scholarly journals Deep Stacked Auto-Encoder Network Based Tool Wear Monitoring in the Face Milling Process

2020 ◽  
Vol 66 (4) ◽  
pp. 227-234
Author(s):  
VanThien Nguyen ◽  
VietHung Nguyen ◽  
VanTrinh Pham
Keyword(s):  
Tool Wear ◽  
Manufacturing Industry ◽  
Cutting Parameters ◽  
Milling Process ◽  
Machining Process ◽  
The Face ◽  
Wear Monitoring ◽  
Spindle Vibration ◽  
Deep Learning Network ◽  
Different Levels

Tool wear identification plays an important role in improving product quality and productivity in the manufacturing industry. The actual tool wear status with input cutting parameters may cause different levels of spindle vibration during the machining process. This research proposes an architecture comprising a deep learning network (DLN) to identify the actual wear state of machining tool. Firstly, data on spindle vibration signals are obtained from an acceleration sensor. The data are then pre-processed using the fast Fourier transform (FFT) method to reveal the relevant outstanding features in the frequency domain. Finally, the DLN is constructed based on stacked auto-encoders (SAE) and softmax, which is trained with the input data on the vibration features of the respective tool wear state. This DLN architecture is then used to identify the actual wear statuses of machining tool. The experimental results from the collected data show that the proposed DLN architecture is capable of identifying actual tool wear with high accuracy.

Acoustic Emission in Stainless Steel End Milling with Carbide Tools

2017 ◽  
Vol 753 ◽  
pp. 206-210
Author(s):  
Peter Babatunde Odedeyi ◽  
Khaled Abou-El-Hossein ◽  
Muhammad M. Liman ◽  
Abubakar I. Jumare ◽  
Abdulqadir N. Lukman
Keyword(s):  
Stainless Steel ◽  
Acoustic Emission ◽  
Tool Wear ◽  
Manufacturing Industry ◽  
End Milling ◽  
Cutting Parameters ◽  
Machining Process ◽  
Dimensional Precision ◽  
Tool Monitoring ◽  
Machined Parts

Tool wear is a complex phenomenon, it worsens surface quality, increases power consumption, and causes rejection of machined parts. Tool wear has a direct effect on the quality of the surface finish of the workpiece, dimensional precision and ultimately the cost of the parts produced. In modern automated manufacturing machines, tool monitoring system for automated machines should be capable of operating on-line and interpret the working condition of machining process at a given point in time. Therefore, there is a need to develop a continuous tool monitoring systems that would notify operator the state of tool in order to avoid tool failure or undesirable circumstances. This study therefore uses acoustic emission (AE) sensing techniques, signal processing and Artificial Neural Networks (ANN) frameworks to model and validate the machining process. The AE showed effects of tool breakage and ANN predictions closest to the experimental cutting parameters were obtained. It was also shown that the ANN prediction model obtained is a useful, reliable and quite effective tool for modeling tool wear of carbide tools when working on stainless steel. Thus, the results of the present research can be successfully applied in the manufacturing industry to reduce the time, energy and high experimental costs.

scholarly journals A Neuro-Fuzzy Approach Based Tool Condition Monitoring in AISI H13 Milling

2018 ◽  
Author(s):  
Md. Shafiul Alam ◽  
Maryam Aramesh ◽  
Stephen Veldhuis
Keyword(s):  
Tool Wear ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Manufacturing Industry ◽  
Cutting Tool ◽  
Milling Process ◽  
Tool Condition Monitoring ◽  
Tool Wear Monitoring ◽  
Tool Condition ◽  
Wear Monitoring

In the manufacturing industry, cutting tool failure is a probable fault which causes damage to the cutting tools, workpiece quality and unscheduled downtime. It is very important to develop a reliable and inexpensive intelligent tool wear monitoring system for use in cutting processes. A successful monitoring system can effectively maintain machine tools, cutting tool and workpiece. In the present study, the tool condition monitoring system has been developed for Die steel (H13) milling process. Effective design of experiment and robust data acquisition system ensured the machining forces impact in the milling operation. Also, ANFIS based model has been developed based on cutting force-tool wear relationship in this research which has been implemented in the tool wear monitoring system. Prediction model shows that the developed system is accurate enough to perform an online tool wear monitoring system in the milling process.

