Biography: Z.W. Zhong is the Director of the Mechatronics Stream Programme in the School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore. He has published over 400 journal and conference papers, books and book chapters. His Hirsch Index is 23 (SCI) or 24 (Scopus). His research interests include mechatronics and design, modeling and analyses, precision engineering, nanotechnology, advanced metrology and sensing systems, servo mechanisms, control of vibrations, optical engineering, artificial intelligence and machines, advanced manufacturing technologies, sensors and actuators, unmanned aerial vehicles, finite element analyses, industrial informatics, microelectronics packaging, micro-systems, etc. He obtained his Doctor of Engineering from Tohoku University, Japan. He has over 20 years research and development experience in industry, research institutes and universities in Japan and Singapore. He worked at RIKEN (The Institute of Physical and Chemical Research) in Japan, and at Gintic Institute of Manufacturing Technology (now called Singapore Institute of Manufacturing Technology) in Singapore, before he joined Nanyang Technological University. A number of journal and conference papers authored or co-authored by him received best paper awards. He has served the Editorial Boards for several international journals, conducted short courses/workshops, and served dozens of international conferences as conference chairs, international program committees, or keynote speakers in various countries. 64
Mechatronics, design and modeling: from precision engineering to nanotechnology 2 Nov 2013 at Automation 2013 Z.W. Zhong School of Mechanical & Aerospace Engineering Nanyang Technological University, Singapore 1 Zhong, Z.W., C.K. Yeong, Development of a Gripper Using SMA Wire, Sensors and Actuators A-Physical, Vol. 126, No. 2, 2006, pp. 375-381. 2 65
S.K. Nah, Z.W. Zhong, A microgripper using piezoelectric actuation for micro-object manipulation, Sensors and Actuators A- Physical 133 (1) (2007) 218 224. 3 Zhong Z.W. L.P. Khoo and S.T. Han, Prediction of surface roughness of turned surfaces using neural networks, The International Journal of Advanced Manufacturing Technology, Vol. 28, Nos. 7-8, 2006, pp. 688-693. 4 66
Zhong Z.W. L.P. Khoo and S.T. Han, Prediction of surface roughness of turned surfaces using neural networks, The International Journal of Advanced Manufacturing Technology, Vol. 28, Nos. 7-8, 2006, pp. 688-693. 5 Zhong Z.W. L.P. Khoo and S.T. Han, Prediction of surface roughness of turned surfaces using neural networks, The International Journal of Advanced Manufacturing Technology, Vol. 28, Nos. 7-8, 2006, pp. 688-693. 6 67
B. Lotfi, Z.W. Zhong, L.P. Khoo, A novel algorithm to generate backlash-free motions, Mechanism and Machine Theory 45 (8) (2010) 1171-1184. 7 B. Lotfi, Z.W. Zhong, L.P. Khoo, A novel algorithm to generate backlash-free motions, Mechanism and Machine Theory 45 (8) (2010) 1171-1184. 8 68
Z.W. Zhong, J.-H. Zhou, Ye Nyi Win Correlation Analysis of Cutting Force and Acoustic Emission Signals for Tool Condition Monitoring, 9th Asian Control Conference, Turkey, 2013. 9 Z.W. Zhong, J.-H. Zhou, Ye Nyi Win Correlation Analysis of Cutting Force and Acoustic Emission Signals for Tool Condition Monitoring, 9th Asian Control Conference, Turkey, 2013. 69 10
Z.W. Zhong, J.-H. Zhou, Ye Nyi Win Correlation Analysis of Cutting Force and Acoustic Emission Signals for Tool Condition Monitoring, 9th Asian Control Conference, Turkey, 2013. 11 Z.W. Zhong, J.-H. Zhou, Ye Nyi Win Correlation Analysis of Cutting Force and Acoustic Emission Signals for Tool Condition Monitoring, 9th Asian Control Conference, Turkey, 2013. 12 70
Z.W. Zhong, J.-H. Zhou, Ye Nyi Win Correlation Analysis of Cutting Force and Acoustic Emission Signals for Tool Condition Monitoring, 9th Asian Control Conference, Turkey, 2013. 13 Z.W. Zhong, J.-H. Zhou, Ye Nyi Win Correlation Analysis of Cutting Force and Acoustic Emission Signals for Tool Condition Monitoring, 9th Asian Control Conference, Turkey, 2013. 14 71
TABLE III. CORRELATION COEFFICIENTS BETWEEN FORCE FEATURES, AND SURFACE ROUGHNESS AS WELL AS TOOL WEAR Z.W. Zhong, J.-H. Zhou, Ye Nyi Win Correlation Analysis of Cutting Force and Acoustic Emission Signals for Tool Condition Monitoring, 9th Asian Control Conference, Turkey, 2013. 15 TABLE IV. CORRELATION COEFFICIENTS BETWEEN AE FEATURES, AND SURFACE ROUGHNESS AS WELL AS TOOL WEAR Z.W. Zhong, J.-H. Zhou, Ye Nyi Win Correlation Analysis of Cutting Force and Acoustic Emission Signals for Tool Condition Monitoring, 9th Asian Control Conference, Turkey, 2013. 16 72
