Tool replacement policy for probabilistic tool life and random wear process

This study presents a tool replacement model which determines the optimal initial tool setting, tool adjustment cycle and tool replacement time with regard to quality loss, adjustment cost, replacement cost and penalty cost for possible tool failure. A quadratic loss function is employed to characterize the costs representing part quality. The Bernstein probability density function (BDF) is applied to describe the usual characteristics of tool life and the tool wear process. Then, based on the considered costs, the best strategy for tool replacement can be determined. A possible application of the model is given that provides a method of off‐line tool management in a production environment. Copyright © 1999 John Wiley & Sons, Ltd.     

Joint optimisation of production rate and preventive maintenance in machining systems

This paper studies an integrated control strategy of production and maintenance for a machining system which produces a single type of product to meet the constant demand. Different from previous research, we assume in this study that during the production, the production rate not only influences the life of cutting tool, but also the reliability of the machine. Both the replacement of cutting tool and the preventive maintenance (PM) of machine are considered in this paper. The machine is preventively maintained at the Nth tool replacement or correctively repaired at the machine failure, whichever occurs first. PM and corrective repair may cause shortage which can be reduced by controlling inventory. There are two decision variables p and N, where p denotes the production rate and N denotes the number of cutting tool replacement before the PM is performed. An integrated model is developed to simultaneously determine the optimal production rate and PM policy that minimise the total expected cost per unit item produced. Finally, an illustrative example and sensitivity analysis are given to demonstrate the proposed model.     

Scheduling of the optimal tool replacement times in a flexible manufacturing system

In Flexible Manufacturing Systems (FMSs). a cutting tool is frequently used for different operations and on different part types to minimize tool change-overs and the number of tools required, and to increase part-routing flexibility. In such situations, the tools become shared resources and work in job-dependent, changeable and nonhomogeneous conditions. It is well known that the tool failure rate depends on both age and machining conditions and that tool reliability is a function of the duration, machining conditions, and the sequence of the operations in FMS. The objective of this paper is to obtain a schedule of the optimal preventive replacement times for the cutting tools over a finite time horizon in a flexible manufacturing system. We assume that the tool will be replaced either upon failure during an operation or preventively after the completion of each operation, incurring different replacement costs. A standard stochastic dynamic programming approach is taken to obtain the optimal tool replacement times. The optimal schedule is obtained by minimizing the total expected cost over a finite time horizon for a given sequence of operations. A computational algorithm is developed and a numerical example is given to demonstrate the procedure.     

Optimal cutting tool replacement based on tool wear status

In this paper, we develop an optimal tool replacement model based on the actual tool wear status. In this model, we consider separately the probability distribution of the tool capacity obtained under testing conditions and the tool capacity consumption by the workpieces. Special cases with exponential distribution for tool capacity consumption and in which one tool can process many workpieces are solved.     

Simultaneous determination of optimal machining conditions and tool replacement policies in constrained machining economics problems by geometric programming

This paper presents an analytical model for the simultaneous determination of the optimal machining conditions (cutting speed and feed) and the optimal tool replacement policy in constrained machining economics problems by geometric programming. The optimal preventive tool replacement policy is initially determined as a tool life fractile (independent of the underlying tool life distribution) and then it is expressed as actual tool life by utilizing the underlying tool life distribution applicable to the combination of tool material, workpiece properties, and machining conditions. Constraints on the optimal values of cutting speeds, feeds, and/or optimal tool replacement policy based on maximum allowable values and/or surface finish requirements are handled through the optimization of the dual objective function. It is shown that the optimal cost distribution does not depend on the cost coefficients in the objective function. Finally, the model is applied to two-stage systems where the necessary conditions are derived for increasing the synchronization between the two stages.     

