucial, enhancing machinability while maintaining high product quality standards. This study bridges a critical research gap by evaluating and comparing five Taguchi-based Multi-Criteria Decision Making (MCDM) techniques—Combined Compromised Solution (CoCoSo), Grey Relational Analysis (GRA), Multi-Objective Optimization Ratio Analysis (MOORA), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Complex Proportional Assessment (COPRAS)—coupled with the Entropy method to optimize machining parameters for enhancing machinability in turning medium carbon steel. The focus is on feed rate and cutting speed under dry and Minimum Quantity Lubrication (MQL) environments considering six critical machining responses: material removal rate, surface roughness, main cutting force, cutting temperature, cutting ratio, and tool life.The results reveal that MQL consistently improves machining performance, with COPRAS, TOPSIS, MOORA, and GRA converging on an optimal setting favoring an MQL environment, a 0.14 mm/rev feed rate, and a cutting speed of 137 m/min, whereas CoCoSo suggests a different optimal parameter setting. CoCoSo and GRA demonstrate the highest reliability, evidenced by minimal discrepancies between predicted and experimental results, with absolute percentage errors of 0.647 % and 0.659 %, respectively. The COPRAS method also shows strong predictive accuracy with a 5.573 % error, outperforming MOORA and TOPSIS. Spearman's rank correlation analysis reveals a high agreement between COPRAS, MOORA, and TOPSIS, with COPRAS emerging as a potential replacement for the latter in similar decision-making scenarios. SEM and EDX analyses confirm that MQL conditions reduce tool wear, enhance surface quality, and extend tool life compared to dry machining. This research provides insights into effective parameter optimization strategies for improving machinability and underscores the benefits of adopting MQL for sustainable manufacturing processes.

DeepTool: A deep learning framework for tool wear onset detection and remaining useful life prediction

Milling tool availability and its useful life estimation is essential for optimisation, reliability and cost reduction in milling operations. This work presents DeepTool, a deep learning-based system that predicts the service life of the tool and detects the onset of its wear. DeepTool showcases a comprehensive feature extraction process, and a self-collected dataset of sensor data from milling tests carried out under different cutting settings to extract relevant information from the sensor signals. The main contributions of this study are:•Self-Collected Dataset: Makes use of an extensive, self-collected dataset to record precise sensor signals during milling.•Advanced Predictive Modeling: Employs hybrid autoencoder-LSTM and encoder-decoder LSTM models to estimate tool wear onset and predict its remaining useful life with over 95 % R2 accuracy score.•Comprehensive Feature Extraction: Employs an efficient feature extraction technique from the gathered sensor data, emphasising both time-domain and frequency-domain aspects associated with tool wear.

Tool wear feature extraction in BTA deep hole drilling process based on maximum probability multi-synchrosqueezing transform of spindle current signal

Multi-synchrosqueezing transform (MSST) enhances the concentration of the time–frequency representation (TFR) of time-varying signals by introducing multi-instantaneous frequency (IF) estimation operator. However, the existence of estimation bias may lead to uncertainty in the discrete values of IF during computer computations, causing issues with non-reassigned points. In this paper, a maximum probability multi-synchrosqueezing transform (MPMSST) is proposed, which incorporates distribution probability during the discretization process of the IF, taking the frequency corresponding to the maximum distribution probability as the determined value of IF. Through verification with multi-component time-varying simulation signals, MPMSST not only effectively eliminates non-reassigned points but also accurately estimates the theoretical time–frequency curve. By applying MPMSST to analyze spindle motor current signals during deep hole machining, TFRs under different tool wear states are obtained. The singular value decomposition (SVD) method is utilized to extract the principal component of the TFR, and a tool wear state feature index is constructed based on the sum of squares of the first 10 singular values. The correlation coefficient and covariance between the proposed feature index and the tool wear curve are 0.835 and 5.704 respectively, indicating a good consistency of the index with tool wear changes, and a high sensitivity to variations in tool wear state.

