Micro-Milling Tool Wear Monitoring via Nonlinear Cutting Force Model

Liu, Tongshun; Wang, Qian; Wang, Weisu

doi:10.3390/mi13060943

Cited by 11 publications

(4 citation statements)

References 25 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The physics-based models of the machining process have been carefully developed in the past by considering the tool wear condition, the machining parameters information, and the environment setting [11,44,45,64]. However, those models are often not involved in the process of wear monitoring for most cases of the direct and indirect approaches.…”

Section: Physics-based Approachmentioning

confidence: 99%

Digital Twin of Micro-Milling Process for Micro-Tool Wear Monitoring

Christiand¹,

Kiswanto

Baskoro³

2023

Preprint

View full text Add to dashboard Cite

This paper presents a novel digital twin of the micro-milling process that can indirectly monitor the micro-tool wear progression by making inferences from the real-time and simulated variables of the micro-milling process. With its wear monitoring service, the digital twin is regarded as the new approach in the field of tool wear monitoring (TWM) systems. The dynamics of the micro-milling process are simulated by using physics-based models, such as spindle motor, spindle controller, and cutting torque models with real-time data from the actual micro-milling machine. The advantage of the proposed digital twin is that the wear monitoring can adaptively adjust to the main machining parameters, such as feed rate and spindle speed. Therefore, exhaustive training of the models is not needed whenever the machining parameters change. The performance of the digital twin in monitoring the wear progression has been evaluated through several slot micro-milling experiments of the stainless steel workpiece. The evaluation and analysis of the experiment result concluded that the proposed digital twin could detect wear progression abnormality by using the estimate discrepancy. Furthermore, the wear severity could be recognized using the final wear estimation value.

show abstract

Section: Physics-based Approachmentioning

confidence: 99%

Digital Twin of Micro-Milling Process for Micro-Tool Wear Monitoring

Christiand¹,

Kiswanto

Baskoro³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Karandikar et al [17] described a naive Bayes classifier method for TCM. Liu et al [18] verified that mechanistic cutting force model had the potential for monitoring micro-milling tool wear. A TCM system established by Seemuang et al [19] can increase the competitiveness of a machining process by increasing the utilized tool life and decreasing instances of part damage from excessive tool wear or tool breakage.…”

Section: Introductionmentioning

confidence: 99%

Research on tool condition monitoring (TCM) using a novel unsupervised deep neural network (DNN)

Gao,

Liu,

2023

J. vibroeng.

View full text Add to dashboard Cite

In order to improve the recognition precision and accuracy of tool wear monitoring, an unsupervised deep neural network (DNN) based on stack denoising autoencoder (SDA) is proposed. After feature extraction and selection, the stack denoising automatic coding network reduces the dimensionality of the feature vector. On this basis, principal component analysis (PCA) and T-distributed random neighbor embedding (t-SNE) are used to reduce the dimensionality of the features twice, and finally a simple two-dimensional feature matrix is obtained. Finally, the deep neural network model of SDA is established by adding SoftMax regression layer, and the tool wear monitoring results are taken as new labeled data, and the deep neural network parameters are fine-tuned by secondary backpropagation. The experimental results show that the proposed method can learn adaptively and obtain effective feature expression, and the tool wear state recognition results are highly accurate. The proposed method can effectively identify the tool wear state.

show abstract

“…The machining trajectory error is the shortest distance from the current actual position of the tool to its desired trajectory and is mainly due to the inconsistency between the actual movement value of the tool relative to the workpiece and the command value; it can directly reflect the accuracy of CNC machining [5,6]. In the process of CNC machining, the influence of various factors, such as the accuracy of the machine tool itself [7], the following error generated by the servo system [8], the wear of the cutting tool [9], the deformation of the material [10], and so on, can lead to the generation of machining trajectory errors. In actual machining, these errors will directly affect the accuracy and quality of the machined parts, and even lead to machining failure.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid-Model-Based CNC Machining Trajectory Error Prediction and Compensation Method

He,

Zhang,

et al. 2024

Electronics

View full text Add to dashboard Cite

Intelligent manufacturing is the main direction of Industry 4.0, pointing towards the future development of manufacturing. The core component of intelligent manufacturing is the computer numerical control (CNC) system. Predicting and compensating for machining trajectory errors by controlling the CNC system’s accuracy is of great significance in enhancing the efficiency, quality, and flexibility of intelligent manufacturing. Traditional machining trajectory error prediction and compensation methods make it challenging to consider the uncertainties that occur during the machining process, and they cannot meet the requirements of intelligent manufacturing with respect to the complexity and accuracy of process parameter optimization. In this paper, we propose a hybrid-model-based machining trajectory error prediction and compensation method to address these issues. Firstly, a digital twin framework for the CNC system, based on a hybrid model, was constructed. The machining trajectory error prediction and compensation mechanisms were then analyzed, and an artificial intelligence (AI) algorithm was used to predict the machining trajectory error. This error was then compensated for via the adaptive compensation method. Finally, the feasibility and effectiveness of the method were verified through specific experiments, and a realization case for this digital-twin-driven machining trajectory error prediction and compensation method was provided.

show abstract

Micro-Milling Tool Wear Monitoring via Nonlinear Cutting Force Model

Cited by 11 publications

References 25 publications

Digital Twin of Micro-Milling Process for Micro-Tool Wear Monitoring

Digital Twin of Micro-Milling Process for Micro-Tool Wear Monitoring

Research on tool condition monitoring (TCM) using a novel unsupervised deep neural network (DNN)

A Hybrid-Model-Based CNC Machining Trajectory Error Prediction and Compensation Method

Contact Info

Product

Resources

About