Tool condition monitoring: unscented Kalman filter for tool flank wear estimation in turning of Inconel 718

2021 ◽  
pp. 1-13
Author(s):  
Chandrani Sadhukhan ◽  
Swarup Kumar Mitra ◽  
Ranjib Biswas ◽  
Mrinal Kanti Naskar
Keyword(s):  
Kalman Filter ◽  
Condition Monitoring ◽  
Inconel 718 ◽  
Flank Wear ◽  
Unscented Kalman Filter ◽  
Tool Condition Monitoring ◽  
Tool Flank Wear ◽  
Tool Condition

Progressive tool condition monitoring of end milling from machined surface images

2016 ◽  
Vol 232 (2) ◽  
pp. 251-266 ◽  
Author(s):  
Samik Dutta ◽  
Surjya K Pal ◽  
Ranjan Sen
Keyword(s):  
Condition Monitoring ◽  
Flank Wear ◽  
End Milling ◽  
Tool Condition Monitoring ◽  
Discrete Wavelet ◽  
Tool Flank Wear ◽  
Tool Condition ◽  
Milled Surface ◽  
Occurrence Matrix ◽  
Surface Images

Indirect tool condition monitoring in end milling is inevitable to produce high-quality finished products due to the complexity of end-milling process. Among the various indirect tool condition monitoring techniques, monitoring based on image processing by analyzing the surface images of final product is gaining high importance due to its non-tactile and flexible nature. The advances in computing facilities, texture analysis techniques and learning machines make these techniques feasible for progressive tool flank wear monitoring. In this article, captured end-milled surface images are analyzed using gray level co-occurrence matrix–based and discrete wavelet transform–based texture analyses to extract features which have a good correlation with progressive tool flank wear. Contrast and second diagonal moment are extracted from gray level co-occurrence matrix and root mean square and energy are extracted from discrete wavelet decomposition of end-milled surface images as features. Finally, these four features are utilized to build support vector machine–based regression models for predicting progressive tool flank wear with 94.8% average correlation between predicted and measured tool flank wear values.

Tool Wear Monitoring Using Microphone Signals and Recurrence Quantification Analysis when Drilling Composites

2013 ◽  
Vol 711 ◽  
pp. 239-244 ◽  
Author(s):  
Eshetu D. Eneyew ◽  
M. Ramulu
Keyword(s):  
Condition Monitoring ◽  
Cutting Tool ◽  
Flank Wear ◽  
Recurrence Quantification Analysis ◽  
Tool Condition Monitoring ◽  
Monitoring Method ◽  
Online Tool ◽  
Tool Condition ◽  
Quantification Analysis

The quality of the hole produced during the drilling of composite materials is one of the controlling factors for the resulting joint strength and integrity of the structural component. Quality of the hole depends on the condition of the cutting tool. Continuous cutting tool condition monitoring method is vital to accomplish the desired hole quality. To address this concern, an online tool condition monitoring technique using a simple audio microphone as a sensor is developed and Recurrence Quantification Analysis (RQA) methodology was used as a signal analysis tool to predict the tool condition in terms of flank wear. A series of experimental drilling operation was carried out on uni-directional carbon fiber reinforced plastic (CFRP) composite. It was found that the amplitude of the microphone signal decreases with the increase of the tool flank wear. In addition, from the selected eight RQA output variables, six of them show an increasing trend with the increase of the measured flank wear, whereas, two of them show a decreasing trend with the increase of tool wear. The same trend has been observed in both set of experiments. These results demonstrate that, this novel approach is an effective and economical online tool condition monitoring method.

scholarly journals Decision support system for tool condition monitoring in milling process using artificial neural network

2021 ◽  
Author(s):  
.Mohanraj T ◽  
◽  
Tamilvanan A. ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Mean Squared Error ◽  
Flank Wear ◽  
Tool Condition Monitoring ◽  
Ann Model ◽  
Vibration Signals ◽  
Tool Condition ◽  
Artificial Neural

This work discusses the development of tool condition monitoring system (TCMs) during milling of AISI stainless steel 304 using sound pressure and vibration signals. Response Surface Methodology (RSM) was used to design the experiments. The various milling parameters and vegetable-based cutting fluids (VBCFs) were optimized to reduce the surface roughness and flank wear. The experimental results reveal the direct relationship between the flank wear and sound and vibration signals. The various statistical parameters were extracted from the measured signals and given as input data to train the artificial neural network (ANN). From the developed ANN model, the flank wear was predicted with the mean squared error (MSE) of 0.0656 mm.