Multi response optimization of process parameters in titanium alloy minimum quantity lubrication drilling operation

Abstract This work investigates the application of minimal quantity lubrication (MQL) in the drilling process, where a combination of liquid and air creates an aerosol in the form of a fine mist. This protective layer decreases friction and tool wear, hence improving overall efficiency. The study examines the conventional Gulfcut oil blend drilling technique to enhance drilling performance on the titanium alloy Ti-6Al-4V under MQL circumstances. The key drilling parameters, namely spindle speed (N), feed rate (F), and point angle (D), were analyzed using a Taguchi L16 orthogonal array. The experimental inquiry aimed to identify the most favorable conditions that lead to the least amount of torque (TQ), thrust force (TF), and surface roughness (SR). An Analysis of Variance (ANOVA) was used to examine the impact of control factors on drilling performance. The optimized drilling parameters led to a reduction in torque, thrust force, and surface roughness by 12.88%, 21.29%, and 25.76%, respectively. Furthermore, microstructure analysis demonstrated that the Gulfcut lubricant plays a pivotal role in reducing grain size during drilling. This research provides valuable insights into optimizing drilling conditions for titanium alloy using a conventional Gulfcut oil blend with MQL, aiming to reduce torque, thrust force, and surface roughness.

Fabrication of through alumina vias (TAV) by ultrasonic micromachining for advanced packaging applications

This article presents the fabrication of a high aspect ratio through alumina-vias (TAV) by combining ultrasonic micromachining (USM), electroless plating, and copper electrodeposition. Various horn designs, i.e., tapered, stepped, exponential, and hybrid horns, were analyzed to achieve a uniform longitudinal vibration in a 6 × 6 multi-tip tool assembly. The modal analysis determined the horn length, and harmonic analysis provided the stress distribution and vibrational amplitude. The tapered horn design was selected for its superior vibration amplification and reduced stresses. Finite element modeling predicted the geometric profiles of the microvias at various USM parameters. Experimental validation showed less than 5 % error in the depths of the microvias. Using the optimized USM parameters, a 6 × 6 array of blind and through-holes was created in a 3 mm thick alumina substrate with an average feed rate of 180 μm/min. The average tool wear was observed to be 0.5 mm when drilling through 3 mm thick alumina. Electroless deposition created a ∼100 nm seed layer on the alumina substrate and achieved good adhesion with via sidewalls. The through-holes were partially filled with a 60 μm thick copper layer by electrodeposition to create through-alumina vias (TAV) that can be used as 3D interconnects in the packaging of high-power electronics.

Study on tool wears in turning Al2219, unhybrid and hybrid metal matrix nano composites by CCD design of experiment

In this article, Aluminum (Al2219) as a matrix, and particles of n-B4C and MoS2 were chosen as reinforcements. By means of the stir casting technique, the unhybrid and hybrid nano metal matrix composites were prepared. The turning experiment was done through CCD design of experiment with TiN coated carbide insert using a Computer Numerically Controlled Lathe. Also, for turned inserts tool wear measurement was done using a Mitutoyo profile projector with digital readout. By ANOVA the significant contribution for tool wear of each parameter can be identified and the confirmation test is performed in sort to validate the tool wear results. Furthermore, the formation of chips during turning nano MMCs (unhybrid and hybrid) are studied for different feed rates ( f) and cutting speeds ( v) at 0.5 mm constant depth of cut. The outcome showed that both increases in cutting speed and feed rate increase the tool wear. For Al 2219, unhybrid and hybrid nano metal matrix composites feed rate is the important factor. The value of tool wear of the Al2219 matrix is minimal and for unhybrid nano composite is maximum. The leading wear mechanism in unhybrid nano composite is abrasion. Moreover, with the addition of 2%MoS2 in the hybrid nanocomposite the tool wears is decreased. The development of Build Up Edge (BUE) was seen on the flank face of the cutting tool for hybrid nanocomposite at 0.5 mm depth of cut, (v = 114.64 m/min) and (f = 0.441 mm/rev). The obtained % error among the modeled and experimental values is <5% and it is well within the limit. The analysis of chip formation was studied for nanocomposites at lower as well as higher feed rates, cutting speeds, and constant depth of cut during turning.

