Progressive Wear Research Articles

Wear Mechanisms and Performance of PET Shredder Blade with Various Geometries and Orientations

Shredder blade is the main component of a shredder machine used in plastic recycling. A comparative performance study was conducted on the shredder blade with different geometries and orientations to understand its wear and shredding mechanism. Identification of the loading distribution along the shredder blades was observed in different orientations. The microstructure and hardness of the worn cutting edge and as-received shredder blade were characterised by optical microscopy, scanning electron microscopy along with the energy dispersive X-ray, X-ray diffraction analysis and hardness testing. The wear mechanism in the shredder blades was categorised as progressive wear. The progressive wear was due to the abrasive, adhesive, and oxidation wear. Recycling efficiency, shredding efficiency, and percentage retention are the parameters used to evaluate the performance of the machine with various geometries and orientations. The best combination of the geometry and orientation is the double-edge shredder blade with spiral orientation, which exhibited recycling efficiency at 97.39 ± 0.04%, shredding efficiency at 69.53 ± 1.32%, and retention at 2.61 ± 0.04%, along with a smaller number of blades recorded severe wear.

A review on tool wear issues in drilling CFRP laminates

Carbon fiber reinforced polymer (CFRP) laminates are featured by superior mechanical properties and excellent structural functions, which have been a promising alternative to conventional alloys and steels in diverse industrial fields. Mechanical drilling is a compulsory operation to shape fibrous composites to desired workpiece quality and target dimensional accuracy. Progressive tool wear is an extremely crucial issue when drilling CFRPs as it may result in undesired machining consequences such as increased force generation, elevated temperatures, and deteriorated surface quality. Despite great endeavors already made to address the wear issues, a complete review of the wear phenomena for CFRP drilling is still significantly lacking. The present paper aims to report the key research advances in tool wear aspects when drilling CFRPs. The wear phenomena, wear modes, and tool failure mechanisms are carefully reviewed. The effects of wear progression on various drilling outputs of CFRP laminates, such as drilling forces, cutting temperatures, and surface quality attributes, are discussed. A particular focus is placed on the quantification and assessment of tool wear during drilling CFRPs. Some research prospects for both academia and industry are outlined. The paper intends to offer a comprehensive understanding of wear mechanisms controlling the drilling of CFRP laminates.

A novel current sensor indicator enabled WAFTR model for tool wear prediction under variable operating conditions

The health indicators (HIs) were extracted from the current sensor to represent the tool wear progression. The extracted HIs were found poorly correlated with the progression of tool wear as the raw current sensor signal was susceptible to the influence of other parts and structures in the machine tool. Hence, this paper proposed a novel current sensor-based HI that utilized the mean of inverse hyperbolic cosine function fitted to an envelope of the current signal to improve the correlation. Using the extracted HIs, many bespoke machine learning (ML) models have been developed by researchers. However, these models have many hyperparameters, difficult to interpret and especially poor prediction accuracy has been observed under variable operating conditions. This study overcame these issues by proposing a Weibull Accelerated Failure Time Regression (WAFTR) model, which combines process parameters data with HI for improving the prediction accuracy under variable operating conditions. This model mapped a functional relationship with tool wear in the form of probability density function to identify best HIs and acceleration/deacceleration factors which makes it interpretable. The acceleration/deacceleration factors are useful to deaccelerate the tool wear evolution by controlling the specific values of the machining parameters.

