Tool Life Estimation Research Articles

An Investigation of Factors Influencing Tool Life in the Metal Cutting Turning Process by Dimensional Analysis

This article uses dimensional analysis to formulate the tool life in the turning process of metal cutting for small-lot production by considering the impacts of the most important parameters. The estimation of tool life specifies process efficiency, machining productivity, resource consumption, machining time, and cost. Many parameters influence tool life on the real shop floor in small-lot production. This literature review studies 29 parameters affecting tool life directly or indirectly. The results of this research are represented as a graph-based analysis in the form of a web of interdependencies and a relationship matrix. The relationship matrix illustrates the direct and indirect interdependencies of the parameters which influence tool life in the turning process. The graph visualizes the weight of the parameters for the estimation of tool life in small-lot production. A cause-and-effect diagram is extracted from the relationship matrix to study the parameters affecting tool life in small-lot production. A dimensional analysis is executed based on the cause-and-effect diagram in order to calculate the tool life. The functions of tool life involve the cutting conditions, tool and workpiece hardness, cutting force, and cutting temperature. The dimensional analysis shows that the cutting speed, feed rate, and workpiece hardness are the most effective factors impacting tool life in the turning process.

Estimation of Tool Life by Industrial Method and Taylors Method Using Coated Carbide Insert in Turning of Work-Material Ss316l

In metal cutting process, selection of process parameters is important to enhance the machining performance and to improve the production rate. Tool life plays a vital role in deciding the overall productivity of manufacturing. In this paper, turning experiment is conducted to calculate the tool life of coated carbide insert in wet environment in a CNC lathe machine. The work material used was stainless steel SS316L. The tool life was calculated by industrial method that is by taking into account the number of jobs turned till it is declared fail, and then tool life is calculated by using theoretical method (Taylor’s tool life Equation). During the turning operation modes of tool failure were identified.

Machining condition-based stochastic modeling of cutting tool’s life

One of the major problems in the application of machining processes is the cutting tool life estimation. In this regard, different studies with various assumptions have been conducted to analyze tool wear characteristics under various cutting conditions to achieve different objectives. Traditional models for the analysis of tool life are mostly based on deterministic approaches, and the variations in cutting conditions are overlooked, and the tool life is not precisely matched with predicted values by these methods. In recent years, researchers have considered using the stochastic approach in forecasting tool life. Among them, Weibull distribution has special significance. One problem in using these approaches is the accurate estimation of tool’s life distribution functions based on the empirical information. In other words, although many researchers have considered Weibull an appropriate distribution for the cutting tool life modeling, however, the estimation of its parameters has certain inherent complexities. In this research, a hybrid methodology is presented to determine the parameters of the tool life distribution, by using the design of experiment (DOE) based on Box-Behnken design (BBD), total time on the test (TTT) transform, and golden section search (GSS). The estimation method of Weibull distribution parameters in this paper is compared with well-known techniques such as the least square method and maximum likelihood estimation. Finally, the proposed methodology was implemented in a case study, and the results were reported. The values of R2 for shape and scale parameters are 92.52% and 96.80%, respectively, which confirm the adequacy of the proposed methodology in the practical applications.

A Use Case to Implement Machine Learning for Life Time Prediction of Manufacturing Tools

Current machine learning techniques show a high degree of maturity and can be implemented for applications in manufacturing. In this context, using machine learning to investigate the relationship between process parameters and process performance allows an optimization of production systems. Conventional methods to analyze the life time of a manufacturing tool provide only a vague estimation of tool life. Therefore, this paper introduces an industrial use case for using machine learning to predict individual cutting tool life times. Hence, the life time of every individual manufacturing tool can be realized more accurately and its operation time can be maximized.

Machinability investigation and sustainability assessment in FDHT with coated ceramic tool

