Tool Wear Research Articles

Investigation on the material removal mechanism in ion implantation-assisted elliptical vibration cutting of hard and brittle material

Ductile-regime machining has been used to generate damage-free surface of hard and brittle materials by setting the cutting depth to be smaller than the ductile-brittle transition depth (DBTD). However, the ductile-regime cutting of sapphire remains challenging owing to its extreme hardness, small DBTD, serious surface fractures, and severe tool wear. To solve this problem, ion implantation-assisted elliptical vibration cutting (Ii-EVC) has been proposed in this study to enhance the machinability of hard and brittle materials. Taking sapphire as an example, high-energy phosphorus ions were implanted into the workpiece to modify its surface. Nanoindentation tests revealed that the modified materials undergo plastic and elastic deformation more easily due to the decrease in hardness and modulus. Compared with nanocutting without implantation assistance, the DBTD of implanted sapphire has been increased by more than five times. The advantageous effects of Ii-EVC achieve great enhancement in machinability, including surface fractures suppression, tool-wear reduction, chips morphology transformation from discontinuous to continuous, and cutting force decrease. Furthermore, even near the cracks in the brittle region after Ii-EVC, the subsurface microstructure showed a more complete lattice arrangement and a strain distribution close to zero, indicating that crack propagation was effectively suppressed. Due to the promoted localized plastic deformation, the stress distribution in the implanted material is much smaller than that in pristine workpiece. Implantation-induced defects not only serve as a core for absorbing external energy from the high-frequency vibration and improving the in-grain deformation but also facilitate the formation of shear bands. The interface with high distortion between the modified layer and substrate can effectively dissipate strain energy and hinder crack propagation to the free surface. The turning experiments verified that Ii-EVC can achieve better surface quality, less tool wear and higher optical transmittance. Overall, Ii-EVC addresses the challenges of tool breakage and surface fracture caused by high-frequency collision between tool and workpiece in traditional EVC, overcomes the problem of limited modification depth in ion implantation, and increases the ductile-regime removal depth of extremely hard and brittle materials to several microns. Such findings demonstrate that Ii-EVC is a promising method for the ultra-precision manufacturing of advanced materials.

Wear behavior of all-ceramic micro end mills in micro milling of PMMA

Micro milling is a state-of-the-art micro manufacturing method employed, for instance, in the context of prototyping applications. Due to the high specific cutting forces and small tool diameters inherent to micro milling, tool wear represents a significant challenge for the fabrication of large, high-quality structures. In conventional cutting applications, ceramic tool substrates have been demonstrated to exhibit greater wear resistance than cemented carbides. In the case of micro milling of polymethylmethacrylate (PMMA), all-ceramic micro end mills made from zirconia (Y-TZP) have been observed to exhibit less tool wear than common cemented carbide micro end mills. In previous experiments, these tools exhibited a feed per tooth-dependent wear behavior, regardless of the spindle speed employed. The objective of this study was to investigate the wear behavior of the all-ceramic micro end mills at higher feeds per tooth (up to 7.5 µm) and spindle speeds (up to 110,000 rpm) in PMMA. The objective was to fully characterize the interactions between the all-ceramic micro end mills and the milling parameters for this thermoplastic workpiece material. The wear at the cutting edge was quantified by analyzing the topography with an atomic force microscope, and the results were correlated with the cutting forces and process outcomes. The results were found to be highly sensitive to the spindle speed used, with higher speeds leading to higher temperatures in the cutting zone and thus softening of the PMMA material. In general, micro milling parameters of 110,000 rpm and a feed per tooth of 5 µm resulted in significantly reduced tool wear with no adverse effect on surface quality when micro milling PMMA with all-ceramic micro end mills.

Impact of surface micro-dimples on machinability of Inconel 718 alloy

Used extensively in the aerospace industry, the superalloy Inconel 718 is deemed hard to cut because of its rapid work hardening, low thermal conductivity, and excessive shear strength. Proposed here is patterning Inconel 718 with micro-dimples using electrochemical machining and electrical discharge machining prior to conventional cutting. How this micro-dimple patterning influences the machinability of Inconel 718 is analyzed via orthogonal cutting experiments, and with the optimal parameters, the cutting temperature is lowered by 45.5% and the cutting forces are reduced significantly, i.e., the tangential cutting force and the thrust force are reduced by 61.1% and 47.1%, respectively. The predictions of finite-element simulations agree with experimental observations, and it is concluded that dimpled Inconel 718 offers significantly improved tool wear and surface integrity.