Modelling of Metal Removal Rate in Copper Alloy Milling Using MATLAB

The copper found in Earth’s soil ranks fourth in abundance among structural metals. Copper alloys are composed of copper and other elements. Most commonly, these alloys are used in aerospace, medical, and energy applications, but they are also used in many other areas. The amount of the stabilizing agents and the temperature determine which phase copper alloys exist in, including α, α + β, and β. The temperature in the cutting zone is one of the most important factors to control when machining copper alloys. Copper alloys have low thermal conductivity and high heat capacity, meaning that they have low heat conduction from the cutting zone, which leads to the built-up heat in the cutting edge. As the workpiece and cutting tool moves at different speeds, the temperature is strongly affected by the cutting speed. The physical and chemical properties of tool wear progression have been used in several studies and research projects to model tool life and metal removal as a result of this fact. The focus of this article is on establishing a model connecting cutting parameters and measured responses in terms of tool life, using the design of experiments and metamodeling to establish a model that can be used to predict tool life from milling experiments. In order to secure reliable machining operations, these models were designed for customer recommendation and cutting data optimization. The study focused on copper alloys 6Al-4V, the most common being α + β alloy. In conclusion, the two models developed in this study are able to predict the tool life based on the cutting speed and radial width of cuts. As long as certain parameters are met, the models will ensure the highest possible metal removal rate.

A model approach for in-process tool condition monitoring in CNC turning using machine vision

Tool wear monitoring and real-time predicting tool life during the machining process is becoming a crucial element in modern manufacturing to properly determine the ideal point to replace tool, remains a challenge currently. In this paper, the model approach for in-process monitoring and predicting progressive tool wear by using machine vision is proposed. The developed method adopts machine vision to acquire tool wear images from a CCD camera. The emerged wear analysis is conducted based on the in-progress of signal processing on captured tool wear images, received throughout the cutting process. This automated analysis is carried out with programming to assess and compare a number of pixels of cutting edge images between cutting tools before machining and during the machining process. The developed system is evaluated through experiments of actual cutting conducted on the CNC turning machine with the proposed system installed to evaluate progressive wear during the machining process. Experimental results are capable of indicating the emerged wear at the current state by comparing the number of pixels between the new and used tools. Average flank wear (VB) is also evaluated linked to tool rejection criteria. The developed system is validated by the 3D microscope measuring actual wear on the used tool after cutting experiments. Comparative wear analysis is then performed by finding the correlation equation of pixels examined by a developed system and SMr2 value measured by the microscope. The results showed that the relationship between the number of pixels and SMr2 is a strong correlation.

Towards a systematical approach for wear detection in sheet metal forming using machine learning

Wear is one of the decisive factors for the economic efficiency of sheet metal forming processes. Thereby, progressive wear phenome lead on the one hand to a poor workpiece quality and on the other hand to tool failure resulting in high machine downtimes. This trend is intensified by processing high-strength materials and the reduction of lubricant up to dry forming. In this context, data-driven monitoring methods such as machine learning (ML) provide the potential of detecting wear at an early stage to overcome manual and cost-intensive process inspections. The presented study aims to provide a ML based inline quantification of wear states within sheet metal forming processes. The development of this monitoring approach is based on a procedure model the Knowledge Discovery in Time series and image data in Engineering Epplications (KDT-EA) which is validated on two forming processes, blanking and roll forming, that strongly differ in their physical process behavior and their acquired process data. The presented inline quantification allows an estimation of wear states with a deviation of less than 0.83% for the blanking process and 2.21% for the roll forming process from the actual wear state. Furthermore, it is shown that combining different feature extraction methods as well as a compensation of unbalanced data using data augmentation techniques are able to improve the performance of the investigated ML models.

Risk prediction models for erosive wear in preschool-aged children: a prospective study

