Tool Wear Progression Research Articles

Tool life prognostics in CNC turning of AISI 4140 steel using neural network based on computer vision

One of the essential requirements for intelligent manufacturing is the low cost and reliable predictions of the tool life during machining. It is crucial to monitor the condition of the cutting tool to achieve cost-effective and high-quality machining. Tool conditioning monitoring (TCM) is essential to determining the remaining useful tool life to assure uninterrupted machining to achieve intelligent manufacturing. The same can be done by direct and indirect tool wear measurement and prediction techniques. In indirect methods, the data is acquired from the sensors resulting in some ambiguity, such as noise, reliability, and complexity. However, in direct methods, the data is available in images resulting in significantly less chances of ambiguity with the proper data acquisition system. The direct methods, which provide higher accuracy than indirect methods, involve collecting images of worn tools at different stages of the machining process to predict the tool life. In this context, a novel tool wear prediction system is proposed to examine the progressive tool wear utilizing the artificial neural network (ANN). Experiments were performed on AISI 4140 steel material under dry cutting conditions with carbide inserts. The cutting speed, feed, depth of cut, and white pixel counts are considered as input parameters for the proposed model, and the flank wear along with remaining tool life is predicted as the output. The worn tool images were captured using an industrial camera during the turning operation at regular intervals. The ANN training set predicts the remaining useful tool life, especially the sigmoid function and rectified linear unit (ReLU) activation function of ANN. The sigmoid function showed an accuracy of 86.5%, and the ReLU function resulted in 93.3% accuracy in predicting tool life. The proposed model’s maximum and minimum root mean square error (RMSE) is 1.437 and 0.871 min. The outcomes showcased the ability of image processing and ANN modeling as the potential approach for developing a low-cost industrial tool condition monitoring system that can measure tool wear and predict tool life in turning operations.

The Use of TOPSIS Method for Multi-Objective Optimization in Milling Ti-MMC

This paper presents the use of TOPSIS, a multi-criteria decision-making model combined with the Taguchi method to find the optimum milling parameters. TOPSIS is the Technique for Order Preference by Similarity to the Ideal Solution and shows the value of closeness to the positive ideal solution. This study shows the optimum combination of process parameters using the shortest distance from the ideal solution. The surface roughness and flank tool wear were considered the objectives for simultaneous optimization. After converting multiple responses into a single response, the Taguchi method was used to analyze and determine the optimum machining parameters. According to reported studies, the initial wear behavior and initial cutting conditions have significant effects on the tool wear progress. Several initial cutting parameters can contribute to tool life and therefore can be used to improve both tool life and surface roughness. However, the cutting speed may significantly affect tool wear and ultimate tool life. In this study, an innovative solution was proposed for interrupted machining with two different cutting speeds. The first level cutting speed was used for 1 s and the second level was used for the rest of the process. The experimental results indicate that the initial speed followed by the feed rate significantly affects tool life. In addition, using the proposed strategy with different levels of cutting speed during machining operations led to improved tool life and surface roughness compared to conventional machining with uniform cutting speed throughout the entire process.

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.

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.

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.

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.

Effect of Porosity on Tool Wear During Micromilling of Additively Manufactured Titanium Alloy

Abstract Porosity is a major quality issue in additively manufactured (AM) materials due to improper selection of raw material or process parameters. While porosity is kept to a minimum for structural applications, parts with intentional (engineered) porosity find applications in prosthetics, sound dampeners, mufflers, catalytic converters, electrodes, heat exchangers, filters, etc. During postprocessing of additive manufactured components using secondary machining to obtain required dimensional tolerance and/or surface quality, part porosity could lead to fluctuating cutting forces and reduced tool life. The machinability of the porous AM material is poor compared to the homogenous wrought material due to the intermittent cutting and anisotropy of AM materials. This paper investigates the tool wear progression and underlying mechanisms in relation to the porosity of AM material during their machining. Micromilling experiments are carried out on AM Ti6Al4V alloy with different porosity levels. Insights into tool-workpiece interaction during micromachining are obtained in cases where pore sizes could be comparable to the cutting tool diameter. Findings of this research could be helpful in developing efficient hybrid additive-subtractive manufacturing technologies with improved tool life and reduced costs.

Comparative analysis of tool wear progression of dry and cryogenic turning of titanium alloy Ti-6Al-4V under low, moderate and high tool wear conditions

Comparative analysis of tool wear progression of dry and cryogenic turning of titanium alloy Ti-6Al-4V under low, moderate and high tool wear conditions

Impacts of flank wear and cooling strategies on evolutions of built-up edges, diffusion wear and cutting forces in Ti6Al4V machining

Titanium alloys are widely used in aerospace industries due to their excellent physical and mechanical performances, however, their poor machinability always induce fast tool wear. To better understand the interaction between progressive tool wear and cooling strategies in machining of titanium alloy Ti6Al4V, an in-depth analysis towards evolutions of built-up edges (BUEs), diffusion wear and cutting forces was carried out through experimental work. Design of experiments was used towards analyzing pertinent effects involved in machining Ti6Al4V. Uncoated carbide inserts were artificially treated with different initial flank wear (VB) in order to decouple the effects involved in progressive wear condition from rake and flank faces. Dry cutting, flood cutting with emulsion and cryogenic cutting with liquid nitrogen (LN2) were used as different cooling strategies. The results show that initial VB presents significant contributions to BUEs formation, diffusion wear and cutting forces fluctuation, especially under aggressive cutting conditions. Cryogenic assistance significantly eliminates these issues compared to dry and flood conditions.

