An adaptive procedure for tool life prediction in face milling

2011 ◽  
Vol 225 (11) ◽  
pp. 1130-1136 ◽  
Author(s):  
D D Zhang
Keyword(s):  
Tool Wear ◽  
Prediction Model ◽  
Tool Life ◽  
Life Prediction ◽  
Face Milling ◽  
Accurate Prediction ◽  
Machining Process ◽  
Model Parameters ◽  
Adaptive Procedure ◽  
Life Prediction Model

Accurate prediction of tool life is essential to guarantee surface quality and economics of cutting operations in face milling. This article presents a procedure for tool life prediction through in-process adaptation of tool wear rate based on indirect measures. The procedure effectively accounts for the uncertainty of tool wear progress owing to the complexity of the machining process. First, sensor fusion of spindle motor current AC and DC portions is taken to estimate the actual tool wear through relevance vector machine. Then, a tool life prediction model relating flank wear with cutting time is proposed for tracking the progress of tool wear under certain cutting settings. Further, a recursive least square algorithm is developed to update the parameters of the tool life prediction model by considering the error between the predicted tool wear and the estimated tool wear. Finally, the updated model capturing the uncertainty of tool wear progress is used to predict tool life in face milling. Tool life experiments validate that the adaptive procedure can quickly track the progress of tool wear, and make more accurate prediction of tool life compared with the procedure with constant model parameters.

scholarly journals Life Prediction Model of Mineral Admixture Cement Based-Materials under Early Age CO2-Erosion

Coatings ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 413
Author(s):  
Saisai Wang ◽  
Jian Chen ◽  
Xiaodong Wen
Keyword(s):  
Prediction Model ◽  
Life Prediction ◽  
Experimental Tests ◽  
Mineral Admixture ◽  
Model Parameters ◽  
Influence Coefficient ◽  
Test Pieces ◽  
Cement Based Materials ◽  
Particle Group ◽  
Life Prediction Model

Most of the existing models of structural life prediction in early carbonized environment are based on accelerated erosion after standard 28 days of cement-based materials, while cement-based materials in actual engineering are often exposed to air too early. These result in large predictions of the life expectancy of mineral-admixture cement-based materials under early CO2-erosion and affecting the safe use of structures. To this end, different types of mineral doped cement-based material test pieces are formed, and early CO2-erosion experimental tests are carried out. On the basis of the analysis of the existing model, the influence coefficient of CO2-erosion of the mineral admixture Km is introduced, the relevant function is given, and the life prediction model of the mineral admixture cement-based material under the early CO2-erosion is established and the model parameters are determined by using the particle group algorithm (PSO). It has good engineering applicability and guiding significance.

scholarly journals Automatic Identification of Tool Wear Based on Convolutional Neural Network in Face Milling Process

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3817 ◽  
Author(s):  
Xuefeng Wu ◽  
Yahui Liu ◽  
Xianliang Zhou ◽  
Aolei Mou
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Machine Tool ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Automatic Detection ◽  
Milling Process ◽  
Face Milling ◽  
Machining Process ◽  
Model Parameters

Monitoring of tool wear in machining process has found its importance to predict tool life, reduce equipment downtime, and tool costs. Traditional visual methods require expert experience and human resources to obtain accurate tool wear information. With the development of charge-coupled device (CCD) image sensor and the deep learning algorithms, it has become possible to use the convolutional neural network (CNN) model to automatically identify the wear types of high-temperature alloy tools in the face milling process. In this paper, the CNN model is developed based on our image dataset. The convolutional automatic encoder (CAE) is used to pre-train the network model, and the model parameters are fine-tuned by back propagation (BP) algorithm combined with stochastic gradient descent (SGD) algorithm. The established ToolWearnet network model has the function of identifying the tool wear types. The experimental results show that the average recognition precision rate of the model can reach 96.20%. At the same time, the automatic detection algorithm of tool wear value is improved by combining the identified tool wear types. In order to verify the feasibility of the method, an experimental system is built on the machine tool. By matching the frame rate of the industrial camera and the machine tool spindle speed, the wear image information of all the inserts can be obtained in the machining gap. The automatic detection method of tool wear value is compared with the result of manual detection by high precision digital optical microscope, the mean absolute percentage error is 4.76%, which effectively verifies the effectiveness and practicality of the method.

Empirical Expression of Tool Wear When Face Milling 416 SS

2009 ◽  
Author(s):  
Patricia Mun˜oz de Escalona ◽  
Paul G. Maropoulos
Keyword(s):  
Tool Wear ◽  
Tool Life ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Face Milling ◽  
Machining Process ◽  
Depth Of Cut ◽  
Milling Operation ◽  
Increase Tool Life ◽  
The Cost

During a machining process, cutting parameters must be taken into account, since depending on them the cutting edge starts to wear out to the point that tool can fail and needs to be change, which increases the cost and time of production. Since wear is a negative phenomenon on the cutting tool, due to the fact that tool life is reduced, it is important to optimize the cutting variables to be used during the machining process, in order to increase tool life. This research is focused on the influence of cutting parameters such as cutting speed, feed per tooth and axial depth of cut on tool wear during a face milling operation. The Taguchi method is applied in this study, since it uses a special design of orthogonal array to study the entire parameters space, with only few numbers of experiments. Also a relationship between tool wear and the cutting parameters is presented. For the studies, a martensitic 416 stainless steel was selected, due to the importance of this material in the machining of valve parts and pump shafts.

