Machine Learning Acoustic Emission Based Monitoring of Cold Forging for Smart Manufacturing: A Review

2021 ◽  
Vol 2 (3) ◽  
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Product Quality ◽  
High Speed ◽  
High Rate ◽  
Cold Forging ◽  
Smart Manufacturing ◽  
Forming Process ◽  
Cold Forming ◽  
Effective Manner

Cold forging is a high-speed forming technique used to shape metals at near room temperature. and it allows high-rate production of high strength metal-based products in a consistent and cost-effective manner. However, cold forming processes are characterized by complex material deformation dynamics which makes product quality control difficult to achieve. There is no well defined mathematical model that governs the interactions between a cold forming process, material properties, and final product quality. The goal of this work is to provide a review for the state of research in the field of using acoustic emission (AE) technology in monitoring cold forging process. The integration of AE with machine learning (ML) algorithms to monitor the quality is also reviewed and discussed. It is realized that this promising technology didn’t receive the deserving attention for its implementation in cold forging and that more work is needed.

Study on the Large Module Spur Gear Cold Forming Process by Means of Numerical Simulation

2011 ◽  
Vol 189-193 ◽  
pp. 2642-2646 ◽  
Author(s):  
Qian Li ◽  
Yi Bian ◽  
Zhi Ping Zhong ◽  
Gui Hua Liu ◽  
Ying Chen
Keyword(s):  
Numerical Simulation ◽  
Simulation Method ◽  
Spur Gear ◽  
Cold Forging ◽  
Forming Process ◽  
Cold Forming ◽  
Forging Process ◽  
Flow Method ◽  
Precision Forging ◽  
Cold Precision Forging

The cold forging process of large module spur gear with four modules and 59mm breadth is performed by means of numerical simulation method. Two processes to forming such spur gears were compared by the simulation method, one is with the closed-die performing and extrusion in the finish-forging, the other is with divided-flow method in the finish-forging. Especially, the divided-flow method is analyzed in detail. The necessary reference and basis to realize practical cold precision forging process of spur gear with large modulus is provided eventually.

scholarly journals Laser processing quality monitoring by combining acoustic emission and machine learning: a high-speed X-ray imaging approach

Procedia CIRP ◽  
2018 ◽  
Vol 74 ◽  
pp. 654-658 ◽  
Author(s):  
K. Wasmer ◽  
T. Le-Quang ◽  
B. Meylan ◽  
F. Vakili-Farahani ◽  
M.P. Olbinado ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Laser Processing ◽  
High Speed ◽  
Quality Monitoring ◽  
Processing Quality ◽  
X Ray ◽  
X Ray Imaging ◽  
Imaging Approach

A Study and Analysis on Electromagnetic Compression Forming Processed Aluminum Alloy Tubes

2011 ◽  
Vol 337 ◽  
pp. 560-563
Author(s):  
Selvam Rajiv ◽  
Karibeeran Shanmuga Sundaram ◽  
Pablo Pasquale
Keyword(s):  
Aluminum Alloy ◽  
Mathematical Analysis ◽  
High Speed ◽  
High Rate ◽  
Outer Diameter ◽  
Forming Process ◽  
High Speed Forming ◽  
Forming Characteristics ◽  
Forming Methods ◽  
Electromagnetic Compression

Electromagnetic forming is one of the high- rate forming methods that are extensively used to form and join axisymmetric tubes and metal sheet. It is a high speed forming process using a pulsed magnetic field to form work pieces made of metals such as copper or aluminum alloys with high electrical conductivity. In this work, the experimental investigation and mathematical analysis of electromagnetic compression forming of aluminum alloy tubes AA6063 is studied. The aim of the work was to verify the results from MATLAB code with the experimental data. Experiments were conducted on aluminum alloy AA6063 tubes of outer diameter 40 mm and wall thickness of 2 mm with a nominal tensile strength of 214 MPa. The tube was compressed using 4 turn helical actuator discharge coil that can be energized up to 20 kJ. A study on the post forming characteristics hardness and on metallurgical effects were also carried out. The results of the mathematical analysis using MATLAB 2010 showed good correlation with experimental results.

scholarly journals Cold forging of a hollow flanged part by an unconventional extrusion method

2021 ◽  
Vol 2130 (1) ◽  
pp. 012019
Author(s):  
G Winiarski ◽  
T Bulzak ◽  
Ł Wójcik ◽  
M Szala
Keyword(s):  
Steel Tube ◽  
Cold Forging ◽  
Forming Process ◽  
Flow Process ◽  
Cold Forming ◽  
Forged Parts ◽  
42Crmo4 Steel ◽  
Hollow Part ◽  
Tool Strength ◽  
Extrusion Method

