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.

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.

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.

Development of a Smart Computer Numerical Control System

2013 ◽  
Author(s):  
Zhiqian Sang ◽  
Xun Xu
Keyword(s):  
Real Time ◽  
High Speed ◽  
Computer Numerical Control ◽  
Numerical Control ◽  
Machining Process ◽  
Cnc Machine ◽  
Field Bus ◽  
Nc System ◽  
Tool Paths ◽  
Cnc Controller

Traditional Computer Numerical Control (CNC) machines use ISO6983 (G/M code) for part programming. G/M code has a number of drawbacks and one of them is lack of interoperability. The Standard for the Exchange of Product for NC (STEP-NC) as a potential replacement for G/M code aims to provide a unified and interoperable data model for CNC. In a modern CNC machine tool, more and more motors, actuators and sensors are implemented and connected to the NC system, which leads to large quantity of data being transmitted. The real-time Ethernet field-bus is faster and more deterministic and can fulfill the requirement of data transmission in the high-speed and high-precision machining scenarios. It can provide more determinism on communication, openness, interoperability and reliability than a traditional field-bus. With a traditional CNC system using G/M code, when the machining is interrupted by incidents, restarting the machining process is time-consuming and highly experience-dependent. The proposed CNC controller can generate just-in-time tool paths for feature-based machining from a STEP-NC file. When machining stoppage occurs, the system can recover from stoppage incidents with minimum human intervention. This is done by generating new tool paths for the remaining machining process with or without the availability of the original cutting tool. The system uses a real-time Ethernet field-bus as the connection between the controller and the motors.

Application of Audible Sound Signals for Tool Wear Monitoring and Workpiece Hardness Identification in Gear Milling Using Machine Learning Techniques

2017 ◽  
Author(s):  
Achyuth Kothuru ◽  
Sai Prasad Nooka ◽  
Patricia Iglesias Victoria ◽  
Rui Liu
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Learning Algorithm ◽  
Machining Process ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Process Conditions ◽  
Tool Wear Monitoring ◽  
Audible Sound ◽  
Wear Monitoring

The machining process monitoring, especially the tool wear monitoring, is very critical in modern automated gear machining environment which needs instant detection of cutting tool state and/or process conditions, quick final diagnosis and appropriate actions. It has been realized that the non-uniform hardness of the workpiece material due to the improper heat treatment can cause expedited tool wear and unexpected tool breakage, which greatly increases difficulties and complexities in monitoring the tool conditions in gear cutting. This paper provides a solution to detect the wear conditions of the gear milling cutter in the cutting of workpiece materials with hardness variations using the audible sound signals. In this study, cutting tools and workpieces are prepared to have different flank wear classes and hardness variations respectively. A series of gear milling experiments are operated with a broad range of cutting conditions to collect sound signals. A machine learning algorithm that incorporates support vector machine (SVM) approach coupled with the application of time and frequency domain analysis is developed to correlate observed sound signals’ signatures to specified tool wear classes and workpiece hardness levels. The performance evaluation results of the proposed monitoring system have shown accurate predictions in detecting tool wear conditions and workpiece hardness variations from the sound signals in gear milling.

scholarly journals Parametric analysis of dry machining process using a novel integrated multi-attribute decision making approach

2022 ◽  
Vol 11 (2) ◽  
pp. 193-202
Author(s):  
G. Venkata Ajay Kumar ◽  
A. Ramaa ◽  
M. Shilpa
Keyword(s):  
Decision Making ◽  
Process Parameters ◽  
Machining Process ◽  
Machining Parameters ◽  
Machining Processes ◽  
Order Preference ◽  
Dry Conditions ◽  
Criteria Importance ◽  
The Right ◽  
Selection Of

In most of the machining processes, the complexity arises in the selection of the right process parameters, which influence the machining process and output responses such as machinability and surface roughness. In such situations, it is important to estimate the inter-relationships among the output responses. One such method, Decision-Making Trial and Evaluation Laboratory (DEMATEL) is applied to study the inter-relationships of the output responses. Estimation of proper weights is also crucial where the output responses are conflicting in nature. In the current study, DEMATEL technique is used for estimating the inter-relationships for output responses in machining of EN 24 alloy under dry conditions. CRiteria Importance Through Inter-criteria Correlation (CRITIC) method is used to estimate the weights and finally the optimal selection of machining parameters is carried out using Techniques for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The model developed guides the decision maker in selection of precise weights, estimation of the inter relationships among the responses and selection of optimal process parameters.

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.

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