Analytical model for predicting tool wear in orthogonal machining of unidirectional carbon fibre reinforced polymer (CFRP)

Progressive tool wear due to abrasive carbon fibres is one of the main issues in machining of CFRP and responsible for the short tool life. Because of occurring wear during machining, the tool’s micro-geometry changes continuously resulting in higher process forces and an increasing risk for workpiece damages. In this paper, a novel analytical model is presented in order to predict the wear-related change of the micro-geometry in orthogonal machining of CFRP depending on the fibre orientation and the initial tool geometry. For this purpose, a concept called the wear rate distribution is introduced which represents a measure to quantify the wear rate along the active micro-geometry. Based on experimental investigation, it is shown that the shape of an arbitrary wear rate distribution between two closely spaced wear states can be approximated and parameterised with a “line - curve - line” approach. Using the authors’ previously published analytical force model, the wear rate distribution can be calculated as function of five wear parameters that are used to parameterise the active micro-geometry of an arbitrary wear state. Based on an iterative solver, this is used to simulate the tool wear progression during machining. For model validation, the simulation is compared to experimental data in terms of the cutting edge profiles, the amount of worn tool material and the process forces. Accordingly, the wear model is capable to reproduce the most important wear characteristics, e.g. the cutting edge rounding, the decreasing clearance angle and the increasing contact length at the flank face.

Effect of cutting parameters on tool life during end milling of AISI 4340 under MQL condition

PurposeThe purpose of this paper is to study the cutting performance of high-speed regime end milling of AISI 4340 by investigating the tool life and wear mechanism of steel using the minimum quantity lubrication (MQL) technique to deliver the cutting fluid.Design/methodology/approachThe experiments were designed using Taguchi L9 orthogonal array with the parameters chosen: cutting speed (between 300 and 400 m/min), feed rate (between 0.15 and 0.3 mm/tooth), axial depth of cut (between 0.5 and 0.7 mm) and radial depth of cut (between 0.3 and 0.7 mm). Toolmaker microscope, optical microscope and Hitachi SU3500 Variable Pressure Scanning Electron Microscope used to measure tool wear progression and wear mechanism.FindingsCutting speed 65.36%, radial depth of cut 24.06% and feed rate 6.28% are the cutting parameters that contribute the most to the rate of tool life. The study of the tool wear mechanism revealed that the oxide layer was observed during lower and high cutting speeds. The former provides a cushion of the protective layer while later reduce the surface hardness of the coated toolOriginality/valueA high-speed regime is usually carried out in dry conditions which can shorten the tool life and accelerate the tool wear. Thus, this research is important as it investigates how the use of MQL and cutting parameters can prolong the usage of tool life and at the same time to achieve a sustainable manufacturing process.

Fundamental Investigation into Tool Wear and Surface Quality in High-Speed Machining of Ti6Al4V Alloy.

This paper reports a fundamental investigation consisting of systematic trials into the response of Ti6Al4V alloy to high-speed machining using carbide inserts. It is a useful extension to work previously published, and aims at assessing the impact of the process parameters, depth of cut, cutting speed and feed rate in addition to cutting length, and their interrelations, on observed crater and flank wear and roughness of the machined surface. The results showed that abrasion was the most important flank wear mechanism at high speed. It also showed that increased cutting length accelerated crater wear more than exhibited flank wear and had considerable effect on surface roughness. In particular, crater wear increased by over 150% (on average), and flank wear increased by 40% (on average) when increasing cutting length from 40 to 120 mm. However, cutting the same length increased surface roughness by 50%, which helps explain how progression of tool wear leads to deteriorated surface quality. ANOVA was used to perform statistical analyses of the measured data and revealed that cutting length and depth of cut had the greatest effect on both crater and flank wear of the cutting tool. These results confirm that high-speed machining of Ti6Al4V alloy is a reliable process, with cutting speed identified as having a relatively small influence on the tool wear and resultant roughness of the machined surface relative to other parameters.

DEVELOPMENT OF A MEASURING AND CONTROL UNIT FOR AN INTELLIGENT ADJUSTMENT OF PROCESS PARAMETERS IN SAWING

Sawing frozen wood differs significantly from sawing unfrozen wood. Temperatures below zero degrees result in an increasing hardness of wood, rising cutting forces and a progression of tool wear. Currently, the adaption of process parameters due to different wood temperatures is based on operator experience, leading to overloaded saw blades, hence causing considerable economic damage by increasing production downtime. This paper presents the development of a measuring and control unit featuring an integrated database of processing values enabling operators to use most suitable process parameters, thus extending the service life of circular saw blades and increasing the performance of sawmills.