BackgroundDespite increasing prevalence, age-specific risk predictive models for erosive tooth wear in preschool-age children have not been developed. Identification of at-risk groups and the timely introduction of behavioural change or treatment will stop the progression of erosive wear in the permanent dentition. This study aimed to identify age-specific risk factors for erosive wear. Distinct risk prediction models for 3-year-old and 5-year-old children were developed.MethodsA prospective cohort study included school-based clinical examinations and parent administered questionnaires for consented 3 and 5-year-old healthy children. Calibrated examiners measured the following health parameters under standardised conditions: erosion, using the Basic Erosive Wear Examination Index (BEWE), caries using the International Caries Detection and Assessment System (ICDAS), plaque and calculus according to the British Association for the Study of Community Dentistry (BASCD) scores, dental traumatic injuries and soft tissue lesions, salivary testing and BMI. Other health conditions were collected via a parent-administered questionnaire that explored oral- and general-health. Non parametric tests were utilised to explore the temporal relation of erosion with, demographic factors, oral hygiene habits, general health and dietary habits. Variables showing significance with a difference in BEWE cumulative score over time were utilised to develop two risk prediction models. The models were evaluated by Receiver Operating Characteristics analysis.ResultsRisk factors for the 3-year-old cohort (N = 336) included erosive wear (χ2(1, 92) = 12.829, p < 0.001), district (χ2(5, 92) = 17.032, p = 0.004) and family size (χ2(1, 92) = 4.547, p = 0.033). Risk factors for the 5-year-old cohort (N = 441) also included erosive wear (χ2(1, 144) = 4.768, p = 0.029), gender (χ2(1, 144) = 19.399, p < 0.001), consumption of iced tea (χ2(1, 144) = 8.872, p = 0.003) and dry mouth (χ2(1, 144) = 9.598, p = 0.002).ConclusionsPredictive risk factors for 3-year-old children are based on demographic factors and are distinct from those for 5-year-old children based on biological and behavioural factors. Erosive wear is a risk factor for further wear in both age cohorts.

Precision of &lt;em&gt;In Vivo&lt;/em&gt; Quantitative Tooth Wear Measurement Using Intra-Oral Scans

Quantitative wear measurement is of increasing interest for measuring tooth wear progression. However, most research on quantitative wear measurement has focused on simulated wear or scanned gypsum casts. A 3D Wear Analysis (3DWA) protocol has been developed that analyzes tooth wear in vivo through intra-oral scanners available to dental clinicians. This study investigated the precision of the 3DWA protocol for measuring wear through maximum height loss (mm) and volume change (mm3). Observational prospective wear data from 55 patients were analyzed after 0-1-, 0-3-, and 0-5-year intervals to determine rates of wear, and convenience samples were chosen to test the protocol's precision on dentitions scanned twice in one sitting and its intra- and inter-rater precision on scans with 0-3- and 0-5-year intervals. Scans were made using intra-oral scanners (IOS) and superimposed using 3D measurement software. T-tests were performed to determine the structural and random error, and trimmed ranges were calculated to interpret the error. For protocol precision, the mean difference was 0.015 mm (-0.002; 0.032, p = 0.076) for height and -0.111 mm3 (-0.250; 0.023, p = 0.101) for volume. The duplicate measurement error was 0.062 mm for height and 0.268 mm3 for volume. The height measurements were precise enough to measure wear after intervals of 0-3 or 0-5 years; however, volume measurements were susceptible to procedural error and operator sensitivity. The 3DWA protocol is precise enough to adequately measure tooth height loss after intervals of a minimum of 3 years or in patients with severe wear progression, but it is not suited to measuring volumetric changes.

Precision of In Vivo Quantitative Tooth Wear Measurement using Intra-Oral Scans.

Quantitative wear measurement is of increasing interest for measuring tooth wear progression. However, most research on quantitative wear measurement has focused on simulated wear or scanned gypsum casts. A 3D Wear Analysis (3DWA) protocol has been developed that analyzes tooth wear in vivo through intra-oral scanners available to dental clinicians. This study investigated the precision of the 3DWA protocol for measuring wear through maximum height loss (mm) and volume change (mm3). Observational prospective wear data from 55 patients were analyzed after 0-1-, 0-3-, and 0-5-year intervals to determine rates of wear, and convenience samples were chosen to test the protocol's precision on dentitions scanned twice in one sitting and its intra- and inter-rater precision on scans with 0-3- and 0-5-year intervals. Scans were made using intra-oral scanners (IOS) and superimposed using 3D measurement software. T-tests were performed to determine the structural and random error, and trimmed ranges were calculated to interpret the error. For protocol precision, the mean difference was 0.015 mm (-0.002; 0.032, p = 0.076) for height and -0.111 mm3 (-0.250; 0.023, p = 0.101) for volume. The duplicate measurement error was 0.062 mm for height and 0.268 mm3 for volume. The height measurements were precise enough to measure wear after intervals of 0-3 or 0-5 years; however, volume measurements were susceptible to procedural error and operator sensitivity. The 3DWA protocol is precise enough to adequately measure tooth height loss after intervals of a minimum of 3 years or in patients with severe wear progression, but it is not suited to measuring volumetric changes.