Effect of Spray Characteristic Parameters on Friction Coefficient of Ultra-High-Strength Steel against Cemented Carbide.

Ultra-high-strength steels have been considered an essential material for aviation components owing to their excellent mechanical properties and superior fatigue resistance. When machining these steels, severe tool wear frequently results in poor surface quality and low machining efficiency, which is intimately linked to the friction behavior at the tool-workpiece interface. To enhance the service life of tools, the adoption of efficient cooling methods is paramount. However, the understanding of friction behavior at the tool-workpiece interface under varying cooling conditions remains limited. In this work, both air atomization of cutting fluid (AACF) and ultrasonic atomization of cutting fluid (UACF) were employed, and their spray characteristic parameters, including droplet size distribution, droplet number density, and droplet velocity, were evaluated under different air pressures. Discontinuous sliding tests were conducted on the ultra-high-strength steel against cemented carbide and the effect of spray characteristic parameters on the adhesion friction coefficient was studied. The results reveal that ultrasonic atomization significantly improved the uniformity of droplet size distribution. An increase in air pressure resulted in an increase in both droplet number density and droplet velocity under both AACF and UACF conditions. Furthermore, the thickness of the liquid film was strongly dependent on the spray characteristic parameters. Additionally, UACF exhibited a reduction of 4.7% to 9.8% in adhesion friction coefficient compared to AACF. UACF provided the appropriate combination of spray characteristic parameters, causing an increased thickness of the liquid film, which subsequently exerted a positive impact on reducing the adhesion friction coefficient.

تأثيرعوامل التشغيل الميكانيكي على تآكل آداة القطع وشكل الرقاقة عند عملية الخراطة الجافة لسبيكة الألومنيوم 6060 باستخدام آدوات قطع كربيدية مختلفة الأبعاد

This study presents an experimental investigation of tool wear and chip shape after turning 6060 aluminium alloy using wiper and conventional cutting tools under dry cutting conditions. The influence of parameters such as cutting speed, feed rate, depth of cut, and tool type on tool wear and chip shape were examined. A Taguchi L9 orthogonal array was employed to optimise the cutting parameter, and the effect of each parameter on tool wear was determined using analysis of means (ANOM) and analysis of variance (ANOVA). It was found that a combination of a cutting speed of 1000 rpm, feed rate at 0.1 mm/rev, and depth of cut of 0.5 mm achieved minimal tool wear when dry turning 6060 aluminium alloy using wiper tools. Also, the ANOVA indicated that the main factor contributing to lower tool wear was cutting speed followed by feed rate, with percentage contribution ratios (PCRs) of 54.75% and 37.67% respectively. This confirms that the relationship between cutting speed and tool wear is proportional. Additionally, the ANOVA results demonstrated that the depth of cut, and tool type had little impact on tool wear. Although the wiper and conventional tools are made of similar material with different nose geometries, the wiper insert showed limited abrasion at the higher cutting speed compared to the conventional one. All turning tests produced long irregular continuous curled chips at the beginning of the cutting process, which then changed from the curled to a string-like shape wrapped around the insert tip and the machined part at the end of the turning process. Meanwhile, all the cutting parameters were found to have only a minor impact on chip shape when dry turning 6060 aluminium alloy. Keywords: Dry turning, 6060 aluminium alloy, wiper and conventional coated carbide tools, tool wear, chip shape.