Optimization of the Cutting Regime in the Turning of the AISI 316L Steel for Biomedical Purposes Based on the Initial Progression of Tool Wear

The development of biomedical devices has improved the quality of life for millions of people. The increase in life expectancy generates an increase in the demand for these devices. One of the most used materials for these purposes is 316 L austenitic stainless steel due to its mechanical properties and good biocompatibility. The objective of the present investigation was to identify the dependence between the main cutting force, the initial speed of the tool wear, the surface roughness, and the parameters of the cutting regime. Based on these dependencies, a multi-objective optimization model is proposed to minimize the energy consumed and initial wear rate, as well as to maximize productivity, maintaining the surface roughness values below those established by the ISO 5832-1 standard. The wear of the cutting tool was measured on a scanning electron microscope. For the optimization process, a genetic algorithm based on NSGA-II (Non-nominated Sorting Genetic Algorithm) was implemented. The input variables were the cutting speed and the feed in three levels. The cutting force and surface roughness were set as restrictions. It is concluded that the mathematical model allows for the optimization of the cutting regime during dry turning and with the use of MQL (Minimum Quantity Lubrication) with BIDEMICS JX1 ceramic tools (NTK Cutting Tools, Wixom, MI, USA), of AISI 316 L steel for biomedical purposes. Pareto sets and boundaries allow for choosing the most appropriate solution according to the specific conditions of the workshop where it is applied, minimizing the initial progression of tool wear and energy consumed, and maximizing productivity by guaranteeing the surface roughness values established for these types of parts according to the standard.

A semi-analytical model for predicting tool wear progression in drilling CFRP

In drilling carbon fiber reinforced polymer (CFRP), tool wear could significantly aggravate the drilling defects, and accurately predicting the tool wear progression is crucial for instructing tool change and preventing drilling defects in automatic production. The tool wear progression is usually modelled based on pure empirical methods, where the fundamental cutting interactions are neglected, and this is limited for applications. This paper established a semi-analytical model for tool wear progression prediction by combining the fundamental interaction analyses between tool and workpiece as the analytical part and regression analysis of three fitting parameters as the empirical part in drilling CFRP with a cemented carbide drill bit. The tool wear is featured and evaluated by the wear rate and the average half axis lengths based on tool wear characteristics. According to the cutting interactions between the drill bit and the CFRP workpiece, the wear rate is predicted based on the abrasive wear model and a previously proposed cutting force model. Combining the wear rate and wear geometry, the average half axis lengths are also predicted. The prediction errors of the wear rate and the average half axis lengths are less than 25% and 15%, respectively, with one test group for calibrating three coefficients. In addition, the effects of tool geometrical parameters on the tool wear progression are revealed for drilling CFRP.

Gaussian process regression model incorporated with tool wear mechanism

Cutting tool condition directly affects machining quality and efficiency. In order to avoid severely worn tools used during machining process and fully release the remaining useful life in the meanwhile, a reliable evaluation method of remaining useful life of cutting tools is quite necessary. Due to the variation of cutting conditions, it is a challenge to predict remaining useful life of cutting tools by a unified model. In order to address this issue, this paper proposes a method for predicting the remaining useful life of cutting tools in variable cutting conditions based on Gaussian process regression model incorporated with tool wear mechanism, where the predicted value at adjacent moments is constrained to a linear relationship by the covariance matrix of Gaussian model based on the assumption of progressive tool wear process, so the wear process under continuous changing conditions can be modelled. In addition to that, the input feature space and the output of the model are also enhanced by considering the tool wear mechanism for improving prediction accuracy. Machining experiments are performed to verify the proposed method, and the results show that the proposed could improve the prediction of tool remaining useful life significantly.

In-process stochastic tool wear identification and its application to the improved cutting force modeling of micro milling

Micro milling aims to manufacture miniature structures with high quality and complex features, and the stochastic time-varying tool wear is a crucial factor which has great influence on machining quality and efficiency of micro milling process. To improve the precision of machining and sustainability of micro cutting tools, the in-process tool wear conditions should be identified and updated ahead of time. In this work, an improved integrated estimation method is proposed based on the long short-term memory (LSTM) network and particle filter (PF) algorithm to predict the stochastic tool wear values. The integrated PF-LSTM identification methodology is developed to predict the in-process stochastic tool wear progression on the basis of the historical measurement data. With the estimation of in-process stochastic tool wear, the cutting force model is modified, in which the influence of tool run-out and the trochoidal trajectory of cutting edge are also considered. The proposed integrated estimation method of in-process stochastic tool wear and the modified cutting force model were validated by the micro milling experiments with workpiece material Al6061. It can be seen from the comparison results that the availability and sustainability of micro cutting tool have been improved, and the prediction accuracy also could be increased by 3.4% compared with that without considering the influence of tool wear.