scholarly journals Tool Condition Monitoring in Hard Turning of Inconel 718 by using Vibration Technique

2019 ◽  
Vol 8 (11) ◽  
pp. 1143-1147
Keyword(s):  
Tool Wear ◽  
Condition Monitoring ◽  
Inconel 718 ◽  
Hard Turning ◽  
High Hardness ◽  
Tool Condition Monitoring ◽  
Depth Of Cut ◽  
Vibration Signals ◽  
Tool Condition ◽  
Coated Carbide

In various machining processes, the vibration signals are studied for tool condition monitoring often referred as wear monitoring. It is essential to overcome unpredicted machining trouble and to improvise the efficiency of the machine. Tool wear is a vital problem in materials such as nickel based alloys as they have high hardness ranges. Though they have high hardness, a nickel based alloy Inconel 718 with varying HRC (51, 53, and 55), is opted as work material for hard turning process in this work. Uncoated and coated carbide tools are employed as cutting tools. Taguchi’s L9 orthogonal array is considered by taking hardness, speed, feed and depth of cut as four input parameters, the number of experiments and the combinations of parameters for every run is obtained. The vibration signals are recorded at various stages of cutting, till the tool failure is observed. Taking this vibration signal data as input to ANOVA and Grey relation analysis (GRA) which categorizes the optimal and utmost dominant features such as Root Mean Square (RMS), Crest Factor (CF), Skewness (Sk), Kurtosis (Ku), Absolute Deviation (AD), Mean, Standard Deviation (SD), Variance, peak, Frequency and Time in the tool wear process

Tool Condition Monitoring While Using Vegetable Based Cutting Fluids During Milling of Inconel 625

2019 ◽  
Vol 18 (04) ◽  
pp. 563-581 ◽  
Author(s):  
S. Shankar ◽  
T. Mohanraj ◽  
A. Pramanik
Keyword(s):  
Condition Monitoring ◽  
Flank Wear ◽  
Tool Condition Monitoring ◽  
Inconel 625 ◽  
Depth Of Cut ◽  
Cutting Fluids ◽  
Mean Square ◽  
Mean Values ◽  
Tool Condition ◽  
Vibration Signatures

This investigation has designed a tool condition monitoring system (TCM) while milling of Inconel 625 based on sound and vibration signatures. The experiments were carried out based on response surface methodology (RSM) central composite design, design of experiments. The process parameters such as speed, feed, depth of cut and vegetable-based cutting fluids were optimized based on surface roughness, flank wear. It was found that the sound pressure and vibration signatures have the direct relation with flank wear. The statistical features like root mean square, skewness, kurtosis and mean values were extracted from the experimental data. From the designed NN estimator, the cutting tool flank wear was predicted with the mean square error (MSE) of 0.084212.

Development of multi sensor fusion based DAQ for in-process TCMS: Experimental and empirical analysis

2021 ◽  
pp. 095440892110580
Author(s):  
Balla Srinivasa Prasad ◽  
Aruna Prabha Kolluri ◽  
Rajesh B. Kumar ◽  
Medidi Rajasekhar
Keyword(s):  
Sensor Fusion ◽  
Condition Monitoring ◽  
Cutting Tool ◽  
Flank Wear ◽  
Tool Condition Monitoring ◽  
Sensor Data ◽  
Process Conditions ◽  
Vibration Signals ◽  
Thermal Images ◽  
Tool Condition

