scholarly journals Tool Health Monitoring Using Airborne Acoustic Emission and Convolutional Neural Networks: A Deep Learning Approach

2021 ◽  
Vol 11 (6) ◽  
pp. 2734
Author(s):  
Muhammad Arslan ◽  
Khurram Kamal ◽  
Muhammad Fahad Sheikh ◽  
Mahmood Anwar Khan ◽  
Tahir Abdul Hussain Ratlamwala ◽  
...  
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Convolutional Neural Networks ◽  
Health Monitoring ◽  
High Speed ◽  
High Speed Steel ◽  
Machining Process ◽  
Turning Process ◽  
Cnc Machine ◽  
Time Frequency

Tool health monitoring (THM) is in great focus nowadays from the perspective of predictive maintenance. It prevents the increased downtime due to breakdown maintenance, resulting in reduced production cost. The paper provides a novel approach to monitoring the tool health of a computer numeric control (CNC) machine for a turning process using airborne acoustic emission (AE) and convolutional neural networks (CNN). Three different work-pieces of aluminum, mild steel, and Teflon are used in experimentation to classify the health of carbide and high-speed steel (HSS) tools into three categories of new, average (used), and worn-out tool. Acoustic signals from the machining process are used to produce time–frequency spectrograms and then fed to a tri-layered CNN architecture that has been carefully crafted for high accuracies and faster trainings. Different sizes and numbers of convolutional filters, in different combinations, are used for multiple trainings to compare the classification accuracy. A CNN architecture with four filters, each of size 5 × 5, gives best results for all cases with a classification average accuracy of 99.2%. The proposed approach provides promising results for tool health monitoring of a turning process using airborne acoustic emission.

PENGARUH VARIASI JENIS PENDINGIN DAN KEDALAMAN POTONG PADA PROSES BUBUT BAJA ST60 TERHADAP UMUR PAHAT

ROTOR ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 23
Author(s):  
Ahmad Khoirul Anwar ◽  
Digdo Listyadi ◽  
Dwi Djumhariyanto
Keyword(s):  
Tool Life ◽  
High Speed ◽  
Low Cost ◽  
High Speed Steel ◽  
Machining Process ◽  
Depth Of Cut ◽  
Turning Process ◽  
Cooking Oil ◽  
Production Processes ◽  
Diameter Reduction

Turning machining process is a warkpiece diameter reduction by using chisel cut to produce the shape of the workpiece on a turning, there are various types of machining turning chisel pieces on the turning chisel types include carbide, CBN, and insert. There are also other types on conventional chisel on a turning process, one of which is a turning type of high speed steel (HSS), the turning is widely used in coventional production processes for other than low cost is also easy to grinding. Parameter in this research is coolant and depth of cut. The coolant used is dromus, ex-oil, ex cooking oil. The depth of cut used is 0,3mm, 0,5mm amd 0,8mm. The highest of tool life in this reserch with dromus as coolant at 0,3mm depth of cut is 83,17 minutes. With ex-oil at 0,3 depth of cut the tool life is 70,79 minutes. And with ex-cooking oil the tool life is 56,77 minutes with 0,3mm depth of cut. While the lowest tool life be obtained with ex-cooking oil coolant at 0,8mm depth of cut is 38,90 minutes. So, the canclusion dromus is a batter then ex-oil and ex-cooking oil. This is caused when the dromus as coolant can mixed with water and become one so can get down temperture of chisel.

scholarly journals Finite Element Method of Nickel Chromium and Nickel Vanadium for Alternative CNC Machine Tool

2019 ◽  
Vol 9 (1) ◽  
pp. 4730-4733
Keyword(s):  
Finite Element ◽  
High Speed ◽  
Tool Material ◽  
High Speed Steel ◽  
Life Time ◽  
Machining Process ◽  
Depth Of Cut ◽  
Cnc Machine ◽  
Element Analysis ◽  
Nickel Chromium