Model-based tool wear detection and fault diagnosis for end mill in various cutting conditions

In recent developments in the field of manufacturing systems, there has been a growing emphasis on optimizing cutting conditions. These optimizations are primarily based on intricate parameters, such as the material removal rate (MRR), surface roughness, and position accuracy. Simultaneously, there’s an increasing focus on enhancing manufacturing efficiency through equipment maintenance strategies that consider parameters, such as corrosion, pressure, temperature, vibration, and other environmental factors. Wear is inevitable during processing, which affects productivity. It is generated in various forms, such as flank, crater, and edge wear, which reduce the tool lifespan and impact machining quality, especially by increasing the cutting forces. Various studies have been conducted to address this issue. Direct measurements using microscopes have high accuracy but require interruption during the process, which adversely affects efficiency and productivity. As a solution, the modern era has witnessed an increase in indirect methods. These methods are often sensor-based, capture data during the machining process, and employ various models, including emerging artificial intelligence techniques, for predicting tool wear. However, these methods have problems with environmental susceptibility, reduced reliability, limitations of application, and excessive costs. This paper suggests a tool wear integrated cutting load prediction model, tool wear detection, and fault diagnosis mechanism. The tool-wear-integrated cutting-load prediction model was constructed by combining the cutting-load prediction and tool-wear models. The coefficients of the model were derived from the actual cutting data extracted by the spindle load. Tool wear detection was implemented by dividing regions based on the tendency of the coefficient of the constructed tool wear integrated cutting load prediction model and the errors between the predicted and actual values. The proposed model demonstrated a performance comparable to that of the existing models in a single-cutting-condition path. However, it excelled in extracting the tool wear coefficients in paths with a mixture of various cutting conditions, which was not achievable with conventional models. Based on these coefficients, the cutting force was predicted with a maximum error of 3.3 %. Also, an accurate determination of the tool-wear regions was possible. Furthermore, the performance of the tool fault diagnosis method was validated using images of tools identified as being at risk of damage.

Prediction of tool wear and surface finish using ANFIS modelling during turning of Carbon Fiber Reinforced Plastic (CFRP) composites

Carbon fiber-reinforced plastics (CFRP) are widely used in various industries due to their high strength to weight ratio, corrosion resistance, durability, and excellent thermo-mechanical properties. The machining of CFRP composites has always been a challenge for the manufacturers. In this study, CNC turning operation with coated carbide tool is used to machine a specific CFRP and the relationship between the cutting parameters (Speed, Feed, Depth of Cut) and response parameters (Vibration, Surface Finish, Cutting Force and Tool Wear) are investigated. An adaptive-network-based fuzzy inference system (ANFIS) model with two multi-input–single-output (MISO) system has been developed to predict the tool wear and surface finish. Speed, feed, depth of cut, vibration and cutting force have been used as input parameters and tool wear and surface finish have been used as output parameters. Three sets of cutting parameter have been used to gather the data points for continuous turning of CFRP composite. The model merged fuzzy inference modeling with artificial neural network learning abilities, and a set of rules is constructed directly from experimental data. This model is capable of predicting the cutting tool wear and surface finish during turning of CFRP composite. The predicted tool wear and surface finish data are compared to the experimental results. The predicted data agreed well with the actual experimental data with 98.96 % accuracy for tool wear and 99.61 % accuracy for surface finish.

An innovative deep reinforcement learning-driven cutting parameters adaptive optimization method taking tool wear into account

Tool wear is critically important for the optimization of cutting parameters. However, the increasing nature of tool wear presents challenges to traditional meta-heuristic cutting parameter optimization methods. To address this issue, we propose an innovative deep reinforcement learning-driven cutting parameters adaptive optimization method taking tool wear into account. More specifically, we use the Markov Decision Process to simulate the optimization process of cutting parameters. Firstly, an innovative deep transfer learning algorithm is used for monitoring tool wear. With the progress of tool wear, the proximal policy optimization method of the transformer with multi-head attention mechanism interacts with the processing environment through a process of trial and error, and accumulates a wealth of experience in selecting cutting parameters through the reward function. The deep reinforcement learning model has quickly discern the best cutting parameters, relying on real-time tool wear value. The experimental results show that the proposed method outperforms other algorithms.