The paper addresses contribution to the modeling and optimization of major machinability parameters (cutting force, surface roughness, and tool wear) in finish dry hard turning (FDHT) for machinability evaluation of hardened AISI grade die steel D3 with PVD-TiN coated (Al2O3–TiCN) mixed ceramic tool insert. The turning trials are performed based on Taguchi\'s L18 orthogonal array design of experiments for the development of regression model as well as adequate model prediction by considering tool approach angle, nose radius, cutting speed, feed rate, and depth of cut as major machining parameters. The models or correlations are developed by employing multiple regression analysis (MRA). In addition, statistical technique (response surface methodology) followed by computational approaches (genetic algorithm and particle swarm optimization) have been employed for multiple response optimization. Thereafter, the effectiveness of proposed three (RSM, GA, PSO) optimization techniques are evaluated by confirmation test and subsequently the best optimization results have been used for estimation of energy consumption which includes savings of carbon footprint towards green machining and for tool life estimation followed by cost analysis to justify the economic feasibility of PVD-TiN coated Al2O3+TiCN mixed ceramic tool in FDHT operation. Finally, estimation of energy savings, economic analysis, and sustainability assessment are performed by employing carbon footprint analysis, Gilbert approach, and Pugh matrix, respectively. Novelty aspects, the present work: (i) contributes to practical industrial application of finish hard turning for the shaft and die makers to select the optimum cutting conditions in a range of hardness of 45-60 HRC, (ii) demonstrates the replacement of expensive, time-consuming conventional cylindrical grinding process and proposes the alternative of costlier CBN tool by utilizing ceramic tool in hard turning processes considering technological, economical and ecological aspects, which are helpful and efficient from industrial point of view, (iii) provides environment friendliness, cleaner production for machining of hardened steels, (iv) helps to improve the desirable machinability characteristics, and (v) serves as a knowledge for the development of a common language for sustainable manufacturing in both research field and industrial practice.

Machinability Investigation of HSLA Steel in Hard Turning with Coated Ceramic Tool: Assessment, Modeling, Optimization and Economic Aspects

The present study addresses the machinability investigation in finish dry hard turning of high strength low alloy steel with coated ceramic tool by considering cutting speed, feed and depth of cut as machining parameters. The technological parameters like surface roughness, flank wear, chip morphology and economical feasibility have been considered to investigate the machinability performances. Twenty seven set of trials according to full factorial design of experiments are performed and analysis of variance, multiple regression method, Taguchi method, desirability function approach and finally Gilbert’s approach are subsequently applied for parametric influence study, mathematical modeling, multi-response optimization, tool life estimation and economic analysis. Results indicated that feed and cutting speed are the most significant controlled as well as dominant factors for hard turning operation if the minimization of the machined surface roughness and tool flank wear is considered. Abrasions, adhesion followed by plastic deformation have been observed to be the principal wear mechanism for tool life estimation and observed tool life for coated ceramic insert is 47[Formula: see text]min under optimum cutting conditions. The total machining cost per part is ensued to be lower ($0.29 only) as a consequence of higher tool life, reduction in downtime and enhancement in savings, which finds economical benefits in hard turning. The current work demonstrates the substitution of conventional, expensive and slow cylindrical grinding process, and proposes the most expensive CBN tool alternative using coated ceramic tools in hard turning process considering techno-economical and ecological aspects.

Estimation of tool life and cutting burr in high speed milling of the compacted graphite iron by DE based adaptive neuro-fuzzy inference system

Abstract. The studies of tool life and formation of cutting burrs in roughing machining field are core issues in high speed milling of compacted graphite iron (CGI). Changing any one of the cutting parameters like cutting speed or feed rate can result in varied tool life and different height of the cutting burrs. In this work in order to study the relationship between cutting parameters and tool life and height of the cutting burrs, a new differential evolution algorithm based on adaptive neuro fuzzy inference system (DE-ANFIS) as a multi-input and multi-output (MIMO) prediction model is introduced to estimate the tool life and height of the cutting burrs. In this model, the inputs are cutting speed, feed rate and exit angle, and the outputs are tool life and height of the cutting burrs. There are 12 fuzzy rules in DE-ANFIS architecture. Gaussian membership function is adopted during the training process of the DE-ANFIS. The proposed DE-ANFIS model has been compared with PSO-ANFIS, Artificial Neural Network (ANN) and Support Vector Machines (SVM) models. To construct the predictive models, 25 cutting data were obtained through the experiments. Compared with PSO-ANFIS, ANN and SVM models, the results indicate that DE-ANFIS can provide a better prediction accuracy of tool life and height of the cutting burrs, and achieve the required product and productivity. Finally, the analysis of variance (ANOVA) shows that the cutting speed and feed rate have the most effects on the tool life and height of cutting burrs, respectively.

Hard Turning of HSLA Steel with Coated Ceramic Inserts: An Assessment, Modelling, Optimisation and Cost Analysis

The present study addresses the machinability investigation in finish dry hard turning of high strength low alloy (HSLA) steel with coated ceramic inserts by considering cutting speed, feed and depth of cut as machining parameters. The technological parameters like surface roughness, flank wear, and economic analysis have been considered to investigate machinability performances. Twenty-seven set of trials according to full factorial design of experiments are conducted, and subsequently, analysis of variance, multiple regression method, Taguchi method, desirability function approach and finally Gilbert’s technique is employed for parametric influence study, mathematical modelling, multi-response optimisation, tool life estimation and economic analysis. Novelty aspects, the current work demonstrates the substitution of conventional, expensive and slow cylindrical grinding process, and proposes the most expensive CBN tool alternative using coated ceramic tools in hard turning process considering techno-economic and ecological aspects, which they are useful and efficient from the industrial point of views.

