Development of grinding intelligent monitoring and big data-driven decision making expert system towards high efficiency and low energy consumption: experimental approach

Jinling Wang, Yebing Tian (), Xintao Hu, Zenghua Fan, Jinguo Han and Yanhou Liu
Additional contact information
Jinling Wang: Shandong University of Technology
Yebing Tian: Shandong University of Technology
Xintao Hu: Shandong University of Technology
Zenghua Fan: Shandong University of Technology
Jinguo Han: Shandong University of Technology
Yanhou Liu: Shandong University of Technology

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 3, No 5, 1013-1035

Abstract: Abstract Grinding has been extensively applied to meet the urgent need for tight tolerance and high productivity in manufacturing industries. However, grinding parameter settings and process control still depend on skilled workers’ engineering experience. The process stability in complicated non-uniform wear can't be guaranteed. Moreover, it is impossible to obtain energy-saved grinding strategies. Intelligent monitoring methods are well-recognized to help conquer present trial–error processing deficiencies. However, discrete manufacturing companies have to face increasing difficulties to identify the monitored big data and make credible decisions directly. A decision-making expert system driven by monitored power data (EconG©) is thus developed. EconG© provides a 4-level database structure to efficiently manage multi-source heterogeneous data. Signal conditioning, peaks-valleys feature exaction, and compression approaches are proposed for reducing the storage volume of real-time monitored data. The data size has been reduced to 6.5% of the source. A mathematical comparison model based on the power feature is embedded to diagnose burns, which has been validated by the 16th and 55th surface grinding results. Mapping relation model from inputs, signals to outputs has been built by the power feature-extended artificial neural network algorithm. Prediction accuracy is improved by introducing adaptive control and dynamic changes in material removal. EconG© breaks a single analysis based on grinding parameters. Energy-saved grinding strategies could be intelligently acquired through the presented Pareto optimization method. In the future, a broader and deeper implementation of EconG© will guild manufacturers to respond quickly to explosive demands on intellectualization, sustainability, and flexibility in the arrived 4th industrial revolution.

Keywords: Power monitoring; Intelligent decision making; Tri-layer mapping model; Pareto optimization; Grinding database (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02089-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:35:y:2024:i:3:d:10.1007_s10845-023-02089-1

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02089-1

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().