Optimal machining conditions with costs of quality and tool maintenance for turning

Although there is a voluminous literature on machine tool economics, the cost of machining quality has received little attention. In this paper, we develop a timedynamic economic model for single-pass turning. The model incorporates considerations on the stochastic nature of tool-life and such tool maintenance activities as tool replacement and tool regrinding. We model the quality cost of tool-cutting in terms of deviation from target roughness and deviation from target dimension. The cost of deviation is either the Taguchi type under continuous assumption or in terms of the cost to the entire workpiece under discrete assumption. The connection between quality cost and tool maintenance cost is explicitly addressed. Essentially, quality cost as well as machining cost is a function of two sets of decisions: machining conditions as defined by the choice of cutting speed and feed rate (depth of cut is a constant in single-pass operations), and the condition of the cutting tool as defined by the tool retirement and regrinding policy. The cost of tool failure is also incorporated.     

Robust optimisation of machining conditions with tool life and surface roughness uncertainties

The economics of the multi-pass turning problem is considered, while accounting for tool life uncertainty. The goal is to minimise the expected production cost per part, given the probability distribution for tool life, and with machining parameters being subject to practical constraints. The cost function accounts for machining cost, idling cost, tool changing cost as well as the cost associated with tool failure. A modified version of the particle swarm optimisation (PSO) algorithm, called the dynamic objective PSO (or DOPSO), is used for minimisation of the cost function. The decision variables include not only the machining parameters but also the tool replacement time. The equality constraint that the total desired depth of cut be achieved by an integral number of roughing passes and a single finishing pass is handled in a novel way, and together with including tool replacement time as a decision variable, this leads to lower costs than those cited by other comparable previous works. To handle uncertain constraints that lead to part failure when violated (e.g. desired surface finish), a robust formulation is also suggested through similar incorporation in the cost function, as for tool failure.     

Differential evolution-based feature selection and parameter optimisation for extreme learning machine in tool wear estimation

Cutting tool wear degrades the product quality in manufacturing processes. Hence, real-time online estimation of tool wear is important for suggesting a tool replacement before the wear limit is reached, in order to protect the workpiece and the CNC machine from damage and breakdown. In this study, using both statistical features and wavelet features extracted from sensor signals, an adaptive evolutionary extreme learning machine (ELM) learning paradigm is developed for tool wear estimation in high-speed milling process. In the proposed method, a discrete differential evolution (DE) algorithm is used to select input features for the ELM, and a continuous DE algorithm is used for parameter optimisation of the mixed kernel function for the ELM. The experimental results indicate that the proposed adaptive evolutionary ELM-based tool wear estimation model can effectively estimate the tool wear in high-speed milling process. Empirical comparisons show that the proposed model performs better than existing approaches in estimating the tool wear.     

Acoustic emission analysis for tool wear monitoring in face milling

Monitoring the condition of the cutting tool in any machining operation is very important since it will affect the workpiece quality and an unexpected tool failure may damage the tool, workpiece and sometimes the machine tool itself. Advanced manufacturing demands an optimal machining process. Many problems that affect optimization are related to the diminished machine performance caused by worn out tools. One of the most promising tool monitoring techniques is based on the analysis of Acoustic Emission (AE) signals. The generation of the AE signals directly in the cutting zone makes them very sensitive to changes in the cutting process. Various approaches have been taken to monitor progressive tool wear, tool breakage, failure and chip segmentation while supervising these AE signals. In this paper, AE analysis is applied for tool wear monitoring in face milling operations. Experiments have been conducted on En-8 steel using uncoated carbide inserts in the cutter. The studies have been carried out with one, two and three inserts in the cutter under given cutting conditions. The AE signal analysis was carried out by considering signal parameters such as ring down count and RMS voltage. The results show that AE can be effectively used to monitor tool wear in face milling operation.     

Prediction and optimisation models for turning operations

Selection of process parameters has very significant impact on product quality, production costs and production times. The quality and cost are much related to tool life, surface roughness and cutting forces which they are functions of process parameters (cutting speed, feed rate, depth of cut and tool nose radius). In this paper, empirical models for tool life, surface roughness and cutting force are developed for turning operations. The process parameters (cutting speed, feed rate, depth of cut and tool nose radius) are used as inputs to the developed machineability models. Two data mining techniques are used; response surface methodology and neural networks. The data of 28 experiments have been used to generate, compare and evaluate the proposed models of tool life, cutting force and surface roughness for the selected tool/material combination. The resulting models are utilized to formulate an optimisation model and solved to find optimal process parameters, when the objective is minimising production cost per workpiece, taking into account the related boundaries and limitation of this multi-pass turning operations. Numerical examples are given to demonstrate the suggested optimisation models.     