Achieving superior mechanical and corrosion properties in medium-thickness Ti-6Al-4 V alloy joints by back heating assisted friction stir welding below β-phase transformation temperature

The equiaxed microstructure formed below the β-phase transition point of Ti alloys generally exhibits an excellent balance of mechanical properties and corrosion resistance. However, this desirable microstructure is impossible to be obtained in fusion welded joints but is prospective to be achieved by the solid-state friction stir welding (FSW) technique. Unfortunately, it generally generates bottom defects and severe tool wear in medium-thickness Ti alloy joints when conventional FSW was conducted below the β-phase transition point. In this study, 6 mm thick Ti-6Al-4 V plates were joined by both the conventional FSW and back heating assisted FSW (BHAFSW). Defects caused by significant tool wear and bottom phase transition differences occurred in the conventional FSW. It was found that the hardness difference between the base material (BM) and the tool increased to 35.6 % from 500 °C to 900 °C. The back heating (150 °C) was used to control welding temperatures remaining the 900 °C, thus largely reducing the tool wear by increasing the hardness difference. In addition, the back temperature compensation increased the bottom temperature and controlled the phase transition position from the bottom to the middle of the joint. The shoulder pressure contributed to the compression of the defects, and the defects were eliminated by increasing the pressure at the phase transition position. A significantly refined equiaxed microstructure with an average grain size of ∼0.9 μm was achieved below the β-phase transition temperature in the stir zone via back heating assisted FSW, while a bimodal structure with an average grain size of 3.5 μm was formed near the β-phase transition temperature. Inconspicuous reduction of the strength was detected for the joints (98 % of the BM) which possess equiaxed microstructures, and the corrosion resistance of the joints was enhanced compared to the BM. This superior synergy of mechanical and corrosion properties exceeded the majority of Ti alloy joints previously reported. This study provided an effective method for obtaining medium-thickness Ti alloy joints with ultrafine equiaxial structures with superior mechanical properties and corrosion resistance.

Effect of hBN/MoS2 hybrid nanofluid minimum quantity lubrication on the cutting performance of self-lubricating ceramic tools when machining AISI 4340

Difficult-to-machine materials have consistently limited the improvement of machining efficiency. To address this challenge, this study proposes a strategy of combining Al2O3/SiC/G self-lubricating ceramic tools with hBN/MoS2 hybrid nanofluids. The experiment evaluates the ratio and concentration of the hBN/MoS2 hybrid nanofluid based on cutting forces, friction coefficients, cutting temperatures, surface roughness, and tool wear. Results show that better lubrication is achieved by hBN/MoS2 hybrid nanofluids compared to single nanofluids. The optimal hBN/MoS2 mixing ratio and nanofluid concentration are 2:1 and 1.5 wt%, respectively. Finally, the cutting lubrication mechanism and the synergistic mechanism of self-lubricating ceramic tools with hybrid nanofluids were proposed.

Preparation of fine-grained/ultrafine-grained Nb521 alloy with superior mechanical property by friction stir processing

High strength-ductility synergy is difficult to achieve in Nb alloys. Although high strength has been achieved through severe plastic deformation (SPD) technology, led to low ductility in alloys. In this work, FSP technology was applied to treat Nb–5W–2Mo–1Zr-0.1C (Nb521) alloys in preparation of fine-grained (FG)/ultrafine-grained (UFG) Nb521 with excellent strength and ductility. The microstructure evolution and mechanical property improvement mechanism were systematically studied for Nb521 alloy through various characterization pathways. The research results indicated that UFG Nb521 alloy with a grain size of 0.63 ± 0.41 μm can be prepared using low shoulder plunge depth FSP (LPD-FSP), which is the first report of UFG Nb521 alloy. The main reason for the formation of onion rings structure in SZ is the periodic wear of the stirring tool, and the onion rings structure does not cause mechanical damage. The texture formed by Nb521 alloy under different processing parameters is off-axis shear texture, which matches the ideal shear texture of D2 (112‾)[111] after rotation. In addition, this study also elaborated on the refinement mechanism of the second phase particles (Nb, Zr) C in Nb521 alloy during FSP. This study also indicated that the increase in yield strength of FSP samples at room temperature is mainly determined by grain refinement. These findings provided new ideas for the development of high-performance niobium alloys.

