scholarly journals Estimation of residual life of a cutting tool used in a machining process

2018 ◽  
Vol 192 ◽  
pp. 01017
Author(s):  
Sourath Ghosh ◽  
Sukanta Kumar Naskar ◽  
Nirmal Kumar Mandal
Keyword(s):  
Cutting Tool ◽  
Residual Life ◽  
Remaining Useful Life ◽  
Machining Process ◽  
Cutting Condition ◽  
Remaining Life ◽  
Tool Failure ◽  
Life Data ◽  
Useful Life ◽  
Good Agreement

A significant part of cost of machining is associated with non-optimum use of cutting tool. Moreover cutting tool failure is responsible for almost 20% of the machining downtime. Thus, having knowledge of residual life of cutting tool is highly recommended so as to maximise the availability time and reduce the machining cost. The aim of this work is to find out residual life of a worn cutting tool which has been used for turning of Ti-6Al-4V alloy under constant cutting condition. The lognormal distribution is used to model the cutting tool life data. Remaining useful life of cutting tool is estimated using Mean Remaining Life (MRL) function. The results obtained from model are compared with the experimental results and it shows good agreement.

scholarly journals Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model

2021 ◽  
Vol 11 (11) ◽  
pp. 5011
Author(s):  
Yuanxing Huang ◽  
Zhiyuan Lu ◽  
Wei Dai ◽  
Weifang Zhang ◽  
Bin Wang
Keyword(s):  
Surface Roughness ◽  
Failure Criterion ◽  
Cutting Tool ◽  
Cutting Tools ◽  
Remaining Useful Life ◽  
Model Parameters ◽  
Inverse Gaussian ◽  
Wear Process ◽  
Useful Life ◽  
Dynamic Wear

In manufacturing, cutting tools gradually wear out during the cutting process and decrease in cutting precision. A cutting tool has to be replaced if its degradation exceeds a certain threshold, which is determined by the required cutting precision. To effectively schedule production and maintenance actions, it is vital to model the wear process of cutting tools and predict their remaining useful life (RUL). However, it is difficult to determine the RUL of cutting tools with cutting precision as a failure criterion, as cutting precision is not directly measurable. This paper proposed a RUL prediction method for a cutting tool, developed based on a degradation model, with the roughness of the cutting surface as a failure criterion. The surface roughness was linked to the wearing process of a cutting tool through a random threshold, and accounts for the impact of the dynamic working environment and variable materials of working pieces. The wear process is modeled using a random-effects inverse Gaussian (IG) process. The degradation rate is assumed to be unit-specific, considering the dynamic wear mechanism and a heterogeneous population. To adaptively update the model parameters for online RUL prediction, an expectation–maximization (EM) algorithm has been developed. The proposed method is illustrated using an example study. The experiments were performed on specimens of 7109 aluminum alloy by milling in the normalized state. The results reveal that the proposed method effectively evaluates the RUL of cutting tools according to the specified surface roughness, therefore improving cutting quality and efficiency.

scholarly journals A Hybrid Remaining Useful Life Prediction Method for Cutting Tool Considering the Wear State

2022 ◽  
Author(s):  
Yifan Li ◽  
Yongyong Xiang ◽  
Baisong Pan ◽  
Luojie Shi
Keyword(s):  
Tool Wear ◽  
High Speed ◽  
Cutting Tool ◽  
Bayesian Framework ◽  
Remaining Useful Life ◽  
Physical Models ◽  
Data Driven ◽  
Support Vector ◽  
Wear Model ◽  
Useful Life

Abstract Accurate cutting tool remaining useful life (RUL) prediction is of significance to guarantee the cutting quality and minimize the production cost. Recently, physics-based and data-driven methods have been widely used in the tool RUL prediction. The physics-based approaches may not accurately describe the time-varying wear process due to a lack of knowledge for underlying physics and simplifications involved in physical models, while the data-driven methods may be easily affected by the quantity and quality of data. To overcome the drawbacks of these two approaches, a hybrid prognostics framework considering tool wear state is developed to achieve an accurate prediction. Firstly, the mapping relationship between the sensor signal and tool wear is established by support vector regression (SVR). Then, the tool wear statuses are recognized by support vector machine (SVM) and the results are put into a Bayesian framework as prior information. Thirdly, based on the constructed Bayesian framework, parameters of the tool wear model are updated iteratively by the sliding time window and particle filter algorithm. Finally, the tool wear state space and RUL can be predicted accordingly using the updating tool wear model. The validity of the proposed method is demonstrated by a high-speed machine tool experiment. The results show that the presented approach can effectively reduce the uncertainty of tool wear state estimation and improve the accuracy of RUL prediction.