Z.W. Zhong, J.-H. Zhou, Ye Nyi Win Correlation Analysis of Cutting Force and Acoustic Emission Signals for Tool Condition Monitoring, 9th Asian Control Conference, Turkey, 2013. 17 Z.W. Zhong, J.-H. Zhou, Ye Nyi Win Correlation Analysis of Cutting Force and Acoustic Emission Signals for Tool Condition Monitoring, 9th Asian Control Conference, Turkey, 2013. 18 73
DFI - X as a Linear Transformation Collected Data X m >> n J.H. Zhou, C.K. Pang, F.L. Lewis, Z.W. Zhong, Intelligent Diagnosis and Prognosis of Tool Wear Using Dominant Feature Identification, IEEE Transactions on Industrial Informatics, 5 (4) (2009) 454-464. 19 DFI - Data Compression z 1 T q Lemma 1: For any y U 1 Xx R vector U y is the best least-square approximation to data vector z R m Singular Value Decomposition (SVD) Data Compression Retained q SVs Optimal choice of R q for least square errors of vectors in R m J.H. Zhou, C.K. Pang, F.L. Lewis, Z.W. Zhong, Intelligent Diagnosis and Prognosis of Tool Wear Using Dominant Feature Identification, IEEE Transactions on Industrial Informatics, 5 (4) (2009) 454-464. 20 74
DFI - Selection of Dominant Features Lemma 2: The th i feature in feature space n R maps into the reduced space q R as the th i column of matrix T 1 V 1 The original feature vectors are in n R denoted by e, e, 2 e 1 n, Three spaces Transfer Ye i V i T 1 1 e i w1 w 2 w n e i w i th n i feature in R maps into the reduced space th T as the column of matrix 1 V 1 q R Equivalent to select the best columns i of T 1 1 V R q J.H. Zhou, C.K. Pang, F.L. Lewis, Z.W. Zhong, Intelligent Diagnosis and Prognosis of Tool Wear Using Dominant Feature Identification, IEEE Transactions on Industrial Informatics, 5 (4) (2009) 454-464. 21 Case Study 1 Tool Wear Prediction High speed milling machine Ball nose cutter and flank wear Components Cutter Röders TEC vertical milling machine 6mm ball nose tungsten carbide cutters Titanium Ti6Al4V workpiece AE Sensor 8152B211 Piezotron AE sensor (Kistler) Kistler 5127B11 multichannel charge amplifier NI-DAQ PCI 6250 M series Work piece LECIA MZ12.5 Computer Dynamometer J.H. Zhou, C.K. Pang, Z.W. Zhong, F.L. Lewis, Tool Wear Monitoring Using Acoustic Emissions by Dominant-Feature Identification, IEEE Transactions on Instrumentation and Measurement 60 (2) (2011) 547-559. 22 75
Dominant Features and SVs m=10,000 and n=16 Singular Values (SVs) Identify dominant features using 3 or 4 retained SVs J.H. Zhou, C.K. Pang, Z.W. Zhong, F.L. Lewis, Tool Wear Monitoring Using Acoustic Emissions by Dominant-Feature Identification, IEEE Transactions on Instrumentation and Measurement 60 (2) (2011) 547-559. 23 Prediction Results Best possible prediction using 16 features with 8.8% mean relative error Prediction using DFI selected 4 features with 11.61% mean relative error Save up to 60% in computational time! J.H. Zhou, C.K. Pang, F.L. Lewis, Z.W. Zhong, Intelligent Diagnosis and Prognosis of Tool Wear Using Dominant Feature Identification, IEEE Transactions on Industrial Informatics, 5 (4) (2009) 454-464. 24 76
We investigate different sensors, and different signal processing and feature extraction techniques. A hierarchical rule-based fault detection system which is comprised of a knowledge base coupled with an inference engine is proposed. The knowledge base that maps the fault mode to signal processing and defect detection methods is established. The architecture of the rule-based diagnosis system Z.W. Zhong, Y.T.L. Wee, J.H. Zhou, Intelligent Fault Detection and Diagnosis of Rotating Machines, International Conference on Intelligent Robotics and Applications, 2012, Canada. 25 Frequency spectra of joint Hilbert-transformation FFT of the acceleration signals obtained when there is a ball fault and there is no fault in bearings at a rotating speed of 1200 rpm Z.W. Zhong, Y.T.L. Wee, J.H. Zhou, Intelligent Fault Detection and Diagnosis of Rotating Machines, International Conference on Intelligent Robotics and Applications, 2012, Canada. 26 77