Experimental investigation on performance of bubble-bursting atomization methods for minimum quantity lubrication in face milling tool steel

The metal cutting industry faces challenges in machining hard materials due to high forces and temperatures. This paper introduces bubble-bursting atomization minimum quantity lubrication (BBA-MQL), a novel MQL technique that generates fine biodegradable oil mists for cooling and lubrication. Although the initial results of BBA-MQL are promising, there has not been a comprehensive investigation into its use in machining hard materials, specifically tool steels. Therefore, this study focused on the applicability of BBA-MQL in face milling of AISI P20 + Ni tool steel in comparison with dry cutting and conventional MQL using commercial and vegetable oil. The machining tests were performed at three cutting speeds (50, 80, and 110 m/min), fixed cutting depth (0.2 mm), and feed rate (0.15 mm/tooth). The performance is evaluated by measuring the cutting force, surface quality, cutting temperature, and tool wear. It was found that BBA-MQL decreased cutting force and surface roughness considerably, with the average reductions being 23.2 % and 49.8 % compared with the conventional minimum quantity lubrication. The highest cutting speed of 110 m/min was preferred for achieving the lowest roughness value and cutting force when milling tool steel P20 + Ni. Furthermore, BBA-MQL with castor oil proved more effective compared to conventional MQL in reducing cutting force, showing improved surface finish, reduced cutting temperature, and delayed tool wear.

Optimization of Tool Wear and Cutting Parameters in SCCO2-MQL Ultrasonic Vibration Milling of SiCp/Al Composites

Silicon carbide particle-reinforced aluminum matrix (SiCp/Al) composites are significant lightweight metal matrix composites extensively utilized in precision instruments and aerospace sectors. Nevertheless, the inclusion of rigid SiC particles exacerbates tool wear in mechanical machining, resulting in a decline in the quality of surface finishes. This work undertakes a comprehensive investigation into the problem of tool wear in SiCp/Al composite materials throughout the machining process. Initially, a comprehensive investigation was conducted to analyze the effects of cutting velocity vc, feed per tooth fz, milling depth ap, and milling width ae on tool wear during high-speed milling under SCCO2-MQL (Supercritical Carbon Dioxide Minimum Quantity Lubrication) ultrasonic vibration conditions. The results show that under the condition of SCCO2-MQL ultrasonic vibration, proper control of milling parameters can significantly reduce tool wear, extend tool service life, improve machining quality, and effectively reduce blade breakage and spalling damage to the tool, reduce abrasive wear and adhesive wear, and thus significantly improve the durability of the tool. Furthermore, a prediction model for tool wear was developed by employing the orthogonal test method and multiple linear regression. The model’s relevance and accuracy were confirmed using F-tests and t-tests. The results show that the model can effectively predict tool wear, among which cutting velocity vc and feed rate fz are the key parameters affecting the prediction accuracy. Finally, a genetic algorithm was used to optimize the milling parameters, and the optimal parameter combination (vc = 60.00 m/min, fz = 0.08 mm/z, ap = 0.20 mm) was determined, and the optimized milling parameters were tested. Empirical findings suggest that the careful selection of milling parameters can significantly mitigate tool wear, extend the lifespan of the tool, and enhance the quality of the surface. This work serves as a significant point of reference for the processing of SiCp/Al composite materials.

Influence of the thickness of TiAlSiN on the thermal properties as input parameter for FEM-simulation