Abstract This paper presents a numerical analysis of a new cold forming process for a hollow part with an external flange. The following techniques were used: forward extrusion, an unconventional method of extrusion with a moving sleeve, and upsetting in a tapered die cavity. The billet (42CrMo4 steel tube) was formed at ambient temperature. The study aimed to investigate the proposed method in terms of forged part accuracy. The following are examined and discussed: material flow, process force parameters in relation to tool strength, energy consumption of individual operations, as well as the distributions of strains, stresses, temperature and Cockcroft-Latham integrals in the produced part. The study has confirmed that hollow forged parts with external flanges of relatively large diameters and heights can be cold formed in several operations using different techniques.

APPLICATION OF THE EARLY DAMAGE DIAGNOSTICS TECHNIQUE TO EXAMINATION OF THE AVIATION PANEL

2019 ◽  
Vol 85 (6) ◽  
pp. 53-63 ◽  
Author(s):  
I. E. Vasil’ev ◽  
Yu. G. Matvienko ◽  
A. V. Pankov ◽  
A. G. Kalinin
Keyword(s):  
Acoustic Emission ◽  
Damage Detection ◽  
High Speed ◽  
Early Stage ◽  
Video Data ◽  
High Speed Video ◽  
Damage Diagnostics ◽  
Subsurface Defect ◽  
Sensitive Coating ◽  
Early Damage

The results of using early damage diagnostics technique (developed in the Mechanical Engineering Research Institute of the Russian Academy of Sciences (IMASH RAN) for detecting the latent damage of an aviation panel made of composite material upon bench tensile tests are presented. We have assessed the capabilities of the developed technique and software regarding damage detection at the early stage of panel loading in conditions of elastic strain of the material using brittle strain-sensitive coating and simultaneous crack detection in the coating with a high-speed video camera “Video-print” and acoustic emission system “A-Line 32D.” When revealing a subsurface defect (a notch of the middle stringer) of the aviation panel, the general concept of damage detection at the early stage of loading in conditions of elastic behavior of the material was also tested in the course of the experiment, as well as the software specially developed for cluster analysis and classification of detected location pulses along with the equipment and software for simultaneous recording of video data flows and arrays of acoustic emission (AE) data. Synchronous recording of video images and AE pulses ensured precise control of the cracking process in the brittle strain-sensitive coating (tensocoating)at all stages of the experiment, whereas the use of structural-phenomenological approach kept track of the main trends in damage accumulation at different structural levels and identify the sources of their origin when classifying recorded AE data arrays. The combined use of oxide tensocoatings and high-speed video recording synchronized with the AE control system, provide the possibility of definite determination of the subsurface defect, reveal the maximum principal strains in the area of crack formation, quantify them and identify the main sources of AE signals upon monitoring the state of the aviation panel under loading P = 90 kN, which is about 12% of the critical load.

Machine Learning Approach for Confirmation of COVID-19 Cases: Positive, Negative, Death and Release (Preprint)

2020 ◽  
Author(s):  
Samir Bandyopadhyay Sr ◽  
SHAWNI DUTTA ◽  
SHAWNI DUTTA ◽  
SHAWNI DUTTA
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Health Workers ◽  
Original Data ◽  
High Rate ◽  
Learning Approach ◽  
Good Decision ◽  
Number Of Patients ◽  
Machine Learning Approach ◽  
Death Cases

BACKGROUND In recent days, Covid-19 coronavirus has been an immense impact on social, economic fields in the world. The objective of this study determines if it is feasible to use machine learning method to evaluate how much prediction results are close to original data related to Confirmed-Negative-Released-Death cases of Covid-19. For this purpose, a verification method is proposed in this paper that uses the concept of Deep-learning Neural Network. In this framework, Long short-term memory (LSTM) and Gated Recurrent Unit (GRU) are also assimilated finally for training the dataset and the prediction results are tally with the results predicted by clinical doctors. The prediction results are validated against the original data based on some predefined metric. The experimental results showcase that the proposed approach is useful in generating suitable results based on the critical disease outbreak. It also helps doctors to recheck further verification of virus by the proposed method. The outbreak of Coronavirus has the nature of exponential growth and so it is difficult to control with limited clinical persons for handling a huge number of patients with in a reasonable time. So it is necessary to build an automated model, based on machine learning approach, for corrective measure after the decision of clinical doctors. It could be a promising supplementary confirmation method for frontline clinical doctors. The proposed method has a high prediction rate and works fast for probable accurate identification of the disease. The performance analysis shows that a high rate of accuracy is obtained by the proposed method. OBJECTIVE Validation of COVID-19 disease METHODS Machine Learning RESULTS 90% CONCLUSIONS The combined LSTM-GRU based RNN model provides a comparatively better results in terms of prediction of confirmed, released, negative, death cases on the data. This paper presented a novel method that could recheck occurred cases of COVID-19 automatically. The data driven RNN based model is capable of providing automated tool for confirming, estimating the current position of this pandemic, assessing the severity, and assisting government and health workers to act for good decision making policy. It could be a promising supplementary rechecking method for frontline clinical doctors. It is now essential for improving the accuracy of detection process. CLINICALTRIAL 2020-04-03 3:22:36 PM

