Investigation of Precision Machined Surface with Advanced Signal Processing Technique


The use of optical products fabricated using ultra-precision cutting processes have been widely utilized in optics, photonics and in biomedical engineering. This technique mainly utilizes potassium dihydrogen phosphate (KDP) crystal optical switch for harmonic frequency conversion in laser systems. The performances of the optical components made from the KDP crystals depend on their surface quality. Recent studies have already established that the texture features have a great impact on the quality of KDP crystal surface. Furthermore, an experimental study on surface roughness in rotary ultrasonic machining process of KDP crystal has indicated that the surface quality is usually affected by different machining variables. To this note, several techniques have been advanced in a bid to analyze the performance of texture features of machined surface. Unfortunately, the performance of prediction based on the popular bidimensional empirical mode decomposition (BEMD) method that is closely related to the surface quality, and the surface profile analysis by denosing embedded BEMD method is yet to be reported.

Recently, a team of researchers at Harbin Institute of Technology: Dr. Lei Lu, Professor Jihong Yan, Dr. Wanqun Chen, Prof. Shi An developed a novel spatial-frequency analysis method based on improved bidimensional empirical mode decomposition to identify and evaluate the KDP surface profile. For this purpose, the team aimed at embedding a denoising technique in the sifting iteration process to remove redundant information in decomposed sub-surfaces. Their work is currently published in the journal, Applied Surface Science.

The research method employed commenced with the processing of KDP crystal by use of an ultra-precision fly-cutting machine. Next, the researchers undertook a series of fly-cutting tests on the ultra-precision machine tool. The influence of the surface characteristics on power spectral density were then determined. Later, the improved BEMD based texture features detecting was then incorporated in to the system and analyzed. Eventually, a comparative study with the Two-dimensional wavelet transform method on surface texture analysis was undertaken.

The authors mainly observed that by embedding a two-dimensional denoising technique in the sifting process, the end effect and iteration errors were removed to improve the decomposed intrinsic mode functions by the BEMD method. Additionally, they noted that from the comparative study with power spectral density method, traditional BEMD method, and Two-dimensional wavelet transform technique of the machined surface demonstrated that, the cutting path was clearly identified at a specific frequency, and the feeding texture feature was detected at another frequency.

The Jihong Yan and colleagues’ study presented the development of an enhanced and improved BEMD method for texture feature investigation of machined KDP crystal surface in spatial frequency domain. They observed that the two high amplitude areas were successfully separated and the gradient analysis revealed the development of gradient information of machined surface. Moreover, the improved IBEMD was seen to evade shortcomings of selecting model parameters. Altogether, the proposed method is a promising tool for the application of online monitoring and optimal control of precision machining process.

Precision Machined Surface with Advanced Signal Processing Technique - Advances in Engineering

About the author

Dr. Lei Lu received his PhD degree in the School of Mechatronics Engineering at the Harbin Institute of Technology (2016). He is currently a post-doctoral researcher at the University of Melbourne, Australia. He ever had visiting research in the Department of Mechanical Engineering at the University of British Columbia, Canada, from 2013 to 2015.

His research is mainly focused on data mining, precision machining, biomedical data processing, etc. His papers appeared in high reputation journals, including Signal Processing, J. Sound Vib., Appl. Surf. Sci., etc. He has participated in several projects funded by the NSFC (National Science Foundation of China), and currently he is a PI of NSFC project regarding advanced signal processing methods and the applications on intelligent systems.

About the author

Prof. Jihong Yan is a Professor (since 2005) in Industrial Engineering at Harbin Institute of Technology (HIT), and the deputy dean of School of Mechatronics Engineering. She received her Ph.D. from Harbin Institute of Technology in 1999. Then she joined Tsinghua University (from 1999 to 2001), the University of Wisconsin (from 2001 to 2004) and Pennsylvania State University (from 2004 to 2005) as a postdoctoral researcher.

Dr. Yan is the director of National High-end Equipment Manufacturing Virtual and Simulation Experiment Center, leader of Research Oriented Teaching Innovation Team for High-end Equipment Manufacturing of the Ministry of Industry and Information Technology of China, vice chairman of Production System Committee of Chinese Mechanical Engineering Society, and chairman of Industrial Engineering Professional Committee of the Mechanical Engineering Society of Heilongjiang Province.

Her research is mainly focused on industrial big data analysis, sustainable manufacturing and intelligent maintenance. As a PI, Dr. Yan has worked on and accomplished 20 projects funded by the NSFC, joint NSF-NSFC projects, national high-tech projects, high-tech funding from industries, etc. She has published 2 books in Chinese and 1 in English (John-Wiley & Sons, 2015) and has authored and co-authored over 100 research papers.

About the author

Dr. Wanqun Chen is an associate professor in Harbin institute of technology. Previously he worked at Newcastle University, University of Strathclyde and University of Huddersfield in UK, where he performed research under two number of EPSRC projects in developing precision machine tools and vibration assisted micro milling. Currently his work in precision manufacturing is focused on ultra-precision machine design, dynamic analysis and ultra-precision machining.

He has published more than 60 peer reviewed papers, contributed to 3 book chapters and held 6 patents. One of his paper won the B. John Davies best paper prize for papers published in IJAMT 2015. Currently he is working on a NSFC project as PI and a number of other projects as co-PI.


Lei Lu, Jihong Yan, Wanqun Chen, Shi An. Investigation of KDP crystal surface based on an improved bidimensional empirical mode decomposition method. Applied Surface Science , volume 433 (2018) page 680–688

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