Comparative study on machinability improvement in hard turning using coated and uncoated carbide inserts: part II modeling, multi-response optimization, tool life, and economic aspects

The present study focused on mathematical modeling, multi response optimization, tool life, and economical analysis in finish hard turning of AISI D2 steel ((55 ± 1) HRC) using CVD-coated carbide (TiN/TiCN/Al2O3) and uncoated carbide inserts under dry environmental conditions. Regression methodology and the grey relational approach were implemented for modeling and multi-response optimization, respectively. Comparative economic statistics were carried out for both inserts, and the adequacy of the correlation model was verified. The experimental and predicted values for all responses were very close to each other, implying the significance of the model and indicating that the correlation coefficients were close to unity. The optimal parametric combinations for Al2O3 coated carbide were d1–f1–v2 (depth of cut = 0.1 mm, feed = 0.04 mm/r and cutting speed = 108 m/min), and those for the uncoated tool were d1–(0.1 mm)–f1 (0.04 mm/r)–v1 (63 m/min). The observed tool life for the coated carbide insert was 15 times higher than that for the uncoated carbide insert, considering flank wear criteria of 0.3 mm. The chip volume after machining for the coated carbide insert was 26.14 times higher than that of the uncoated carbide insert and could be better utilized for higher material removal rate. Abrasion, diffusion, notching, chipping, and built-up edge have been observed to be the principal wear mechanisms for tool life estimation. Use of the coated carbide tool reduced machining costs by about 3.55 times compared to the use of the uncoated carbide insert, and provided economic benefits in hard turning.

Inner polishing of small diameter copper pipe using wire polishing tool and estimation of tool life

Inner polishing of small diameter copper pipe using wire polishing tool and estimation of tool life

Experimental investigation, modelling and optimization in hard turning of high strength low alloy steel (AISI 4340)

The present study addresses the machinability investigation of high strength low alloy grade AISI 4340 steel with coated ceramic tools on surface roughness, tool wear, and economic analysis by considering the hard turning process parameters such as cutting speed, feed and depth of cut. Twenty seven set of trials according to full factorial design of experiments are conducted. Analysis of variance, multiple regression method, Taguchi method, desirability function approach and Gilbert’s technique are employed for parametric influence study, predictive modelling, response optimization, tool life estimation followed by cost analysis. Results indicated that feed and cutting speed are the most significant and crucial factors for hard turning operation in order to achieve minimum surface roughness of machined component as well as flank wear of cutting tool. Abrasions, adhesion followed by plastic deformation are the key wear mechanisms of coated ceramic insert, resulted 47 min of tool life under optimum cutting conditions and ensued lower total machining cost per part ($ 0.29 only) due to higher tool life, and reduced downtime that justifies cost effectiveness of hard turning. Novelty aspects, the current research work demonstrates the substitution of conventional, expensive and slow cylindrical grinding process, and proposes the most expensive CBN tool alternative using coated ceramic tools in hard turning process from techno-economical and ecological point of views in line with the industrial requirements.

A multi-sensor based online tool condition monitoring system for milling process

Tool condition monitoring has been considered as one of the key enabling technologies for manufacturing optimization. Due to the high cost and limited system openness, the relevant developed systems have not been widely adopted by industries, especially Small and Medium-sized Enterprises. In this research, a cost-effective, wireless communication enabled, multi-sensor based tool condition monitoring system has been developed. Various sensor data, such as vibration, cutting force and power data, as well as actual machining parameters, have been collected to support efficient tool condition monitoring and life estimation. The effectiveness of the developed system has been validated via machining cases. The system can be extended to wide manufacturing applications.

Predicting tool life in turning operations using neural networks and image processing

Abstract A two-step method is presented for the automatic prediction of tool life in turning operations. First, experimental data are collected for three cutting edges under the same constant processing conditions. In these experiments, the parameter of tool wear, VB, is measured with conventional methods and the same parameter is estimated using Neural Wear, a customized software package that combines flank wear image recognition and Artificial Neural Networks (ANNs). Second, an ANN model of tool life is trained with the data collected from the first two cutting edges and the subsequent model is evaluated on two different subsets for the third cutting edge: the first subset is obtained from the direct measurement of tool wear and the second is obtained from the Neural Wear software that estimates tool wear using edge images. Although the complete-automated solution, Neural Wear software for tool wear recognition plus the ANN model of tool life prediction, presented a slightly higher error than the direct measurements, it was within the same range and can meet all industrial requirements. These results confirm that the combination of image recognition software and ANN modelling could potentially be developed into a useful industrial tool for low-cost estimation of tool life in turning operations.