FFT periodogram of the acceleration signal when there is unbalance Z.W. Zhong, Y.T.L. Wee, J.H. Zhou, Intelligent Fault Detection and Diagnosis of Rotating Machines, International Conference on Intelligent Robotics and Applications, 2012, Canada. 27 FFT periodogram of the acceleration signal when there is no fault Z.W. Zhong, Y.T.L. Wee, J.H. Zhou, Intelligent Fault Detection and Diagnosis of Rotating Machines, International Conference on Intelligent Robotics and Applications, 2012, Canada. 28 78
Summary of the capabilities to detect the various machine faults using the current, torque and acceleration sensors and the signal processing methods investigated Z.W. Zhong, Y.T.L. Wee, J.H. Zhou, Intelligent Fault Detection and Diagnosis of Rotating Machines, International Conference on Intelligent Robotics and Applications, 2012, Canada. 29 Machine Faults Detection and Isolation Fault Simulator Machine Encoder and clamp meters J.H. Zhou, C.K. Pang, F.L. Lewis, Z.W. Zhong, Dominant Feature Identification for Industrial Fault Detection and Isolation Applications, Expert Systems With Applications 38 (8) (2011) 10676-10684. 30 79
Feature Extraction Features extracted Features extracted J.H. Zhou, C.K. Pang, F.L. Lewis, Z.W. Zhong, Dominant Feature Identification for Industrial Fault Detection and Isolation Applications, Expert Systems With Applications 38 (8) (2011) 10676-10684. 31 All 8 sensors and 120 features Fault identification accuracy 12 features from current, 12 features from torque, 18 feature from accelerometer Total numbers of features: 12*3 +12 +18*4 = 120 J.H. Zhou, C.K. Pang, F.L. Lewis, Z.W. Zhong, Dominant Feature Identification for Industrial Fault Detection and Isolation Applications, Expert Systems With Applications 38 (8) (2011) 10676-10684. 32 80
Proposed 2-Stage Framework Decentralized DFI (DDFI) Augmented DFI (ADFI) Neural Network (NN) 1 hidden layer, 5 neurons, and radial basis functions J.H. Zhou, C.K. Pang, F.L. Lewis, Z.W. Zhong, Dominant Feature Identification for Industrial Fault Detection and Isolation Applications, Expert Systems With Applications 38 (8) (2011) 10676-10684. 33 Decentralized DFI Features Selected Prediction accuracy J.H. Zhou, C.K. Pang, F.L. Lewis, Z.W. Zhong, Dominant Feature Identification for Industrial Fault Detection and Isolation Applications, Expert Systems With Applications 38 (8) (2011) 10676-10684. 120 features reduced to 28, 84.4% Accurate 34 81
Augmented DFI Features Selected Prediction accuracy 120 features to 13, 8 sensors to 4! 99.4% Accurate Computational Complexity ADFI O( mn 2 3 6 6 ) O( n ) O(7.2 10 ) O(1.728 10 ) DDFI 2 3 6 6 6 6 5 ( O( mn ) O( n )) 5 ( O(1.44 10 ) O(1.728 10 )) O(7.2 10 ) O(8.64 10 ) J.H. Zhou, C.K. Pang, F.L. Lewis, Z.W. Zhong, Dominant Feature Identification for Industrial Fault Detection and Isolation Applications, Expert Systems With Applications 38 (8) (2011) 10676-35 Zhong, Z.W., Nakagawa, T., Development of a Micro-Displacement Table for Ultra-Precision Machining and Grinding for Curved Surfaces by the Use of It, International Journal of the Japan Society for Precision Engineering, Vol.26, No.2, 1992, 102-107. 36 82
Zhong, Z., Venkatesh, V.C., Generation of parabolic and toroidal surfaces on silicon and silicon based compounds using diamond cup grinding wheels, CIRP ANNALS- MANUFACTURING TECHNOLOGY, VOL.43/1, 1994,.323-326. 37 Z.W. Zhong, X C Shan, S J Wong, Roll-to-roll largeformat slot die coating of photosensitive resin for UV embossing, Microsystem Technologies 17 (12) (2011) 1703-1711. 38 83
Z.W. Zhong, X C Shan, S J Wong, Roll-to-roll largeformat slot die coating of photosensitive resin for UV embossing, Microsystem Technologies 17 (12) (2011) 1703-1711. 39 X.C. Shan, Voytekunas V. Yu, M. Mohaime, Z.W. Zhong, S.J. Wong, S.K. Lau, W.J. Lin and C.W. Lu, Process Study on Roll-to-Roll Ultraviolet (UV) Embossing, Proceedings of 12th Electronics Packaging Technology Conference, 2010, Singapore, 231-235. 40 84
Z.W. Zhong, X C Shan, Microstructure formation via roll-toroll UV embossing using a flexible mould made from a laminated polymer copper film, Journal of Micromechanics and Microengineering 22 (8) (2012) 085010 (12pp) 41 M.C.G. Lim, Z.W. Zhong, Dynamical behavior of copper atoms in a carbon nanotube channel, Carbon 49 (3) (2011) 996-1005. Atomic arrangement of copper atoms in a carbon nanotube channel under electromigration conditions 42 85
M.C.G. Lim, Z.W. Zhong, The effect of carbon nanotube chirality on the spiral flow of copper atoms in their cores, Materials Chemistry and Physics 137 (2) (2012) 519-531. 43 86