Reliability analysis of cutting tools using transformed inverse Gaussian process-based wear modelling considering parameter dependence

Reliability analysis is crucial for ensuring the performability of the desired function. The cutting tool performs the machining operation at varied conditions to manufacture diverse products. During operation, the tool degrades stochastically in the form of wear. To avoid the unfavourable consequences occurring from severe tool wear, appropriate formulation of the tool reliability, considering threshold degradation level as the failure criterion, is crucial. However, the degradation of the tool during machining is impacted by the current state of the tool wear and operating conditions. Considering these, the present study proposes a state-dependent transformed inverse Gaussian (TIG) process incorporating the effects of operating conditions to develop the tool wear model. In order to evaluate the proposed method, tool wear experiments are conducted at different operating conditions following the Taguchi orthogonal array experimental design. The experimental data are utilised to estimate the parameters of the developed model using the Bayesian approach. Following the parameter estimation, tool reliability is evaluated under varying operating conditions. The comparison of the estimated median time to failure of the tools with the failure time observed in the validation experiments ensures the effectiveness of the proposed model. The proposed approach has the potential to estimate the reliability of the industrial products subjected to state-dependent degradation under varied operating conditions.

Investigating the effect of nanobubble-based cutting fluid on tool wear and cutting forces in milling of Inconel 718

Machining of Nickel-Based Superalloys (NBSAs) is characterized by rapid work hardening, low thermal conductivity, and extreme abrasive behavior. Due to such characteristics of NBSAs, the machining of materials like Inconel 718 (IN718) results in extensive tool wear and high cutting forces. The present study employs nanobubble-based cutting fluid during machining of IN718 and investigates its effect on tool wear and cutting forces. The trochoidal milling experiments were conducted on an IN718 workpiece with nanobubble-based and normal cutting fluid at varying surface speeds. The evolution of tool flank wear area and peak resultant cutting force were recorded and compared to evaluate the effectiveness of nanobubble-based cutting fluid over normal cutting fluid. The results showed that nanobubble-based cutting fluid lowers the tool wear rate by enhancing heat dissipation and reducing abrasion wear. However, higher cutting forces were observed with nanobubble-based cutting fluid due to increase in hardness of the workpiece. Also, it is realized that using nanobubble-based cutting fluid can significantly improve the lifespan of the tool, leading to sustainable manufacturing.

Maximizing machining efficiency and quality in AA7075/Gr/B4C HMMCs through advanced DS-EDM parameter optimization strategies

Abstract Hybrid Metal Matrix Composites (HMMC) offer improved mechanical properties crucial for various industrial applications. However, machining these composites via traditional methods is arduous due to their inherent hardness and abrasive nature. The optimization of the Die-Sinking Electrical Discharge Machining (DS-EDM) process presents a viable solution to overcome these machining challenges and unlock the full potential of HMMCs. Despite the potential benefits of HMMCs, the lack of optimized machining strategies hampers their widespread adoption in industrial applications. Traditional machining approaches often result in excessive tool wear, poor surface finish, and suboptimal material removal rates (MRR) when applied to HMMCs. Additionally, the complex interplay between DS-EDM process parameters and material properties necessitates a systematic investigation to identify optimal machining conditions. This study proposes a multi-step methodology to systematically optimize the DS-EDM process for machining HMMCs comprising AA7075 alloy, 5% of graphite (Gr), and 5% of boron carbide (B4C) particles, which is termed as (AA7075-GR-B4C). Beginning with the formulation of the composite material through stir casting, the research progresses to an L16 Orthogonal Array (L16-OA) based Design of Experiments (DoE) to investigate the effects of key process parameters on machining performance. Experimental testing is conducted to measure mechanical properties, followed by statistical analysis using Analysis of Variance (ANOVA) with Regression Analysis to identify significant factors influencing machining outcomes. Furthermore, an Artificial Neural Network with Black Widow Optimization (ANN-BWO) approach is employed to optimize machining performance metrics such as MRR, Surface Roughness Rate (SRR), and Tool Wear Rate (TWR). The Relative Error (RE) was estimated between experimental MRR (E-MRR), experimental TWR (E-TWR), experimental SRR (E-SRR), and predicted MRR (P-MRR), predicted TWR (P-TWR), predicted SRR (P-SRR) generated by ANN-BWO shows effectiveness of AA7075-GR-B4C.