Tool wear patterns and their promoting mechanisms in hybrid cooling assisted machining of titanium Ti-3Al-2.5V/grade 9 alloy

Hybrid lubri-cooling is a latest technology that provides synergistic cooling and lubrication effect in the machining area especially in the cutting of titanium and its alloys. In this current study, cryogenic-LN2, minimum quantity lubrication (MQL), and hybrid cryogenic LN2-MQL are applied and compared against dry medium in perspective of in-depth analysis of tool flank wear, EDS mapping, and intensity of tool wear. Experimental results showed that in comparison with dry, hybrid LN2-MQL substantially reduced the tool flank and rake wear followed by LN2, MQL, and dry conditions, respectively. Additionally, the SEM and EDS analysis depicted relatively less severe wear and chemical elements adhesion on the tool’s main cutting edge, while turning titanium alloy under a hybrid LN2-MQL lubri-cooling environment. In addition, the dry condition has maximum value of tool wear progressions i.e., 1.04 mm and hybrid LN2-MQL have 0.06 mm while machining titanium alloys. When tool wear is evaluated from a tribological point of view, the reduction in flank wear value compared to dry machining is 89.4 %, 92.3 % and 94.2 % owing to MQL, LN2, MQL and hybrid LN2-MQL cutting strategies. In terms of crater wear, the improvement was 87.7 %, 90.4 % and 90.8 % thanks to MQL, LN2, MQL and hybrid LN2-MQL.

Trans-Posterior Cruciate Ligament All-Inside Root Repair Versus Partial Meniscectomy for Medial Meniscus Posterior Root Tears: Comparison of Semiquantitative and Quantitative MRI Outcomes in Cartilage Degeneration and Osteoarthritic Progression.

ObjectiveTo perform conventional, morphological, and T2 mapping compositional MRI imaging to assess the cartilage degeneration and osteoarthritic progression in patients with medial meniscus posterior root tears (MMPRTs) who underwent trans-posterior cruciate ligament (PCL) all-inside repair or partial meniscectomy.DesignPatients with MMPRTs after trans-PCL all-inside repair (group AR) or partial meniscectomy (group PM) between 2015 and 2018 were retrospectively identified. Preoperative and postoperative conventional MRI were collected to assess medial meniscus extrusion (MME) and the whole-organ magnetic resonance imaging score (WORMS). Postoperative morphological MRI and T2 mapping compositional MRI were collected to evaluate the quantitative cartilage thickness/volume and cartilage composition.ResultsThe final cohort consisted of 21 patients in group AR and 22 patients in group PM, with no differences in demographic data and baseline patient characteristics between the 2 groups. Group AR demonstrated less progression of articular cartilage wear (P < 0.05) and decreased meniscal extrusion (P = 0.008) than group PM at the final follow-up. In addition, group AR demonstrated less extracellular matrix degeneration in the cartilage subregion of the medial compartment (P < 0.05) than group PM with lower T2 relaxation times in the superficial layer of the articular cartilage.ConclusionTrans-PCL all-inside repair of MMPRTs could delay the initial cartilage deterioration and morphological cartilage degeneration compared with partial meniscectomy. However, the amount of residual meniscal extrusion is clinically important, and an improved root repair fixation method should be investigated.

