New insights into the methods for predicting ground surface roughness in the age of digitalisation

Yuhang Pan, Ping Zhou, Ying Yan, Anupam Agrawal, Yonghao Wang, Dongming Guo, Saurav Goel

Research output: Journal PublicationReview articlepeer-review

55 Citations (Scopus)

Abstract

Grinding is a multi-length scale material removal process that is widely employed to machine a wide variety of materials in almost every industrial sector. Surface roughness induced by a grinding operation can affect corrosion resistance, wear resistance, and contact stiffness of the ground components. Prediction of surface roughness is useful for describing the quality of ground surfaces, evaluate the efficiency of the grinding process and guide the feedback control of the grinding parameters in real-time to help reduce the cost of production. This paper reviews extant research and discusses advances in the realm of machining theory, experimental design and Artificial Intelligence related to ground surface roughness prediction. The advantages and disadvantages of various grinding methods, current challenges and evolving future trends considering Industry-4.0 ready new generation machine tools are also discussed.

Original languageEnglish
Pages (from-to)393-418
Number of pages26
JournalPrecision Engineering
Volume67
DOIs
Publication statusPublished - Jan 2021
Externally publishedYes

Keywords

  • Digital manufacturing
  • Digitalisation
  • Industry-4.0
  • Precision grinding
  • Prediction
  • Quality

ASJC Scopus subject areas

  • General Engineering

Fingerprint

Dive into the research topics of 'New insights into the methods for predicting ground surface roughness in the age of digitalisation'. Together they form a unique fingerprint.

Cite this