Open AccessData‐driven framework for the prediction of cutting force in turning
Author(s) -
Chatterjee Kaustabh,
Zhang Jian,
Dixit Uday Shanker
Publication year - 2020
Publication title -
iet collaborative intelligent manufacturing
Language(s) - English
Resource type - Journals
ISSN - 2516-8398
DOI - 10.1049/iet-cim.2019.0055
Subject(s) - machining , computer science , process (computing) , reliability (semiconductor) , cloud computing , industrial engineering , filter (signal processing) , data mining , power (physics) , engineering , mechanical engineering , physics , quantum mechanics , computer vision , operating system
Cutting force is one of the most important parameters for assessing power consumption and tool wear. This work attempts to utilise the concept of data analytics for collecting and building a data warehouse in a cloud‐based platform called Central Database Repository (CDR), which can share the information about the machining forces in turning operations directly to the process planner or shop floor operator. The estimation of cutting force is accomplished based on the input of process parameters. CDR uses multiple‐linear regression on the data collected and stored in the data bank. Uncertainties in machining operations are taken care of in a separate cloud‐based platform known as mini‐repository. This study estimates the interval of cutting force with a certain level of confidence and a new concept of dynamic reliability is attached to it. The information from the CDR can be accessed from anywhere across the globe. There is a provision to update the database based on the feedback and filter out unnecessary data by evaluating Cook's distance. The proposed framework is explained through illustrative case studies.