US 10,139,311 B2 · Computer-implemented method and system for machine tool damage assessment, prediction, and planning in manufacturing shop floor

Abstract

A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

Claims15

Dependent claims

A computer-implemented system for detecting machine tool wear, predicting machine tool failure, and manufacturing shop floor planning, comprising:

a computing device comprising a processor and configured to:
obtain a machine tool's operational data comprising positional parameters and movement parameters during a time window;
identify a plurality of machining processes of the machine tool based upon a match of the positional parameters and the movement parameters that defines the machining processes;
cluster the plurality of the machining processes into one or more process clusters, based upon a similarity of the machining processes;
detect the machine tool's wear by characterizing a trend of change in a parameter from the one or more clusters of machining processes performed by the machine tool;
and
predict the machine tool's remaining useful life by extrapolating the trend under a first-order Markov process, wherein the machine tool is replaced based on the predicted remaining useful life.

4 more elements

A computer-implemented method for detecting machine tool wear, predicting machine tool failure, and manufacturing shop floor planning, comprising:

obtaining by a computing device comprising a processor a machine tool's operational data comprising positional parameters and movement parameters during a time window;
identifying by the computing device a plurality of machining processes of the machine tool based upon a match of the positional parameters and the movement parameters that defines the machining processes;
clustering by the computing device the plurality of the machining processes into one or more process clusters, based upon a similarity of the machining processes;
detecting by the computing device the machine tool's wear by characterizing a trend of change in a parameter from the one or more clusters of machining processes performed by the machine tool;
and
predicting by the computing device the machine tool's remaining useful life by extrapolating the trend under a first-order Markov process, wherein the machine tool is replaced based on the predicted remaining useful life.

3 more elements

Dependencies are inferred from the claim text.

Computer-implemented method and system for machine tool damage assessment, prediction, and planning in manufacturing shop floor

Life of this patent

Computer-implemented method and system for machine tool damage assessment, prediction, and planning in manufacturing shop floor

Life of this patent

Abstract

Claims15