The capability of Acoustic Emission features to monitor diamond-coated burr grinding wear and effectiveness

Within manufacturing there is a growing need for autonomous Tool Condition Monitoring (TCM) systems, with the ability to predict tool wear and failure. This need is increased, when using specialised tools such as Diamond-Coated Burrs (DCBs) for grinding high strength ceramics or glass, in which the random nature of the tool, inconsistent manufacturing methods and high wear rates create large variance in tool life. This unpredictable nature leads to a significant fraction of a DCB tool's life being underutilised due to premature replacement. Workpiece surface damage, increased grinding forces and large-scale diamond grain pullout could all be the result of high levels of runout and in-circularity common within electroplated DCBs. As such it is important to not only monitor the overall tool wear but also tool condition. Acoustic Emission (AE) presents as an indirect on-machine sensing method highly suited to grinding applications. The high frequency range of AE, >20 kHz, prevents machine noise from dominating the acquired signals, isolating the micro-scale machining processes within noises machine environments. AE resulting from the grinding process has the potential to monitor not only tool wear but also runout and circularity. A series of DCB wear tests have been conducted, each consisting of the continuous acquisition of AE during grinding and frequent tool surface measurements. Preliminary results demonstrate AE kurtosis can be seen as an indicator of each tool’s runout, representing the fraction of time the tool and workpiece are in contact during a revolution. As a result, an indirect monitoring system capable of monitoring wear and tool state with AE could be utilised within the manufacturing sector.

Research on multi-source information fusion tool wear monitoring based on MKW-GPR model

To enhance intelligent manufacturing capabilities and achieve the goal of online real-time high-precision monitoring of tool wear with small sample data, this paper proposes a multi-source information fusion method for turning tool wear monitoring based on the multi-kernel weighted Gaussian process regression (MKW-GPR) model. Firstly, the framework and process of multi-source information fusion for turning tool wear monitoring are established, and the experimental platform is constructed. Secondly, a full-life cutting experiment of the turning tool is conducted. During the experiment, tool wear image data and multi-sensor signal data, including cutting force and vibration signals, are collected. Then, the experimental data were processed to construct a dataset with multi-source information fusion, leading to the establishment of the MKW-GPR model. Subsequently, the experimental dataset was employed to validate the effectiveness of the turning tool wear monitoring model. The results demonstrate that, in recognizing the wear state, the MKW-GPR model exhibited superior recognition performance compared to the single kernel function Gaussian process regression model. Furthermore, in predicting the wear value, the MKW-GPR model is better than the long short-term memory (LSTM), random forest (RF), and scalable end-to-end tree boosting (XGBoost) models. During the small sample size priori point data prediction performance test, the MKW-GPR model achieved the highest accuracy, with a maximum relative error of only 2.8 % in predicting tool life during the severe wear stage. These findings substantiate the feasibility and effectiveness of the proposed turning tool wear monitoring method.

A Study on the Manufacture of CNT Composites Bipolar Plate for the Fuel Cell Using a Nanosecond Pulsed Laser

The channels of the graphite-based bipolar plate for hydrogen fuel cells were mainly manufactured through milling due to low forming elongation and processability. However, during the milling process, the machining precision is low due to tool wear and vibration, and there is a risk of breakage. Non-traditional laser processing can solve the problems of tool wear and vibration through non-contact processing. In this study, the interaction characteristics of the nanosecond pulsed laser and CNT composites were observed, and channels and bipolar plates were manufactured. The effect of scanning speed and pulse duration was observed for interaction characteristics. Defects were observed due to high thermal effects at low scanning speed and high pulse duration. Channels were created depending on parallel pitch distance [] and Number of scans (NOS). The channel was evaluated for depth, top width, bottom width, channel angle, material removal rate, and surface roughness, and significant changes were observed depending on . The chemical composition, surface resistance, and contact angle of the laser-processed channel were measured. The laser processed channel was oxidized, and the surface resistance and contact angle increased. Finally, the manufacturability of the CNT composites bipolar plate using laser was examined.

