# US 11,307,558 B2 · Machining defect occurrence prediction system for machine tool

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- Number: US 11,307,558 B2
- Document ID: US11307558B2
- Status: Granted
- Kind: B2
- Application number: 16/801,095
- Filed: 2020-02-25
- Published: 2020-08-27
- Granted: 2022-04-19
- Earliest claimed: 2019-02-27
- Assignees: Fanuc Corporation
- Applicants: Fanuc Corporation
- Inventors: Kouya Okudera; Hiroki Mukai; Yuuya Miyahara; Yasushi Okajima; Shinichi Ogawa; Noboru Kurokami
- Web page: https://patentgrep.com/patent/US11307558B2

## CPC

- G05B 19/4065: Monitoring tool breakage, life or condition
- G06F 17/18: for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)}
- G05B 2219/33002: Artificial intelligence AI, expert, knowledge, rule based system KBS
- G05B 2219/34477: Fault prediction, analyzing signal trends
- G05B 2219/50276: Detect wear or defect tool, breakage and change tool
- G06N 20/00: Machine learning
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Provided is a defect occurrence prediction system for a machine tool that makes it possible to identify the factors causing the occurrence of defects efficiently and effectively, and predict the occurrence of the defects accurately with good precision. A defect occurrence prediction system includes an information data accumulation unit that accumulates various types of information and various types of data relating to a machining operation of the machine tool; a defective product occurrence information data extraction unit that extracts from the information data accumulation unit the various types of information and the various types of data when the defective product is produced in the machined products; and a defect occurrence prediction unit that performs a defect occurrence prediction on a basis of the various types of information and the various types of data extracted by the defective product occurrence information data extraction unit and various types of information and various types of data relating to a machining operation of the machine tool obtained in real time.

## Claims

### Claim 1

A defect occurrence prediction system of a machine tool which predicts an occurrence of a defective product in machined products by the machine tool, the defect occurrence prediction system comprising a processor, the processor being configured to:
accumulate various types of information and various types of data from at least one first sensor of a plurality of sensors relating to a machining operation of the machine tool;
extract the various types of information and the various types of data when the defective product is produced in the machined products; and
perform a defect occurrence prediction on a basis of the extracted various types of information and the extracted various types of data and various types of information and various types of data relating to a machining operation of the machine tool obtained in real time,
wherein the various types of data include measurement data of at least one of vibration and sound during machining of the machine tool obtained from at least one second sensor of the plurality of sensors,
wherein the processor is configured to perform statistical processing for the measurement data and to predict a defect occurrence by comparing data obtained by the statistical processing and the measurement data obtained in real time, and
wherein the processor issues a warning when the processor predicts a defect occurrence.

### Claim 2

A defect occurrence prediction system of a machine tool which predicts an occurrence of a defective product in machined products by the machine tool, the defect occurrence prediction system comprising a processor, the processor being configured to:
accumulate various types of information and various types of data from at least one first sensor of a plurality of sensors relating to a machining operation of the machine tool;
extract the various types of information and the various types of data when the defective product is produced in the machined products; and
perform a defect occurrence prediction on a basis of the extracted various types of information and the extracted various types of data and various types of information and various types of data relating to a machining operation of the machine tool obtained in real time,
wherein the various types of data include measurement data of at least one of a spindle motor and a servomotor during machining of the machine tool obtained from at least one second sensor of the plurality of sensors,
wherein the processor is configured to perform statistical processing for the measurement data and to predict a defect occurrence by comparing data obtained by the statistical processing and the measurement data obtained in real time, and
wherein the processor issues a warning when the processor predicts a defect occurrence.

### Claim 3

A defect occurrence prediction system of a machine tool which predicts an occurrence of a defective product in machined products by the machine tool, the defect occurrence prediction system comprising a processor, the processor being configured to:
accumulate various types of information and various types of data from at least one first sensor of a plurality of sensors relating to a machining operation of the machine tool;
extract the various types of information and the various types of data when the defective product is produced in the machined products; and
perform a defect occurrence prediction on a basis of the extracted various types of information and the extracted various types of data and various types of information and various types of data relating to a machining operation of the machine tool obtained in real time,
wherein the various types of data include image data of at least one of a machined part and a tool during machining of the machine tool obtained from at least one second sensor of the plurality of sensors,
wherein the processor is configured to predict a defect occurrence by using at least threshold determination on the image data, and
wherein the processor issues a warning when the processor predicts a defect occurrence.

### Claim 4

A defect occurrence prediction system of a machine tool which predicts an occurrence of a defective product in machined products by the machine tool, the defect occurrence prediction system comprising:
an information data accumulation unit that accumulates various types of information and various types of data relating to a machining operation of the machine tool;
a defective product occurrence information data extraction unit that extracts from the information data accumulation unit the various types of information and the various types of data when the defective product is produced in the machined products; and
a defect occurrence prediction unit that performs a defect occurrence prediction on a basis of the various types of information and the various types of data extracted by the defective product occurrence information data extraction unit and various types of information and various types of data relating to a machining operation of the machine tool obtained in real time,
wherein the various types of data include measured data detected by an environmental sensor that measures, among air flow, temperature, humidity, pressure, illuminance, and air pollution which are environmental elements in a factory where the machine tool is installed, the environmental elements including at least the air flow, and
wherein the defect occurrence prediction unit is configured to predict a defect occurrence by using at least one of machine learning, threshold determination, and statistical processing for the measurement data.