Performance of Low Cost 3D Printed Minimum Quantity Lubrication Applicator Using Palm Oil in Milling Steel

2020 ◽  
Vol 997 ◽  
pp. 85-92
Author(s):  
Abang Mohammad Nizam Abang Kamaruddin ◽  
Abdullah Yassin ◽  
Shahrol Mohamaddan ◽  
Syaiful Anwar Rajaie ◽  
Muhammad Isyraf Mazlan ◽  
...  
Keyword(s):  
Tool Wear ◽  
Cutting Forces ◽  
Cutting Tool ◽  
Low Cost ◽  
Minimum Quantity Lubrication ◽  
Machining Process ◽  
Cutting Condition ◽  
Dry Cutting ◽  
Minimum Quantity ◽  
Tool Performance

One of the most significant factors in machining process or metal cutting is the cutting tool performance. The rapid wear rate of cutting tools and cutting forces expend due to high cutting temperature is a critical problem to be solved in high-speed machining process, milling. Near-dry machining such as minimum quantity lubrication (MQL) is regarded as one of the solutions to solve this problem. However, the function of MQL in milling process is still uncertain so far which prevents MQL from widely being utilized in this specific machining process. In this paper, the mechanism of cutting tool performance such as tool wear and cutting forces in MQL assisted milling is investigated more comprehensively and the results are compared in three different cutting conditions which is dry cutting, wet cutting (flooding) and MQL. The MQL applicator is constructed from a household grade low-cost 3D printing technique. The chips surface of chips formation in each cutting condition is also observed using Scanning Electron Microscopy (SEM) machine. It is found out that wet cutting (flooding) is the best cutting performance compare to MQL and dry cutting. However, it can also be said that wet cutting and MQL produced almost the same value of tool wear and cutting forces as there is negligible differences in average tool wear and cutting forces between them based on the experiment conducted.

Remaining Useful Life Prediction Based on Spindle Load and Cutting Process Parameters in Machining

2019 ◽  
Author(s):  
Ankur Krishna ◽  
Bilal Muhammed
Keyword(s):  
Tool Wear ◽  
Process Parameters ◽  
Remaining Useful Life ◽  
Machining Process ◽  
Shop Floor ◽  
Cnc Machine ◽  
Wear Monitoring ◽  
Useful Life ◽  
The Right ◽  
Cnc Controller

Abstract Tool wear increases machining resistance, part dimensional inaccuracy and machining vibration. Tool wear monitoring and Remaining Useful Life (RUL) prediction of the tool during machining operation will assist a machine operator to provide tool wear compensation at the right time and plan the tool change activity. These aspects become significantly important for economical and quality production. This work focuses on a physics and data-based approach for monitoring cutting tool wear state and Remaining Useful Life (RUL) during a machining operation by adapting a well-known empirical wear-rate equation. The constants in the model are estimated based on machine heuristics which depends on the tool-machine-workpiece combination. The proposed model takes real-time spindle power and machining process parameters as inputs, which are obtained directly through querying the CNC controller. Therefore, it does not require the mounting of any external sensors on the CNC machine tool. Hence, the proposed method is a more economical and convenient way to predict tool wear and RUL in a machining shop floor. The model is validated from experimental data and it can capture the progression of tool wear and RUL of the tool at any point of time during a machining operation. Since the model captures the physics of tool wear and machining heuristics, it is more robust than a purely data-based model.

Residual life prediction for ball bearings based on joint approximate diagonalization of eigen matrices and extreme learning machine

2015 ◽  
Vol 231 (9) ◽  
pp. 1699-1711 ◽  
Author(s):  
Fang Liu ◽  
Yongbin Liu ◽  
Fenglin Chen ◽  
Bing He
Keyword(s):  
Frequency Domain ◽  
Extreme Learning Machine ◽  
Life Prediction ◽  
Residual Life ◽  
Remaining Useful Life ◽  
Time Frequency ◽  
Fast Learning ◽  
Useful Life ◽  
Learning Machine ◽  
Degradation Indicator

Data-driven approaches have been proved effective for remaining useful life estimation of key components (bearings for example) in rotating machinery. In such approaches, it is important to determine an appropriate degradation indicator from the collected run-to-failure life cycle data. In this paper, a new degradation indicator is introduced based on the joint approximate diagonalization of eigen matrices algorithm. First, a matrix consisting of time domain, frequency domain, and time–frequency domain features extracted from the collected data instances is created. Then a two-layer joint approximate diagonalization of eigen matrices is introduced to transform the matrix to the advanced features (a vector) that represents the behavior of the bearing’s degradation. As an independent component analysis method, the designed two-layer joint approximate diagonalization of eigen matrices is able to eliminate the redundancy of the directly extracted features. Further, the obtained vector is input into an extreme learning machine to train a remaining useful life prediction model. Finally, a set of experimental cases are utilized to verify the presented method. Results show that the two-layer joint approximate diagonalization of eigen matrices is capable of exploring features that reflects the trend of bearing’s degradation state much better. And due to the easy parameter configuration and fast learning speed, the extreme learning machine is capable of training a model that can effectively predict the remaining useful life of the bearings.