Designing and implementation of a novel online adaptive control with optimization technique in hard turning

2020 ◽  
pp. 095965182095219
Author(s):  
Vahid Pourmostaghimi ◽  
Mohammad Zadshakoyan
Keyword(s):  
Adaptive Control ◽  
Tool Wear ◽  
Hard Turning ◽  
Cutting Tool ◽  
Optimization Technique ◽  
Cutting Parameters ◽  
Numerical Control ◽  
Machining Process ◽  
Machining Parameters ◽  
Optimization Methodology

Determination of optimum cutting parameters is one of the most essential tasks in process planning of metal parts. However, to achieve the optimal machining performance, the cutting parameters have to be regulated in real time. Therefore, utilizing an intelligent-based control system, which can adjust the machining parameters in accordance with optimal criteria, is inevitable. This article presents an intelligent adaptive control with optimization methodology to optimize material removal rate and machining cost subjected to surface quality constraint in finish turning of hardened AISI D2 considering the real condition of the cutting tool. Wavelet packet transform of cutting tool vibration signals is applied to estimate tool wear. Artificial intelligence techniques (artificial neural networks, genetic programming and particle swarm optimization) are used for modeling of surface roughness and tool wear and optimization of machining process during hard turning. Confirmatory experiments indicated that the efficiency of the proposed adaptive control with optimization methodology is 25.6% higher compared to the traditional computer numerical control turning systems.

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.

Experiment and Discussion on Ultrasonic Vibration-Assisted Single Point Diamond Turning of Die Steels

2012 ◽  
Vol 497 ◽  
pp. 1-5
Author(s):  
Xiao Dan Xie ◽  
Yong Li ◽  
Cam Vinh Duong ◽  
Ahmed Al-Zahrani
Keyword(s):  
Tool Wear ◽  
Ultrasonic Vibration ◽  
Single Point ◽  
Cutting Parameters ◽  
Diamond Turning ◽  
Machining Process ◽  
Die Steels ◽  
Ultra Precision ◽  
Promising Solution ◽  
Vibration Parameters

Traditionally, single point diamond turning (SPDT) can not process ferreous metals because of acute tool wear. Ultrasonic vibration-assisted cutting(UVC) provides a promising solution for the problem. In this paper, for the aim of directly obtaining mirror surface on die steels, UVC method was used combining with SPDT process. Experiments were carried out on an ultra precision turning machine, cutting parameters and vibration parameters were well-chosen, and two kind of feed rates, two kinds of prevailing die steels were experimented. Mirror surfaces were successfully achieved on face turning, with the best roughness of Ra16.6nm. And the surface roughness, surface texture and tool wear in machining process were discussed.

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.

Turning of Austempered Ductile Iron with ceramic tools

2020 ◽  
pp. 095440542095715 ◽  
Author(s):  
A Fernández-Valdivielso ◽  
LN López de Lacalle ◽  
P Fernández-Lucio ◽  
H González
Keyword(s):  
Heat Treatment ◽  
Tool Wear ◽  
Ductile Iron ◽  
High Strength ◽  
Cutting Parameters ◽  
Precise Determination ◽  
Machining Process ◽  
Austempered Ductile Iron ◽  
Machining Time ◽  
Ceramic Tools

Austempered ductile iron castings (ADI) are characterized by the high strength and resistance to fatigue, impact, and wear. ADI mechanical properties are obtained by performing a heat treatment on ductile iron casting. Thus, the so-called ausferrite microstructure is achieved. However, heat treatment significantly affects ductile casting machinability. A precise determination of ADI microstructure, on the one hand, and to choose correct machining process parameters and tool wear control on the other, are essential to optimize cutting processes and for the introduction of ceramic inserts. Ceramics are an alternative to carbide tools. In this paper, ceramic tools for the dry turning of ADI castings are studied. Thus, different technical ceramics were analyzed, identifying the dominant wear mechanism and evolution. Tool wear rate magnitude was determined indirectly by the variation of cutting force along machining time. Finally, different tests helped to study ceramics wear sensitivity with respect to cutting parameters. Mixed ceramics of Al2O3 with TiC showed the best performance, followed by SiAlON ones.

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