Location History:
- Dalian, CN (2022)
- Liaoning, CN (2023 - 2024)
Company Filing History:
Years Active: 2022-2024
Title: Innovations of Mengmeng Niu in Surface Roughness Prediction
Introduction
Mengmeng Niu is a prominent inventor based in Dalian, China. He has made significant contributions to the field of machining technology, particularly in the prediction of surface roughness and tool wear. With a total of three patents to his name, Niu's work is at the forefront of innovation in this area.
Latest Patents
Niu's latest patents include an "On-line prediction method of surface roughness of parts based on SDAE-DBN algorithm." This method utilizes a tri-axis acceleration sensor to collect vibration signals during the cutting process. Additionally, a microphone captures noise signals, allowing for the elimination of trend terms and smoothing of the data. The processed signals are then input into a deep belief network to predict the current surface roughness of the machined parts.
Another notable patent is the "Prediction method of part surface roughness and tool wear based on multi-task learning." This method involves collecting vibration signals and measuring the corresponding surface roughness and tool wear. A multi-task prediction model is constructed using deep belief networks, enabling the prediction of both surface roughness and tool wear based on the input vibration signals.
Career Highlights
Mengmeng Niu is affiliated with Dalian University of Technology, where he continues to advance research in machining technology. His innovative approaches have garnered attention in the academic and industrial sectors, contributing to improved manufacturing processes.
Collaborations
Niu has collaborated with notable colleagues, including Kuo Liu and Mingrui Shen. Their joint efforts have further enhanced the research and development of predictive methods in machining technology.
Conclusion
Mengmeng Niu's contributions to the field of machining technology through his innovative patents demonstrate his expertise and commitment to advancing industry standards. His work in predicting surface roughness and tool wear is paving the way for more efficient manufacturing processes.