US 12,135,258 B2
Tool condition monitoring system
Moslem Azamfar, Cincinnati, OH (US); Vibhor Pandhare, Cincinnati, OH (US); Marcella Miller, Cincinnati, OH (US); Fei Li, Cincinnati, OH (US); Pin Li, Cincinnati, OH (US); Jaskaran Singh, Cincinnati, OH (US); Hossein Davari, Cincinnati, OH (US); Jay Lee, Mason, OH (US); Joseph Frank Sanders, Jr., Erlanger, KY (US); and Keita Yamaguchi, Aichi (JP)
Assigned to University of Cincinnati, Cincinnati, OH (US); and Mazak Corporation, Florence, KY (US)
Filed by University Of Cincinnati, Cincinnati, OH (US); and Mazak Corporation, Florence, KY (US)
Filed on Dec. 15, 2021, as Appl. No. 17/551,652.
Claims priority of provisional application 63/125,625, filed on Dec. 15, 2020.
Prior Publication US 2022/0187164 A1, Jun. 16, 2022
Int. Cl. G01M 13/00 (2019.01); B23Q 17/24 (2006.01); G06F 30/27 (2020.01)
CPC G01M 13/00 (2013.01) [B23Q 17/2457 (2013.01); G06F 30/27 (2020.01)] 17 Claims
OG exemplary drawing
 
1. A system for monitoring a tool health condition, comprising:
one or more processors; and
a memory coupled to the one or more processors and including program code that, when executed by the one or more processors, causes the system to:
collect first operational data from a machine while the machine is operating in a predetermined manner with a tool including a plurality of pockets, the tool being in a first known health condition defined by operatively coupling a first number of inserts into a like number of the plurality of pockets;
collect second operational data from the machine while the machine is operating in the predetermined manner with the tool in a second known health condition defined by operatively coupling a second number of inserts into a like number of the plurality of pockets, the second number being different from the first number;
extract a first plurality of features from the first operational data;
extract a second plurality of features from the second operational data;
generate a training dataset from the first plurality of features and the second plurality of features; and
train an analytic model to determine the health condition of the tool using the training dataset.