Experimental investigation on sustainable machining of monel using vegetable oils as cutting fluids and machine learning-based surface roughness prediction

Abstract The material processing industry is anticipated to mitigate environmental degradation. The protocols established by the International Organisation for Standardisation were adhered to. As a result, it would be prudent to investigate the feasibility of minimizing the use of synthetic cutting fluids from the machining process. This study discusses an environmentally-friendly machining technique for turning nickel-based alloy Monel-500, which evaluates four different cooling conditions: dry machining, flood machining, Co-MQL (coconut oil), and Rb-MQL (Rice Bran Oil). These conditions were tested by experimenting with various machining parameters to investigate four aspects of the turning process: surface finish,cutting temperature, tool wear and chip morphology. Rice bran oil is considered eco-friendly compared to synthetic cutting fluids, and employing it in minimum quantity is economical and helps improve the machined workpiece’s surface finish. The investigation has been further extended by applying machine learning algorithms to predict surface roughness, utilising two logical regressions implemented in Python. Among the two machine learning approaches, the random forest regression technique has demonstrated superior results, achieving a prediction accuracy of 99.8%. Consequently, a decision tree has been developed using this regression model to predict the surface roughness. The structured analysis of the decision tree provides more accurate conclusions, offering flexibility in adjusting parameters and expanding options for operation. As a result, the decision tree approach enables the efficient utilisation of production resources and enhances production capacity by making informed choices about cooling methods during the turning process. Rb-MQL has performed better in all aspects than the other three cooling conditions. When comparing machining under dry conditions, flood cooling, Co-MQL, and Rb-MQL (rice bran oil) reduce the tooltip temperature by 39.5%,25.45 and 24.11%, respectively. Rb-MQL reduced surface roughness by 28.23%,43.59 and 60.49% in contrast with machining under dry, flood, and Co-MQL.

Evaluation and quantification of diffusion wear between cutting chip and workpiece using forging press

The state of the interface between the workpiece and the cutting tool affects the cutting temperature and pressure on the tool surface during the cutting process. In particular, while cutting difficult-to-cut materials such as Ni-based alloy 718, the workpiece exhibits a high affinity for cutting tool materials and could easily adhere to them. Adhesion can, at times, adversely affect productivity. The diffusion between the cutting tool and the workpiece is a factor considered to contribute to the adhesion phenomenon during cutting. Addressing this issue involves choosing tool materials and coated materials with high resistance to diffusion and optimizing cutting conditions, particularly the cutting speed, which significantly impacts cutting temperature. However, because cutting tool wear comprises various forms, clarifying the effect of diffusion on tool wear remains open. In this study, to reproduce the diffusion phenomenon between cutting tool and workpiece, two pairs of test specimens were prepared: (1) cemented carbide-AISI 1045 and (2) cemented carbide-Alloy718, which could be held at high temperature under vacuum conditions by a forging press. The degree of diffusion phenomena was evaluated at each tool-work material interface, and the quantification of diffusion amount was performed by diffused element in each work material. Additionally, the theoretical analysis of the diffusion phenomenon using the thermodynamic and phase diagram calculation software Thermo-Calc was also performed.

Tool chip contact length analysis of dry and cryogenic turning of aerospace alloy Ti-6Al-4V

Tool chip contact length is a vital machining index being representative of manufacturing efficiency. Various machining factors including tool wear and energy consumption are governed by tool chip contact length. The current work was undertaken to make a comparative analysis of dry and cryogenic turning of Ti-6Al-4V in terms of their sustainability and productivity indices. During experimentation, cutting speeds of 50 m/min and 125 m/min were selected working at feed rates ranging from 0.16 mm/rev to 0.24 mm/rev. Scanning electron microscopy was utilized to analyse the results. It was observed that machining under cryogenic conditions, in comparison with dry conditions, resulted in up to 51 % lower tool chip contact length. The results were also investigated in terms of chip formation for both machining environments. In addition, benefits of lower tool chip contact length were quantified in terms of lesser tool wear and better energy consumption. Tool chip contact length and chip formation analysis, indicative of the machining process efficiency, highlights that cryogenic condition is more sustainable and productive than dry conditions.