铝基碳化硅精密铣削刀具磨损实验研究

针对铝基碳化硅切削加工中刀具易磨损、寿命低、切削难度大和加工成本高等问题,选用不同材料的硬质合金铣刀及金刚石铣刀进行切削加工实验,并利用扫描电镜和工具显微镜对高体积分数铝基碳化硅铣削时刀具磨损形态进行了分析研究.研究表明:硬质合金刀具前刀面和刃口磨损主要形式为粘结磨损和微崩刃,后刀面磨损主要为刻划磨损,而金刚石铣刀加工时刀具磨损很小;YG6X铣刀材料微观组织致密,抗磨损能力较强,宜粗加工时选用;金刚石刀体的硬度远大于SiC颗粒,且金刚石与工件的摩擦系数小,金刚石铣刀寿命远大于硬质合金铣刀,宜精加工时选用.     

Optimal machining conditions and buffer space size for the two-stage case

This paper presents a model for calculating optimal cutting speeds and tool replacement policies for both operations of a two-stage machining problem when the unit cost is minimized or the profit rate is maximized. The tool life is assumed to be a stochastic variable and penalty costs are imposed for tool failures during production. The optimal size of buffer space between the two machines is also calculated analytically. It is shown that the unit cost increases as the tool variability and/or the penalty cost increase. The cutting speeds and tool replacement policies on both operations depend strongly on the tool variability and the penalty cost. The cutting speeds differ from those determined independently for each operation. Finally, the optimal buffer space size is the one necessary to keep the critical machine running when there is a tool change on the non-critical machine, and its optimal size can be calculated analytically.     

The efficiency range of economical cutting conditions and tool replacement under the age replacement strategy

This paper presents models for calculating the optimal cutting feed rate, spindle speed, and age of preventive tool replacement for a standalone cutting machine. The optimal cutting conditions are determined and analysed for three different objective functions: minimum expected cycle time, minimum expected cost per unit, and maximum expected profit-rate, under the Age Replacement Strategy (ARS) and assuming that the tool-life distribution function is Normal. We show that the first two objective functions are separable, and present an efficient, one-dimensional search procedure for the optimization. A definition of the efficiency ranges of feed rate and standard age of tool replacement is suggested for the ARS, which improves the efficiency range defined in the literature for the Failure Replacement Strategy (FRS).     

Optimizing tool selection

Selecting optimal cutting tools that can answer to the performance criteria of manufacturing economics (quality, productivity, cost, etc) is an important step in planning the manufacture of components. Achieving this, however, is difficult because of the many constraints involved in the tool selection process. This paper describes a method for determining a theoretical optimal combination of cutting tools given a set of 3D volumes or 2D profiles. Optimal tools are selected by considering residual material that is inaccessible to oversized cutters and the relative clearance rates of cutters that can access these regions of the selected machining features. The current implementation described does not give exact results because several machining parameters have been ignored during the selection process, such as tool path length, plunge rates, etc. However, the experimental studies carried out to verify the theoretical results suggest that while these factors may influence the absolute values calculated, in general, their influence on the relative ranking of the tools is insignificant. The results presented here suggest that the 'correct' combination of tools could significantly reduce machining times. Consequently, the paper concludes with a discussion of how modifications to typical tool path generation routines in commercial CAM systems could improve productivity.     

On-line tool condition monitoring in face milling using current and power signals

The vast majority of tool condition monitoring systems use the cutting force as the predictor signal. However, due to prohibitive cost to performance ratios and maintenance and operational problems, such methods are not favoured by industries. In this paper, a method for continuous on-line estimation of tool wear, based on the inexpensive spindle motor current and voltage measurements, is proposed for the complex and intermittent cutting face milling operation. Sensors for these signals are free from problems associated with the cutting forces and the vibration signals. Novel signal processing strategies have been proposed for on-line computation of useful features from the measured signals. Feature space filtering is introduced to obtain robust and improved predictors from the extracted features. A multiple linear regression model, built on the filtered features, is then used to estimate tool wear in real-time. Very accurate predictions are achieved for both laboratory and industrial experiments, surpassing earlier results using cutting forces and estimation methods based on complex methodologies such as artificial neural networks.     