Hard coatings deposited by physical vapor deposition are state of the art for wear and oxidation protection of cutting tools. The cutting performance depends on coating material and process as well as cutting edge microgeometries. Both have an influence on the thermomechanical tool loads resulting in tool wear. Therefore, a process adapted design for the consideration of the entire system is necessary. One approach to substitute costly machining investigations and save material resources is the use of finite element (FE)-based chip formation simulations. However, in order to perform these simulations, information about chemical, thermal and physical coating behavior in the temperature range relevant for machining is necessary. In the present study, nanocomposite TiAlSiN coatings with varying coating thicknesses were deposited on cemented carbide tools. The effect of coating thickness on coating morphology, chemical composition, thermal conductivity as well as indentation hardness at ϑ = 20 °C, ϑ = 200 °C, ϑ = 400 °C and ϑ = 600 °C was analyzed. Therefore, three coating variants with a coating thickness of ds = 2 μm, ds = 4 μm and ds = 6 μm were deposited. Additionally, the distribution of the heat, generated during turning 42CrMo4 + A, was simulated for the coated cutting tool. A columnar morphology with constant chemical composition was determined for the variants. While the arithmetic mean value of the coating roughness increased with increasing coating thickness, there was no influence of coating thickness on thermal diffusivity and high temperature coating hardness measurable. Nevertheless, an influence of the tool temperature can be observed in the application behavior in turning tests as well as in the simulation, possibly caused by a change in the contact conditions due to increasing cutting edge microgeometry by increasing coating thickness. Regarding the tested TiAlSiN hard coatings no significant influence of the coating thickness on properties such as thermal diffusivity and indentation hardness were observed. This leads to the assumption, that even when the coating thickness is changed no significant changes in the FEM simulation are needed, which makes modelling easier.

Vibration Friction Investigation on the NCS of Joints of the CNC Machine Tools Considering Friction Factor

Machine tool vibrations play a significant role in hindering productivity during machining. The growing vibrations accelerate tool wear and chipping, cause a poor wave surface finish, and may damage the spindle bearing. Some research showed that tribological properties such as friction factors can have obvious influences on the topography of rough surfaces and the nonlinear dynamic characteristics of machine tool systems. Therefore, studying the vibration friction dynamic characteristics on the normal contact stiffness (NCS) of joints of CNC machine tools is absolutely necessary for improving the machining accuracy and precision of the whole system. The study results of NCS of joints of the CNC and the friction coefficient are discussed in this paper. The model of NCS based on fractal parameters was obtained. The models of deformations of the rough surfaces and contact surfaces were deduced. The results showed that the NCS based on the calculation method considering the elastic–plastic deformation of the asperity is much higher in precision than the methods considering only elastic or plastic deformation separately. The observations this paper described suggest that in the CNC machine tools system, higher D and G and higher friction coefficients lead to higher normal contact stresses (NCSs).

Magnetic field assisted blasting erosion arc machining (M-BEAM): A novel efficient and quality improved machining method for Inconel 718

Electrical Arc Machining (EAM) presents an efficient technique for processing difficult-to-cut materials such as Inconel 718. However, despite its extremely high machining efficiency, the high energy density in machining makes it difficult to achieve a desirable surface quality that can be compatible with the subsequent cutting process. To solve this issue, this study explores the application of a magnetic field to enhance the performance of Blasting Erosion Arc Machining (BEAM), namely Magnetic Field assisted BEAM (M-BEAM). It leads to an increased Material Removal Rate (MRR) and reductions in both the Tool Wear Ratio (TWR) and the Surface Roughness (represented by Sa) with the increasing magnetic field strength, attributed to the improvements of the arc deflection and molten metal expelling. Moreover, the electromagnetic force is positively correlated with the discharge energy, which can further improve the machining performance of BEAM, especially when the hydrodynamic force is hard to act at large discharge energy. The MRR and Sa are enhanced and reduced by 14.52 % and 41.86 % respectively due to the stronger control effect at large energy. After multi-objective optimization, the optimized MRR in M-BEAM achieves a 9.22 % enhancement and Sa also decreases by 25.96 % and 60.61 % in the roughing and finishing process. This advancement also elevates machining precision as well as mitigates defects such as debris adhesion, overburning, and cracks. Moreover, the recast layer is significantly reduced by 88.59 % and 89.96 %. Consequently, M-BEAM promises extensive applications in aerospace industries for machining Inconel 718 and even for other difficult-to-cut materials with high efficiency and quality.