scholarly journals Feasibility of Machine Learning Algorithms for Predicting the Deformation of Anodic Titanium Films by Modulating Anodization Processes

Materials ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1089
Author(s):  
Sung-Hee Kim ◽  
Chanyoung Jeong
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Multiclass Classification ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Smart Manufacturing ◽  
Gradient Boosting ◽  
Experimental Conditions ◽  
Learning Techniques ◽  
Tio2 Nanostructures

This study aims to demonstrate the feasibility of applying eight machine learning algorithms to predict the classification of the surface characteristics of titanium oxide (TiO2) nanostructures with different anodization processes. We produced a total of 100 samples, and we assessed changes in TiO2 nanostructures’ thicknesses by performing anodization. We successfully grew TiO2 films with different thicknesses by one-step anodization in ethylene glycol containing NH4F and H2O at applied voltage differences ranging from 10 V to 100 V at various anodization durations. We found that the thicknesses of TiO2 nanostructures are dependent on anodization voltages under time differences. Therefore, we tested the feasibility of applying machine learning algorithms to predict the deformation of TiO2. As the characteristics of TiO2 changed based on the different experimental conditions, we classified its surface pore structure into two categories and four groups. For the classification based on granularity, we assessed layer creation, roughness, pore creation, and pore height. We applied eight machine learning techniques to predict classification for binary and multiclass classification. For binary classification, random forest and gradient boosting algorithm had relatively high performance. However, all eight algorithms had scores higher than 0.93, which signifies high prediction on estimating the presence of pore. In contrast, decision tree and three ensemble methods had a relatively higher performance for multiclass classification, with an accuracy rate greater than 0.79. The weakest algorithm used was k-nearest neighbors for both binary and multiclass classifications. We believe that these results show that we can apply machine learning techniques to predict surface quality improvement, leading to smart manufacturing technology to better control color appearance, super-hydrophobicity, super-hydrophilicity or batter efficiency.

Comparative study on the use of acoustic emission and vibration analyses for the bearing fault diagnosis of high-speed trains

2021 ◽  
pp. 147592172110360
Author(s):  
Dongming Hou ◽  
Hongyuan Qi ◽  
Honglin Luo ◽  
Cuiping Wang ◽  
Jiangtian Yang
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
High Speed ◽  
High Speed Train ◽  
Signal Attenuation ◽  
Transmission Path ◽  
Bearing Fault Diagnosis ◽  
Operation Conditions ◽  
Fault Features ◽  
High Speed Trains

A wheel set bearing is an important supporting component of a high-speed train. Its quality and performance directly determine the overall safety of the train. Therefore, monitoring a wheel set bearing’s conditions for an early fault diagnosis is vital to ensure the safe operation of high-speed trains. However, the collected signals are often contaminated by environmental noise, transmission path, and signal attenuation because of the complexity of high-speed train systems and poor operation conditions, making it difficult to extract the early fault features of the wheel set bearing accurately. Vibration monitoring is most widely used for bearing fault diagnosis, with the acoustic emission (AE) technology emerging as a powerful tool. This article reports a comparison between vibration and AE technology in terms of their applicability for diagnosing naturally degraded wheel set bearings. In addition, a novel fault diagnosis method based on the optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) and chirp Z-transform (CZT) is proposed to diagnose early composite fault defects in a wheel set bearing. The optimization CYCBD is adopted to enhance the fault-induced impact response and eliminate the interference of environmental noise, transmission path, and signal attenuation. CZT is used to improve the frequency resolution and match the fault features accurately under a limited data length condition. Moreover, the efficiency of the proposed method is verified by the simulated bearing signal and the real datasets. The results show that the proposed method is effective in the detection of wheel set bearing faults compared with the minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) methods. This research is also the first to compare the effectiveness of applying AE and vibration technologies to diagnose a naturally degraded high-speed train bearing, particularly close to actual line operation conditions.

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