Tool wear condition monitoring based on wavelet transform and improved extreme learning machine

2019 ◽  
Vol 234 (5) ◽  
pp. 1057-1068 ◽  
Author(s):  
Soufiane Laddada ◽  
Med. Ouali Si-Chaib ◽  
Tarak Benkedjouh ◽  
Redouane Drai
Keyword(s):  
Wavelet Transform ◽  
Tool Wear ◽  
Extreme Learning Machine ◽  
Cutting Tools ◽  
Remaining Useful Life ◽  
Machining Process ◽  
Non Linear ◽  
Useful Life ◽  
Learning Machine ◽  
Wear Condition

In machining process, tool wear is an inevitable consequence which progresses rapidly leading to a catastrophic failure of the system and accidents. Moreover, machinery failure has become more costly and has undesirable consequences on the availability and the productivity. Consequently, developing a robust approach for monitoring tool wear condition is needed to get accurate product dimensions with high quality surface and reduced stopping time of machines. Prognostics and health management has become one of the most challenging aspects for monitoring the wear condition of cutting tools. This study focuses on the evaluation of the current health condition of cutting tools and the prediction of its remaining useful life. Indeed, the proposed method consists of the integration of complex continuous wavelet transform (CCWT) and improved extreme learning machine (IELM). In the proposed IELM, the hidden layer output matrix is given by inverting the Moore–Penrose generalized inverse. After the decomposition of the acoustic emission signals using CCWT, the nodes energy of coefficients have been taken as relevant features which are then used as inputs in IELM. The principal idea is that a non-linear regression in a feature space of high dimension is involved by the extreme learning machine to map the input data via a non-linear function for generating the degradation model. Then, the health indicator is obtained through the exploitation of the derived model which is in turn used to estimate the remaining useful life. The method was carried out on data of the real world collected during various cuts of a computer numerical controlled tool.

Accrued Damage and Remaining Life in Field Extracted Assemblies Under Sequential Thermo-Mechanical Loads

2011 ◽  
Author(s):  
Pradeep Lall ◽  
Mahendra Harsha ◽  
Jeff Suhling ◽  
Kai Goebel ◽  
Jim Jones
Keyword(s):  
Thermal Aging ◽  
Residual Life ◽  
Remaining Useful Life ◽  
Relative Accuracy ◽  
Percentage Error ◽  
Valuable Insight ◽  
Sample Standard Deviation ◽  
Damage Initiation ◽  
Useful Life ◽  
Insight Into

Field deployed electronics may accrue damage due to environmental exposure and usage after finite period of service but may not often have any macro-indicators of failure such as cracks or delamination. A method to interrogate the damage state of field deployed electronics in the pre-failure space may allow insight into the damage initiation, progression, and remaining useful life of the deployed system. Aging has been previously shown to effect the reliability and constitutive behavior of second-level leadfree interconnects. Prognostication of accrued damage and assessment of residual life can provide valuable insight into impending failure. In this paper, field deployed parts have been extracted and prognosticated for accrued damage and remaining useful life in an anticipated future deployment environment. A subset of the field deployed parts have been tested to failure in the anticipated field deployed environment to validate the assessment of remaining useful life. In addition, some parts have been subjected to additional know thermo-mechanical stresses and the incremental damage accrued validated with respect to the amount of additional damage imposed on the assemblies. The presented methodology uses leading indicators of failure based on micro-structural evolution of damage to identify accrued damage in electronic systems subjected to sequential stresses of thermal aging and thermal cycling. Damage equivalency methodologies have been developed to map damage accrued in thermal aging to the reduction in thermo-mechanical cyclic life based on damage proxies. The expected error with interrogation of system state and assessment of residual life has been quantified. Prognostic metrics including α-λ metric, sample standard deviation, mean square error, mean absolute percentage error, average bias, relative accuracy, and cumulative relative accuracy have been used to compare the performance of the damage proxies.

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