In order to manufacture low-cost, high-quality goods, in-process tool condition monitoring is an essential responsibility in the manufacturing industry. In this study, a multisensor fusion technique was used to build and execute an effective and reliable TCM system in turning operations with coated and uncoated tungsten carbide inserts. In dry turning, an attempt has been made to optimize the turning process parameter and monitor the tool's condition. Acoustic optic emission based sensor i.e., Laser Doppler Vibrometer and FLIR E60 infrared thermal camera are strategically placed near the machining zone. Thermal images and vibration signals are recorded using an appropriate charge amplifier. To extract characteristics from numerous sensor data, a National Instruments data acquisition (NI-DAQ) system is constructed utilizing LabVIEW software. Thermal images are used to gather temperatures from tool-work piece locations. Vibration signals are translated into vibration parameters. These characteristics serve as the foundation for establishing in-process TCMS. Tool wear, vibrational displacements (Disp), and cutting temperature are investigated as a result of varied tool insert materials and process conditions (CT). Utilizing ISO 10816-3, ISO 3685, and ISO-18434-2008 standards, the cutting tool condition was assessed using extracted features from multi sensor fusion techniques. For Ti-6Al-4 V, the displacement of uncoated and coated tools increased by 65.28% and 44.71%, respectively. For AISI 316L flank wear, the uncoated insert effected 41% and the coated insert impacted 24.14%, respectively. While machining Al7075, the relationship of depth of cut and feed rate on flank wear maintains an identical trend. It is discovered that both temperature and displacement have a significant role in the evolution of flank wear, which is examined in depth. This paper recognizes the use of multi-sensor data in tool condition monitoring when rotating with various cutting tool inserts.

scholarly journals Condition Monitoring in Drilling Operation based on Vibration Signals

2019 ◽  
Vol 8 (3) ◽  
pp. 1272-1277
Keyword(s):  
Neural Network ◽  
Frequency Domain ◽  
Condition Monitoring ◽  
Time Domain ◽  
Flank Wear ◽  
Tool Condition Monitoring ◽  
Vibration Signals ◽  
Drilling Operation ◽  
Tool Condition ◽  
The Time Domain

Tool condition monitoring is the efficient process for all machining managing operation and the maintenance of machinery operation. Tool condition monitoring implies effective production cost, the rate of tool life, tool quality, dimensional accuracy in terms of tolerance and surface finish in machine shop. Here the machining operation is fully depending on the whims & fancies of the operator. So when a new person operating the machine it makes more troubles in terms to find out the tool wearing point and it make operation difficulty by the operator. To overcome this difficulty a systematic methodology required for machining operation. This paper deals with monitoring the condition on the drilling operation with the help of Accelerometer sensor a physical vibration model 8636C50 having a broad band sensitivity of Sensitivity (±5%) 100.0mV/g and resonant frequency up to 22.0 kHz and performing the drilling operation on EN 24 steel at various operation parameters and analyzing the time domain signal response and frequency domain response graph and implemented analyze the feasibility of proposed methodology for practical applications. Further, the Lab View was used to predict amplitude of work piece vibration which determines the tool condition after various experimental tests. In the time domain, the characteristic parameter during drill wear represent RMS value increase in flank wear and also shows the linear relationship between these two. In the frequency domain, the characteristic parameters during drill failure represent the magnitude of vibration amplitude and the increase in flank wear. Here multilayer Artificial Neural Network (ANN) model, Fuzzy Neural Network and Taguchi Method have been trained with the experimental data using back propagation algorithm. Condition monitoring of drilling is fully depending on the vibration signals. Based on the vibration signal the tool wear point is found out. Experiments results indicated the effect of unconditional drilling operation and detected the tool failure and proper operating condition for drilling machining.

scholarly journals Tool Condition Monitoring with Non-contacting Sensors in Inconel 718 Milling Processes

2016 ◽  
Vol 25 (6) ◽  
pp. 445-451
Author(s):  
Yong-Ki Choi ◽  
Moon-Chang Hwang ◽  
Young-Jun Kim ◽  
Kwang-Hwi Park ◽  
Joon-Young Koo ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Inconel 718 ◽  
Tool Condition Monitoring ◽  
Tool Condition

scholarly journals Adoption of the Omative system in Inconel 718 turbine blade machining

Mechanik ◽  
2017 ◽  
Vol 90 (1) ◽  
pp. 42-43
Author(s):  
Jan Burek ◽  
Paweł Sułkowicz ◽  
Michał Gdula ◽  
Jarosław Buk ◽  
Marcin Sałata
Keyword(s):  
Control System ◽  
Turbine Blade ◽  
Condition Monitoring ◽  
Inconel 718 ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
Blade Machining

This paper presents a research focusing on adopting adapting control system Omative for tool condition monitoring during milling of Inconel 718 turbine blade.

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