Optimising Sensor Placement for Tool Condition Monitoring: A Comparative Analysis of Acoustic Emission Data

In the monitoring of machining processes and in the context of tool condition monitoring, the position of acoustic emission sensors affects the quality of the resulting information. This drives this comparative study which presents a comparison of acoustic emission data recorded simultaneously by a sensor attached to the side of the milling table and another sensor near the spindle during a tool wear test on a CNC milling machine. The signal information is clearly influenced by the different positions. Whereas the signals near the spindle are dominated by vibrations of the spindle motor during machining, these strong contributions do not occur in the recorded data of the machining process detected by the sensor mounted to the table. However, the position of the sensor near the spindle has the advantage of maintaining a constant distance to the source of the acoustic emissions of the machining process, as the sensor moves with the tool. In contrast, the sensor attached to the milling table is affected from the fact that the distance to the milling tool is constantly changing. This results in different dominant frequencies, that are analysed in this study. To analyse both the entire frequency range and the higher frequencies, two distinct approaches are employed, one without and one with the use of a high-pass filter. Furthermore, the recorded signals were used to calculate the characteristic features, namely the Partial Powers, thereby enabling assessment of the condition of the tool. The results demonstrate that this single feature type is sufficient for training a machine learning model to predict the tool wear, focusing on the flank wear width. This study analyses the efficiency of wear prediction with respect to both sensors and frequency spectra, and compares the results. In this context, the most accurate predictions were achieved with Gaussian process regression models.

Investigation of the effects of heat treatment and hot isostatic pressing on micro-drilling of Ti6Al4V alloy fabricated by laser powder bed fusion

In this study, micro-drilling performance of laser powder bed fusion (LPBF) Ti6Al4V alloy with heat treatment (HT) and hot isostatic pressing (HIP) post-processing conditions was investigated and compared to wrought material. The tests were performed at two spindle rotational speeds (5000 and 10000 rev/min) and a feed per tooth (4 μm/tooth) under dry and minimum quantity lubrication (MQL) conditions using conventional drilling (CD) and peck drilling (PD) strategies. Micro-drilling performance was evaluated in terms of thrust force, surface roughness, subsurface microstructure and microhardness, burr formation, and tool wear. The test results showed that LPBF-HT and LPBF-HIP Ti6Al4V alloys exhibited different micro-drilling performances than the wrought material due to their different microstructural and mechanical properties. The LPBF-HIP Ti6Al4V characterized by higher strength and lamellar microstructure with higher β phase fraction (33.2%) led to higher thrust force, lower surface quality and higher burr height. On the other hand, the wrought Ti6Al4V with higher ductility, lower hardness, and lower β phase fraction (16.3%) showed lower thrust forces, surface roughness, and burr height. Notably, employing MQL conditions with the PD strategy provided good surface quality and reduced burr formation. A higher degree of tool wear was obtained in micro-drilling of LPBF-HIP Ti6Al4V. The major wear types were adhesion, BUE, and flank wear. Besides, chippings were observed in micro-drilling of LPBF-HT and LPBF-HIP Ti6Al4V.

Recurrence analysis of 3D point clouds for the detection of surface anomalies in advanced manufacturing

Due to tool wear, machine malfunction, and other internal and external factors, surface anomalies are widely observed in diverse manufacturing processes, such as machining, stamping, and additive manufacturing. The detection of surface anomalies remains an important challenge for assuring the quality and reliability of manufacturing products. Conventional image-based approaches lack the ability to fully capture the 3-dimensional (3D) morphologies of surface defects, which hinder their applications in defect identification, characterization, and diagnosis. The emerging 3D sensing technologies, such as laser or structured-light scanning, enable the collection of point clouds, which embodies a comprehensive representation of defect-altered surface morphologies in 3D space. However, scanned point cloud data are unstructured and oftentimes contain large numbers of points, which pose significant challenges for the recognition of defective surface patterns. To address the challenges, this study develops a recurrence-based approach for the representation learning of 3D point clouds to detect surface defects in manufactured products. Specifically, with a scanned point cloud, key points are first extracted using a voxel-based sampling strategy, and each key point and its neighboring points form a localized sub-region. Then, a recurrence network is generated through a tailored approach, which considers both the morphology similarity and spatial closeness of sub-regions. For anomaly detection, a tensor-based one-class classification model is further deployed to distinguish abnormal surface patterns from normal ones. The developed methodology is experimentally evaluated through simulation and real-world case studies. Experimental results have demonstrated that the developed methodology outperforms benchmarks, highlighting its efficacy in the detection of surface anomalies.