Development of a tool life prediction model for plaster machining

2007 ◽  
Vol 2 (2) ◽  
pp. 186
Author(s):  
Timothy J. Coole ◽  
Jose Filipe C.P. Antunes Simoes ◽  
Antonio R. Pires ◽  
David G. Cheshire
Keyword(s):  
Prediction Model ◽  
Tool Life ◽  
Life Prediction ◽  
Life Prediction Model

scholarly journals Calibrating a Local Civil Airport Runway Residual Life Prediction Model Using Random Effects and Joint Estimation

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Baoli Wei ◽  
Chengchao Guo ◽  
Fuming Wang
Keyword(s):  
Prediction Model ◽  
Life Prediction ◽  
Historical Data ◽  
Residual Life ◽  
Joint Estimation ◽  
Model Parameters ◽  
Marginal Effect ◽  
Airport Pavement ◽  
Residual Life Prediction ◽  
Life Prediction Model

Existing runway residual life prediction models need to be modified using the historical data of evaluated airports during evaluation and management for civil airports in China. If the data measured in the field and the historical data used for model calibration do not represent the actual historical performance of the evaluated airport, the predicted performance of the revised model might be poor. This study used measured pavement performance data for local civil airports in Henan Province from 2007 to 2017. The joint-estimation method was used to establish a functional residual life prediction model for local civil airport pavement with another dataset. A functional residual life prediction model for airport pavement was proposed in consideration of the influence of aircraft traffic and the thickness of the pavement surface layer. Taking into account the differences between samples in the two datasets, nonlinear regression with random-effect analysis and joint estimation were used to explain unobserved heterogeneity at the sample level and heteroscedasticity in the dataset. Based on the results of the established residual life prediction model, the marginal effect of the model parameters and the prediction performance of the entire model were analyzed with the measured data from the local airport pavement. Finally, the engineering applicability of the calibrated prediction model for pavement residual life was further evaluated.

Remaining Useful Life Prediction Model Based on Gradient Feature Stochastic Filtering

2013 ◽  
Vol 380-384 ◽  
pp. 1151-1155
Author(s):  
Lei Sun ◽  
Gang Li ◽  
Gang Li
Keyword(s):  
Prediction Model ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Engine Oil ◽  
Model Parameters ◽  
Filter Model ◽  
Stochastic Filtering ◽  
Useful Life ◽  
The Impact ◽  
Life Prediction Model

This paper proposed a remaining useful life prediction model to avoid the original monitoring information due to the influence of the oil monitoring linear regression process, thereby reducing the prediction error. According to the process of equipment wear, we analyzed the impact of the relationship between the wear, the metal particle concentration and the remaining useful life; then established an improved filter model. Using maximum likelihood parameter to estimate model parameters. Finally, taking a certain type of self-propelled Gun Engine Oil Spectrum Data for example, and the results show that the remaining useful life prediction model of equipment has a certain practical value.

Probabilistic Fatigue Life Prediction of Turbine Disc Considering Model Parameter Uncertainty

2016 ◽  
Vol 33 (2) ◽  
Author(s):  
Liping He ◽  
Le Yu ◽  
Shun-Peng Zhu ◽  
Liangliang Ding ◽  
Hong-Zhong Huang
Keyword(s):  
Monte Carlo ◽  
Fatigue Life ◽  
Prediction Model ◽  
Parameter Uncertainty ◽  
Life Prediction ◽  
Bayes Theorem ◽  
Fatigue Life Prediction ◽  
Model Parameters ◽  
Turbine Disc ◽  
Life Prediction Model

AbstractAiming to improve the predictive ability of Walker model for fatigue life prediction and taking the turbine disc alloy GH4133 as the application example, this paper investigates a new approach for probabilistic fatigue life prediction when considering parameter uncertainty inherent in the life prediction model. Firstly, experimental data are used to update the model parameters using Bayes’ theorem, so as to obtain the posterior probability distribution functions of two parameters of the Walker model, as well to achieve the probabilistic life prediction model for turbine disc. During the updating process, Markov Chain Monte Carlo (MCMC) technique is used to generate samples of the given distribution and estimating the parameters distinctly. After that, the turbine disc life is predicted using the probabilistic Walker model based on Monte Carlo simulation technique. The experimental results indicate that: (1) after using the small sample test data obtained from turbine disc, parameter uncertainty of the Walker model can be quantified and the corresponding probabilistic model for fatigue life prediction can be established using Bayes’ theorem; (2) there exists obvious dispersion of life data for turbine disc when predicting fatigue life in practical engineering application.

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