Surface Roughness Analysis for Economical Feasibility Study of Coated Ceramic Tool in Hard Turning Operation

The paper addresses the assessment, modeling, and optimization study of surface roughness in finish dry hard turning (FDHT) of AISI 4340 steel with coated ceramic tool by considering the cutting speed, axial feed, depth of cut, and nose radius as machining parameters. Thirty sets of longitudinal turning trials based on central composite (CCD) design of experiments (DOEs) are performed, and response surface methodology (RSM), particle swarm optimization (PSO), and finally, Gilbert’s approach are subsequently applied for mathematical modeling, response optimization, tool life estimation, and economic analysis. Additionally, various diagnostic tests have been executed to check the statistical significance and validity, adequacy, effectiveness, and fitness of data of the proposed model using analysis of variance (ANOVA) and Anderson-Darling normal probability test. Results indicated that nose radius and feed are the most significant controlled as well as dominant factors for hard turning operation if the minimization of the machined surface roughness is considered. The RSM model combined with PSO technique leads to minimum surface roughness value similar to cylindrical grinding of 0.2021 μm, corresponding to optimum process parameters: 220 m/min of cutting speed, 0.05 mm/rev of feed, 0.193 mm of depth of cut, and 1.6 mm of tool nose radius. Finally, under pre-cited optimum machining conditions, tool life was evaluated to perform cost analysis for the economical justification and feasibility of coated ceramic tools in hard turning. The total machining cost per piece is ensued to be lower ($0.34) as a consequence of higher tool life (44 min), reduction in downtime, and enhancement in savings, which finds economical benefits in hard turning. The novelty aspects of the present work are that (i) it demonstrates the replacement of expensive, time-consuming conventional cylindrical grinding process and proposes the alternative of costlier CBN tool by utilizing coated ceramic tool in hard turning processes considering technological, economical, and ecological aspects, which are helpful and efficient from industrial point of view and (ii) it contributes to practical industrial application of finish hard turning for the shaft and die makers to select the optimum cutting conditions in a range of hardness of 45–55 HRC.

Energy Usage Analysis of Carbide End Mills on AISI 1045 Steel

Machining and manufacturing processes have a strong influence on industry development and economic growth. Many important factors have been studied in order to optimize these processes. Over the last few years energy and power have been used as parameters to verify the efficiency of these processes. Monitoring the state of energy consumption may serve as an indicator of the manufacturing process behavior. The main challenge is to translate the energy usage into a practical indicator, since monitoring energy presents a challenge due to the dynamic characteristics of the machines and the diversity of associated factors interacting with each other during the machining processes. Tool wear and tool life expectancy are other important issue that have a significant impact on both, cost and efficiency, especially, in computer assisted processes. Although it has been continuously studied over many years, until now, monitoring the state of tool wear has remained a complex task. Thus, in this study, a specific type of carbide end mill is analyzed using, a potential energy approach. The energy limit for a cutting tool with specific physical and chemical features will be estimated since it can simplify the tool life estimation and the tool change timing prediction. A series of experiments are performed with carbide end mills on AISI 1045 steel, periodically measuring the tool wear and the required energy until the end of tool life, to analyze if a cutting tool could transmits a specific amount of energy to the work piece material before reaching the end of its useful life.

Tool Life Prediction Model Based on GA-BP Neural Network

In order to improve the working efficiency of a manufacturing system, tool life estimation is very essential. In this paper, the dominant factors affecting tool life are analyzed by theoretical analysis. According to the nonlinear relationship between affecting factors and tool life, a tool life prediction model based on BP neural network, which is optimized by genetic algorithm (GA), is built up. 15 network patterns are trained to get the best network structure. The accuracy of GA-BP model is verified through computing and compared with the standard BP model. The results show that GA-BP model prediction value is exactly closed to the expected value of tool life and the prediction accuracy can be improved more than 5% compared than the standard BP model. The model is proved to be accuracy and it can be used as an effective method of tool selection decision.