Bayesian and regression approaches to on-line prediction of residual tool life

In this paper, two statistical approaches to on-line prediction of cutting tool life are presented and discussed. A Bayesian approach utilizes in-process information about the cutting tool state and constitutes a valuable basis for improved prediction. A second approach is based on the cutting forces and facilitates a prediction of the tool life with an uncertainty of 15% after 1.5–2.0 cutting minutes. Traditional tool condition monitoring can be improved by increased reliability of tool life predictions, increased utilization of the cutting tools together with reduced need for pre-process data and calibrating procedures. © 1998 John Wiley & Sons, Ltd.     

Life-cycle reliability-based robust design optimization for GP model with response uncertainty

Reliability-based robust design optimization (RBRDO) is a crucial tool for life-cycle quality improvement. Gaussian process (GP) model is an effective alternative modeling technique that is widely used in robust parameter design. However, there are few studies to deal with reliability-based design problems by using GP model. This article proposes a novel life-cycle RBRDO approach concerning response uncertainty under the framework of GP modeling technique. First, the hyperparameters of GP model are estimated by using the Gibbs sampling procedure. Second, the expected partial derivative expression is derived based on GP modeling technique. Moreover, a novel failure risk cost function is constructed to assess the life-cycle reliability. Then, the quality loss function and confidence interval are constructed by simulated outputs to evaluate the robustness of optimal settings and response uncertainty, respectively. Finally, an optimization model integrating failure risk cost function, quality loss function, and confidence interval analysis approach is constructed to find reasonable optimal input settings. Two case studies are given to illustrate the performance of the proposed approach. The results show that the proposed approach can make better trade-offs between the quality characteristics and reliability requirements by considering response uncertainty.     

Dynamic management of cutting tools for flexible and quality machining

Traditional economic tool-life models assume a homogeneous cutting environment, where a tool's continued service is irrespective of its condition. It is wellknown that the quality of a machining process is significantly impacted by a tool's wear-and-tear. To ensure good machining quality, tool assignment should consider the wear level of the tool as well as the type of machining job to be performed. This paper presents a dynamic management model for cutting tools that emphasizes the cost of machining quality. The model describes a heterogeneous environment typical of computerized manufacturing systems, where a tool carries out variable machining assignments during its life. The formulation is a stochastic dynamic programme, which determines optimal preventive actions based on a periodic evaluation of the tool's operating conditions. Tool deterioration is described as movement to different operating states (increasing levels of tool wear) and job assignment of tools is state-dependent. A tool's optimal economic life is also determined within the context of variable machining. The cost of quality-deviation is assessed using Taguchi's quality-loss function.     

Optimal tool replacement with asymmetric quadratic loss

In this paper, an analysis of a tool replacement problem with asymmetric quadratic loss function is presented. The expected average cost per unit time is derived and it is shown that the optimal initial setting and the optimal replacement time can be found by solving two nonlinear equations. The paper generalizes and improves on previous work in the area.     

Optimal buffer space size in two-stage machining systems with Markovian or non-Markovian tool life processes

This paper presents a model for obtaining the optimal buffer space size in two-stage machining systems. It is shown that the optimal buffer space size is the one necessary to keep the critical machine running even when there are tool failures and subsequent tool changes on the non-critical machine. An analytical expression for the optimal buffer space size is derived when tool failures follow a Markovian process and the objective function is the minimization of the unit cost. A simulation mode! is developed for obtaining the optimal buffer space size when tool failures follow a non-Markovian process. It is shown that the optimal buffer space size depends on the expected delay time per part due to tool failures on the non-critical machine (which depends on the probability of tool failure on the non-critical machine during machining on the critical machine), on the labour and the buffer space cost, and on the tool-change time. The model can be extended to cover the case of two or more identical parallel machines per stage and the case of multi-stage machining systems.