Optical Cutting Tool Wear Monitoring by 3D Geometry Reconstruction

Cutting tool wear needs to be monitored closely to ensure good quality of machined parts. However, manual inspection is both expensive and time consuming, therefore there is a need for automated monitoring methods. We present a technique that can reconstruct the cutting tool surface in 3D, allowing a spatial estimation of the tool wear with high accuracy. The reconstruction allows an automated direct monitoring method that estimates at any time the cutting tool condition, avoiding conversion work and major quality issues. The optical measurement setup consists of a hardware triggered line scan camera that registers the spinning cutting tool’s shadow inflicted by a collimated backlight. We show how to leverage the 1D line scan signal acquired at varying cutting heights of the tool into a full 3D reconstruction. The progression of tool wear may thus be monitored by comparing the reconstructed shape to previous measurements. To this end we show a methodology for tool wear quantification. Additionally, to assess the measurement technique, an accuracy analysis with ground truth geometry was performed. The technique was applied to multiple degrading drilling tools. By automation of the cutting tool health monitoring, retrofitting this technology on a conventional machining center would transform it into an Industry 4.0 compatible (smart) machining center utilizing off-the-shelf optical equipment with moderate costs.

Prognosis of Wear Progression in Electrical Brakes for Aeronautical Applications

The evolution towards “more electric” aircrafts has seen a decisive push in the last decade, due to the growing environmental concerns and the development of new market segments (flying taxis). Such push interested both the propulsion components and the aircraft systems, with the latter seeing a progressive trend in replacing the traditional solutions based on hydraulic power with electrical or electromechanical devices. Although more attention is usually devised towards the flight control actuation, an interesting and fast-developing application field for electro-mechanical systems is that of the aeronautical brakes. Electro-mechanical brakes, or E-Brakes hereby onwards, would present several advantages over their hydraulic counterparts, mainly related to the avoidance of leakage issues and the simplification of the system architecture. The more difficult heat dissipation, associated with the thermal issues that usually constitute one of the most significant sizing constraints for electromechanical actuators, limits so far, their application (or proposal of application) to light-weight vehicles. Within this context, the development of PHM solutions would align with the need for an on-line monitoring of a relatively unproven component. This paper deals with the preliminary stages of the development of such PHM system for an E-Brake to be employed on a future executive class aircraft, where the brake is actuated through four electro-mechanical actuators. Since literature on fault diagnosis and prognosis for electrical motors is fairly extensive, we focused this preliminary analysis on the development of PHM techniques suitable to monitor and prognose the evolution of the brake pads wear instead. The paper opens detailing the system architecture and continues presenting the high-fidelity dynamic model used to build synthetic data-sets representative of the possible operating conditions faced by the E-Brake within realistic operative scenarios. Such data are then used to foster a preliminary feature selection process, where physics-based indexes are compared and evaluated. Simulated degradation histories are then used to test the application of data-driven fault detection algorithm and the possible application of particle-filtering routines for prognosis.

An intelligent fault diagnosis framework based on piecewise aggregate approximation, statistical moments, and sparse autoencoder