Experimental Investigation to Optimize Tool Life and Surface Roughness in Inconel 718 Machining

In addition to the cutting conditions, the surface quality is also affected significantly by a worn tool in machining processes. Identification of the desirable tool life so that the surface quality is maintained within a desirable level is an essential task, especially in the machining of hard materials. In this paper, an optimal tool life and surface quality were identified in the turning operation of Inconel 718 Superalloy by means of experimental investigations and intelligent methods. First, the effect of machining time (MT) at the different cutting parameters was widely investigated on the surface roughness using the neural network model. Then, the modified Non-dominated Sorting Genetic Algorithm (NSGA) was implemented to optimize tool life and surface roughness. For this purpose, a new approach was implemented and the MT was taken into account as the input and output parameters during the optimization. Finally, the results of optimization were classified and the suitable states of the machining outputs were found. The results indicate that the implemented strategy in this paper provides an efficient approach to determine a desirable criterion for tool life estimation in machining processes.

Tool life estimation based on acoustic emission monitoring in end-milling of H13 mould-steel

Developing a reliable monitoring system is essential to create an autonomous manufacturing industry for increased productivity. The tool life of a machine tool is a major key parameter in accessing process quality control for developing an automated system. Various machining parameters are known to have different effects on the tool life criterion. Thus, it is essential to estimate the correlation of these parameters on tool life. The aim of this research is geared at estimating the tool life criterion from the effects of machining parameters and monitors the high-speed end-milling process of H13 tool with coated carbide inserts using highly correlated AE features. Furthermore, it proposes a diagnostic scheme using a multi-sensor approach for categorising the state of the tool. This scheme uses feature components extracted via statistical means and wavelet transform to serve as inputs for a neural network. The results found that increased speed decreased tool life and feed rate possesses a negative correlation to wear.

Analysis of adhered contacts and boundary conditions of the secondary shear zone

Prediction of the tribological parameters controlling the tool wear is one of the most complex research axes in the metal cutting literature. The nature of the friction on the tool–chip interface is the main process which influences the distribution of stresses and temperatures which in turn activates the thermomechanical process governing tool wear. Under extreme conditions of temperature, strain rates and pressure, occurring especially in high speed machining, the adhered contact phenomenon is highly localized especially near to the tool tip and extremely non-linear due to strong influence of the secondary shear zone (SSZ) and the nature of bonds between asperities of tool and chip. In addition, the analysis based on post-mortem examinations of chips and worn tools, especially in hard metal cutting alloy and high speed machining, show that the adhered friction with intimate contact and no relative motion of the material chip on the interface, are the principal cause of appearance of the plastic deformation layers (SSZ). This localized shear zone plays a role of intensive heat sources interacting with the tool side and, in turn, activates diffusive and abrasive wear. In this work, a hybrid model combining analytical and numerical approaches is performed in order to solve the non-linear thermomechanical problem on the chip and predicts the nature of friction contact, i.e. fully sliding, sticking/sliding or fully sticking contact, and these for a given distribution of asperities, characterizing the ratio between the real and apparent contact areas Ar/An on the interface, and a given global friction coefficient μ¯, characterizing the ratio of the experimental cutting forces. The shear stress generated in the primary shear band, the energy produced in the secondary shear zone, the local friction coefficient and the friction energy produced on the sliding part of contact, the proportion of the sticking/sliding interfaces, and geometrical parameters of the chip, are obtained by analytical means. The analysis of this model is based on experimental data and applied for a large cutting speed (1ms−1≤V≤60ms−1) and able to give some correlation about the distribution of adhesions marks on the contact with respect to the distribution of the tribological parameters at the interface. This model can be used in improving tool wear prediction and the estimation of tool-life.

Tool life modeling in high speed turning of AISI 4340 hardened steel with mixed ceramic tools by using face central cubic design

Tool life estimation for the cutting tool before the machining process is important due to economic and quality consideration. Thus, developing a model that can predict the tool life with high accuracy is an important issue. This paper deals with developing a new model of tool life for mixed ceramic tools in turning hardened steel AISI 4340 based on experimental tests. The experiments were planned and implemented using Central Composites Design (CCD) of Response Surface Methodology (RSM) with three input factors: cutting speed, feed rate and negative rake angle. The Face Central Cubic Design has been used as a special case of CCD. The analysis of variance (ANOVA) has been conducted to analyze the influence of process parameters and their interaction during machining. The first and second order models have been developed. It was found that the second order model provide higher accuracy prediction than the first order model. It was observed that the cutting speed is the most significant factor that influences the tool life for the two models, followed by the feed rate then the negative rake angle. The predicted values are confirmed by using validation experiments. Copyright © 2013 Praise Worthy Prize S.r.l. - All rights reserved.