A CNN prediction method for belt grinding tool wear in a polishing process utilizing 3-axes force and vibration data

• Main Authors: Caesarendra, Wahyu, Triwiyanto, Triwiyanto, Pandiyan, Vigneashwara, Glowacz, Adam, Permana, Silvester Dian Handy, Tjahjowidodo, Tegoeh
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Electrical and electronic engineering; Convolutional Neural Network; Manufacturing
• Online Access: https://hdl.handle.net/10356/151888
• Institution: Nanyang Technological University

This paper presents a tool wear monitoring methodology on the abrasive belt grinding process using vibration and force signatures on a convolutional neural network (CNN). A belt tool typically has a random orientation of abrasive grains and grit size variation for coarse or fine material removal. De...