Rotating machines (RMs) have vast applicability in almost all the industries in mechanical domain. Rolling element bearings (RBs) are the key elements to ensure that the RMs perform efficiently. RBs are highly prone to wear and tear which could have devastating consequences such as massive economic losses and accidents. In the past, many time-domain based condition-indicators such as root mean square (RMS), skewness and kurtosis, etc. have been proposed by researchers to diagnose the bearing faults and prevent RM failures. However, they are often insensitive to early stage faults, affected by outliers and possess poor degradation tracking characteristics. To overcome these shortcomings, this paper proposes a novel statistical feature extraction technique called as multiscale statistical moment (MSM) analysis, in combination with sparse autoencoder to detect the incipient faults as well as track the progression of wear. Firstly, the vibration signal are acquired from the bearings to be monitored. Secondly, the MSM features are extracted from the vibration signals. Thirdly, the MSM features corresponding to normal conditions are utilized to train the sparse autoencoder network. Fourthly, the MSM features corresponding to test conditions are supplied to the pre-trained sparse autoencoder model. The MSM technique offers the advantage that it extracts the fault properties contained in multiple time-scales of the vibration signals instead of a single time-scale only. Finally, the dissimilarity between the actual and predicted output is measured to obtain the bearing health indicator (BHI). The experimental results demonstrate that the suggested BHI detects the faults at early stages, possess better sensitivity and trends the bearing degradation more accurately as compared to the traditional techniques such as RMS, kurtosis, and BHI obtained with statistical moment features at single-scale only.

Online damage detection of cutting tools using Dirichlet process mixture models

The ability to monitor and predict tool deterioration during machining is an important goal because the state of wear has a significant influence on the surface quality of machined components. To build up a comprehensive condition monitoring system for diagnosis and prognosis, however, extensive measurements and knowledge of tool wear is required. Collecting labelled datasets that include damage information for this purpose can be expensive and time consuming.This paper suggests an unsupervised clustering approach using Dirichlet process mixture models to detect the change in characteristics of a cutting process online for diagnosis. As well as providing a useful monitoring tool, this approach has the potential to reduce the need for exhaustive wear measurements associated required for prognosis. The model is well suited to the erratic and unpredictable nature of tool wear progression, as the number of clusters required to determine the possible damage states are not set a-priori. Consequently, this method is equipped to handle variations across homogeneous and heterogeneous groups of tool material compositions.The proposed approach is demonstrated here as a method to reduce the time required for trials for wear characterisation of new tools. In the example shown, the results indicate that the approach would result in around a 30% reduction of test times (on average) during outer diameter turning of case hardened steel, across 10 Polycrystalline cubic Boron Nitride tools from two different material compositions.

Associated risk factors with quantitative erosive tooth wear progression

ObjectivesThe aim of this study was to determine predictors of erosive tooth wear (ETW) progression, using novel primary-care quantitative analysis techniques. MethodsIn a single-centre, prospective, observational cohort longitudinal study, adolescents, aged 11–18 years, underwent a baseline BEWE examination, validated risk factors questionnaire and baseline intraoral scan (IOS, TRIOS 3.0, 3Shape, Copenhagen, Denmark). One year later a repeat scan was taken. Quantitative analysis of wear progression (volume loss) on first permanent molars and upper central incisors was performed using previously published protocols. Multilevel linear regression was used to investigate the associations between baseline predictors and volume loss per mm2. ResultsA total of 295 dental surfaces (131 incisors and 164 molars) in 70 patients were analysed for this study. The mean age of participants at baseline was 15.0 years (SD: 0.79), and the average time between scans was 11.8 months (1.61 SD; range 10–13). The mean volume loss per mm2 of dental surfaces was -0.013 mm3 (SD: 0.009). Greater volume loss was observed amongst adolescents with higher baseline BEWE scores and those whose parents had lower education as well as on molar than incisor surfaces. No associations were found with dietary acid intake and intrinsic acid exposure when baseline levels of tooth wear, parental education and other factors were controlled for. ConclusionsThe findings help to characterise groups at greater risk of ETW and would indicate that improved screening and upstream preventative measures should form the basis of a preventative program. Clinical significanceIntra-orals scans and registration software might improve the ability to diagnose, monitor and prevent the early loss of dental hard tissue.

Tool Wear Prediction When Machining with Self-Propelled Rotary Tools.