Spot-checking machine learning algorithms for tool wear monitoring in automatic drilling operations in CFRP/Ti6Al4V/Al stacks in the aircraft industry

In aircraft manufacturing, where diverse materials, including Carbon Fiber-Reinforced Plastics (CFRP), aluminum, and titanium alloys, are employed, the assembly process heavily relies on creating thousands of holes. These holes accommodate bolts and rivets, facilitating the secure interlocking of structural components within the aircraft fuselage. The proliferation of sensor systems in this domain has led to a substantial increase in data generation during the hole-making process, offering a compelling opportunity to optimize the production system. In this context, this article is dedicated to harnessing the data collected from the production system of a commercial aircraft to refine the assembly process, with a specific focus on reducing consumable costs. The primary approach involves developing a real-time Tool Wear Monitoring System by comparing the performance of Linear Regression, Lasso Regression, Ridge Regression, k-Nearest Neighbors, Support Vector Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting Machine Learning models. Using a scale of the general drill condition as an outcome, the Gradient Boosting Regressor has shown outstanding results. Notably, the residuals consistently exhibited zero-centered errors in training and test sets. However, it suggests that further enhancements are needed to surpass human-level performance in predicting tool conditions because of the quality and quantity of available data.

Информационные свойства частотных характеристик динамической системы резания при диагностике износа инструментов

Introduction. One of the directions for increasing the efficiency of cutting is related to the creation of tool wear diagnostic systems. Algorithms and devices have been developed that evaluate wear based on analysis of the vibroacoustic emission signal. These algorithms, as a rule, do not reveal the nature of its formation and the reasons for the change as wear develops. Subject. The paper is devoted to the analysis of the reasons for changes in vibration properties with the development of tool wear. The aim of the work is to study the changes in the frequency characteristics of a dynamic cutting system caused by the development of wear, and to build diagnostic information models on this basis, as well as its use in industry. Method and methodology. The results of mathematical simulation of a perturbed dynamic cutting system are presented, in which the observed vibration sequences are a consequence of disturbances transformed by a dynamic system, the parameters of which depend on wear. Two frequency ranges are considered. Results and discussions. The first range includes frequencies that lie within the bandwidth of the instrument subsystem. The second is outside of it. In the first frequency range, it has been analytically and experimentally proven that the development of wear leads to fundamental changes in the frequency properties of the cutting system as a converter of disturbances into tool vibrations. There is a shift in the natural frequencies of the oscillatory circuits formed by the cutting system, a decrease in its quality factor, and as wear develops, some identified features of the vibration spectra appear, including the ratio of the low-frequency and high-frequency parts of the spectrum, etc. In the second frequency range, a model of force emission in the form of a random pulse sequence is considered and wear is displayed in it. The results of studying the coherence function between the forces acting on the tool and vibrational displacement are presented. Information models of wear are proposed, an example of an information model of wear and the results of its use in industry are given.

Investigation of coated and uncoated carbide cutting tool wear in dry turning of en aw 2007 aluminum alloy

Investigation of coated and uncoated carbide cutting tool wear in dry turning of en aw 2007 aluminum alloy

Influence of the process parameters on the microstructure and the machinability of additively manufactured Ti-5553 titanium alloy

Additive Manufacturing (AM) technologies, particularly laser powder bed fusion (LPBF), are revolutionising the production of complex geometries and lightweight structures. Furthermore, LPBF allows to tailor the microstructure and resulting properties of metallic materials. This study focuses on titanium alloys, crucial for high-performance applications like aircraft components and medical implants. Although AM enables near-net-shape fabrication, many titanium parts still require machining to meet surface and dimensional standards. Titanium’s challenging machinability is well-documented for cast and wrought alloys, but only less is known about additively manufactured variants. In this work, the machinability of an additively manufactured Ti-5Al-5V-5Mo-3Cr alloy (Ti-5553) is investigated, focusing on chip formation, cutting forces, and tool wear across different LPBF process parameters. Four LPBF parameter sets were validated, and results were compared to conventional wrought sample. The findings reveal significant variations in machinability linked to LPBF parameters. Specifically, the highest tool loads and wear were observed for samples produced with the highest energy density of EV = 37.0 J/mm3, likely due to α-phase precipitation. In contrast, samples with lower energy densities (<29.1 J/mm3) exhibited up to 100% longer tool life. Concluding, this study highlights how the machinability of Ti-based components can be significantly influenced by the LPBF processing parameters.