CNC tool is generally made of high speed steel (HSS) which has shorter life due to the increasing depth of cut. The steel shank wears within a short life time by the chips produced during machining process. And even the tool material cannot withstand a large amount of load. The damaged tool material is analysed and an alternate material is used to manufacture the tool. Alternate material used to manufacture the CNC tool which would have properties superior than HSS. The objective of this paper is to analyze the temperature increase & the thermal deformation of different materials like nickel-chromium and nickel-vanadium using Finite Element Analysis.

scholarly journals Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images

Computation ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 35
Author(s):  
Hind R. Mohammed ◽  
Zahir M. Hussain
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Speed ◽  
Moving Objects ◽  
Real Life ◽  
Fuzzy Rules ◽  
High Rate ◽  
Moving Images ◽  
Animal Images

Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achieved a very high rate of correctly predicting malicious objects equal to recall = 0.98121 and a precision rate of 1. The test’s accuracy was evaluated using the F1 Score, which obtained a high percentage of 0.99052.

scholarly journals Development of turning process digital twin based on machine learning

2021 ◽  
pp. 32-41
Author(s):  
D. A. Rastorguev ◽  
◽  
A. A. Sevastyanov ◽  
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Networks ◽  
Information Technologies ◽  
Cutting Process ◽  
Turning Process ◽  
Force Load ◽  
Cnc Machine ◽  
Digital Twin ◽  
Artificial Neural

Today, manufacturing technologies are developing within the Industry 4.0 concept, which is the information technologies introduction in manufacturing. One of the most promising digital technologies finding more and more application in manufacturing is a digital twin. A digital twin is an ensemble of mathematical models of technological process, which exchanges information with its physical prototype in real-time. The paper considers an example of the formation of several interconnected predictive modules, which are a part of the structure of the turning process digital twin and designed to predict the quality of processing, the chip formation nature, and the cutting force. The authors carried out a three-factor experiment on the hard turning of 105WCr6 steel hardened to 55 HRC. Used an example of the conducted experiment, the authors described the process of development of the digital twin diagnostic module based on artificial neural networks. When developing a mathematical model for predicting and diagnosing the cutting process, the authors revealed higher accuracy, adaptability, and versatility of artificial neural networks. The developed mathematical model of online diagnostics of the cutting process for determining the surface quality and chip type during processing uses the actual value of the cutting depth determined indirectly by the force load on the drive. In this case, the model uses only the signals of the sensors included in the diagnostic subsystem on the CNC machine. As an informative feature reflecting the force load on the machine’s main motion drive, the authors selected the value of the energy of the current signal of the spindle drive motor. The study identified that the development of a digital twin is possible due to the development of additional modules predicting the accuracy of dimensions, geometric profile, tool wear.

Granulation of Extruded Plastic Wires: Influence of Tool Coating Properties and Tool Geometry on Cutting Forces and Tool Wear

2016 ◽  
Vol 1140 ◽  
pp. 181-188
Author(s):  
Macario Cardone ◽  
Matthias Putz ◽  
Gerhard Schmidt ◽  
Martin Dix ◽  
Jürgen Friedrich ◽  
...  
Keyword(s):  
High Speed ◽  
High Speed Steel ◽  
Polyamide 6 ◽  
Machining Process ◽  
Test Facility ◽  
Tool Geometry ◽  
Coated Tools ◽  
Tool Coating ◽  
Before And After ◽  
Wear Evaluation

Granulators are widely used to reduce reinforced and unreinforced plastic strands in small pieces. The tools implemented in this machining process are mainly made of high-speed steel. This work investigates diverse PVD hard thin coatings with the aim of improving tool life and efficiency in granulation technology. A test facility reproducing the main features of a real granulator has been designed and assembled. The machined strand materials are ABS plastic and fibreglass-reinforced polyamide 6, while the tested PVD films are CrN, TiCN, TiAlN and two different diamond-like carbon coatings. The wear evaluation of all coated tools has been done via structured light projection, together with a scanning electron microscopy-based analysis, before and after their implementation on the test facility. Furthermore, a suitable 2D finite element modelling of the machining process has been realized.

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