The performance of a self-propelled rotary carbide tool when cutting hardened steel is evaluated in this study. Although various models for evaluating tool wear in traditional (fixed) tools have been introduced and deployed, there have been no efforts in the existing literature to predict the progression of tool wear while employing self-propelled rotary tools. The work-tool geometric relationship and the empirical function are used to build a flank wear model for self-propelled rotary cutting tools. Cutting experiments are conducted on AISI 4340 steel, which has a hardness of 54–56 HRC, at various cutting speeds and feeds. The rate of tool wear is measured at various intervals of time. The constant in the proposed model is obtained using genetic programming. When experimental and predicted flank wear are examined, the established model is found to be competent in estimating the rate of rotary tool flank wear progression.

Wear Analysis and Tool Life Modeling in Micro Drilling of Inconel 718 Superalloy

Abstract Aircraft engine parts, specifically blades, discs, and sheets are primarily made up of Inconel 718, which required drilled holes for assembly. Drilling holes in Inconel 718 is very difficult due to its high hardness, especially microdrilling. The primary studies majorly used uncoated drills for microdrilling Inconel 718, which leads to low tool life due to frequent tool wear. Microdrills bearing different coatings have not been investigated for their efficiency and mechanism of wear in microdrilling of super alloys. Also, time-dependent prediction of cutting tool wear has not been addressed adequately. In this study, a theoretical model to predict tool wear in drilling as a function of time and to evaluate tool life of anti-abrasion-, TiAlSiN-, and TiAlN-coated drills are presented. Considering feed rate and cutting speed as independent variables microdrilling experiments were conducted. In addition, the model estimates the progression of abrasion wear in various sections of the drill and the section with highest wear-rate was considered for calculation of drill life. Life of uncoated and coated drills were also calculated using a failure criterion of 31.3 μm and Taylor’s tool life exponents. Finally, a comparison of the experimental and modelling results was done to validate the model. It was observed that the model and experimental results are in good agreement at low and medium cutting speed. Both the coatings increased the life of the drills compared to uncoated drills. The wear-rate is highest at the corner of the drill. The mode of failure for TiAlSiN-coated drill was noticed to be corner chipping and for TiAlN-coated tool was built-up-edge (BUE) formation followed by flank chipping.

Short-Term Cross-Sectional Time-Series Wear Prediction by Deep Learning Approaches

Abstract Wear is one of the major causes that affect the performance and reliability of tribo-systems. To mitigate its adverse effects, it is necessary to monitor the wear progress so that preventive maintenance can be timely scheduled. An online visual ferrograph (OLVF) apparatus is used to obtain online measurements of wear particle quantities, and monitor the wearing of a four-ball tribometer under different lubrication conditions, and several popular deep learning algorithms are evaluated for their effectiveness in providing maintenance decisions. The obtained data are converted to the cross-sectional time series (CSTS), for its effectiveness in representing the variation trends of multiple variables, and the data are used as the input to the deep learning algorithms. Experimental results indicate that the CSTS together with the bidirectional long short-term memory (Bi-LSTM) architecture outperforms other tested settings in terms of the mean-squared error (MSE). Increased prediction accuracy is observed for tribological pairs with a stochastically changing coefficient of friction.

Pseudo analytical modelling of flank wear for coated/micro blasted cemented carbide cutting tools

Machining of difficult to cut material leads to excessive amount of tool wear owing to dynamic nature of stresses. The amount of tool wear affects the machinability as well as tolerances during the cutting operation. Thus, monitoring of the flank wear is necessary under sustainable cutting environment. However, such monitoring in industrial and practical applications is a tedious task. The current study focuses on development of pseudo analytical model to predict the wear behaviour during the machining operation for the coated as well as surface modified (coated/micro blasted) tools. The model is possibly the first of its kind which incorporates the hardness of the coating, workflow stress and forces acting on the tool, while predicting the tool wear values. The developed model was calibrated and validated experimentally during the turning of Nimonic C 263 using AlTiN coated tools (treated and untreated) at varying cutting conditions. During the analysis it was found that the experimental results were very much in agreement with predicted values of the flank wear, and the flank wear progression also followed the similar pattern as that of the later.