JP2018025936A - Machine tool - Google Patents

Abstract

PROBLEM TO BE SOLVED: To solve the problem in which: even with damage to a tool at a level in which a user determines that the damage can be sharpened, when an abnormality score of actual data exceeds an abnormal score of normal data, a prior art machine tool performs abnormal processing, which leads to an increase in cost, and to provide a machine tool that can reduce the cost.SOLUTION: A user sets damage levels at which abnormal processing of displaying an alarm, retreating a tool, and stopping a machine are respectively executed, and a machine tool executed the abnormal processing according to the set values. The tool can thus be used as long as possible according to the user and situation, and cost can be reduced.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a machine tool that detects an abnormality of a rotary tool.

FIG. 2 is a block diagram illustrating an embodiment of the prior art.
The normal data preprocessing unit 11 inputs the spindle load time series data ND at the time of normal machining, and converts the data into a data NR that makes a certain section (for example, data for each rotation of the tool) as one unit. The normal data learning unit 12 inputs the data NR of a certain interval, learns with an auto encoder or a variational auto encoder so that it matches the output of the multilayer perceptron, and constructs a normal data model NM. The actual data preprocessing unit 31 receives the spindle load time-series data RD at the time of actual machining, and converts the data in a certain section into data RR that is a unit. The actual data abnormality score calculation unit 32 inputs the data RR in a certain section converted by the actual data preprocessing unit 31 to the normal data model constructed by the normal data learning unit 12, and the maximum value of the difference between the output and the input Alternatively, the average value is output as an abnormal score RS. Here, every time the abnormal score is calculated, online learning may be performed according to the magnitude of the abnormal score. When the abnormality score RS exceeds the normal abnormality score, the abnormality processing execution unit 36 performs processing such as displaying an alarm, retracting the tool, or stopping the machine.

JP-A-5-169355

Jinwon An, Sungzoon Cho. “Variational Autoencoder based Anomaly Detection using Reconstruction Probability”. SNU Data Mining Center. 2015. P 3-9

  However, in the above-described conventional form, even if the tool damage is at a level that the user still considers to be scraped, if the abnormal score of the actual data exceeds the abnormal score of the normal data, an abnormal process is performed. Therefore, there was a problem in terms of cost, such as having to replace with a new tool at an earlier time than necessary.

  Therefore, the present invention has been made in view of the above problems, and an object of the present invention is to provide a machine tool capable of reducing the cost.

In order to achieve the above object, the invention according to claim 1 of the present invention is a machine tool for detecting an abnormality of a rotary tool, and time series data collected during normal processing is grouped with data of a certain section. Normal data pre-processing means to convert to the data to be, and how far the data at the time of actual machining is different from the data at the time of normal by unsupervised learning from data that is a set of data for each fixed section at the time of normal pre-processing Normal data learning means for constructing a model capable of calculating the value, appraisal data preprocessing means for converting the data for each predetermined section into data that is collected as a unit from the appraisal data collected to appraise the damage level of the tool, and pre-appraisal Appraisal data is input to the pre-normal data model as a set of data for each fixed section of data, and output as an abnormal score based on the difference between the output and the input. Data obtained by converting the pre-assessment data into data that is a set of data for each predetermined section, and a damage level judgment means for assigning the damage state of the tool to a finite number of damage levels after one machining. Damage level learning means for constructing a model that has been learned using the previous damage level as a teacher input, and actual data preprocessing means for dividing the time-series data collected during actual machining into data that is a set of data for each predetermined section And the actual data abnormality score calculation means for inputting the data for each predetermined section at the time of the previous actual machining to the previous normal data model and outputting as an abnormal score based on the difference between the output and the input, and the previous actual data in the previous damage level learning model Damage level calculation means to input damage score and output damage level, and execute desired abnormality processing at each damage level And abnormality processing switching execution means for setting a Luke, characterized by comprising an abnormality processing switching execution means for executing the abnormality processing according to pre-injury levels.
The invention according to claim 2 is characterized in that, in the invention according to claim 1, the maximum value or the average value of the difference between the output and the input is used as the abnormality score.

  According to the present invention, the tool can be used as long as possible according to the user and the situation, and the cost can be reduced.

It is a block diagram which shows one Example of this invention. It is a block diagram which shows one form of a prior art.

FIG. 1 is a block diagram of an embodiment of the present invention.
The normal data preprocessing unit 11, the normal data abnormality score learning unit 12, the actual data preprocessing unit 31, and the actual data abnormality score calculation unit 32 are the same as those in the related art. However, although the spindle load is taken as an example as normal data and actual data, other data such as spindle speed and vibration acceleration may be used.

The appraisal data pre-processing unit 21 inputs data TD collected in order to appraise the damage level of the tool, and converts the data for each rotation of the tool into data TR.
The appraisal data abnormality score calculation unit 22 inputs the data TR for each rotation of the tool converted by the appraisal data preprocessing unit 21 to the normal data model NM constructed by the normal data abnormality score learning unit 12, and the difference between the output and the input Is output as an abnormal score TS. Here, every time the abnormal score is calculated, online learning may be performed according to the magnitude of the abnormal score.

  The damage level appraisal unit 23 determines whether it corresponds to, for example, a tool damage level divided into 10 stages by quantifying with an image data or a shape measuring instrument, etc., in which a person observes the tool in detail after one processing is completed. And assigning the damage level TL. Here, when the tool has a plurality of blades, the damage level TL may be the most damaged blade level or the total damage level of all blades.

The damage level learning unit 24 learns with a neural network using the abnormal score TS as input data and the damage level TL as teacher data, and constructs a damage level model LM.
The damage level calculation unit 33 inputs the abnormality score RS input from the actual data abnormality score calculation unit 32 to the damage level model LM constructed by the damage level learning unit 24, and outputs the damage level RL.

In the abnormal process setting unit 34, the user sets information AS indicating how many abnormal levels, such as displaying an alarm, retracting a tool, and stopping the machine, are executed.
The abnormality process switching execution unit 35 executes the abnormality process set based on the damage level RL input from the damage level calculation unit 33 and the information AS set by the abnormality process setting unit 34.

  11. Pre-processing unit for spindle load time-series data at normal time, 12.-learning unit for spindle load time-series data at normal time, 21.-Pre-processing unit for spindle load time-series data at appraisal, 22.- Anomaly score calculation unit of spindle load time series data at the time of appraisal, 23... Tool damage level appraisal unit, 24 .. Tool damage level learning unit, 31... Preprocessing unit of spindle load time series data at actual machining, 32 ..Score load time series data anomaly score calculation unit during actual machining, 33..Tool damage level calculation unit, 34..Abnormal process setting unit, 35..Abnormal process switching execution unit, 36..Abnormal process execution unit , ND ... Spindle load time series data during normal machining, NR ... Data for each rotation of tool during normal machining, NM ... Normal data model, TD ... Tool damage level Data collected for the purpose, TR・ Data for TD as a group of data per tool rotation, TL ・ ・ Tool damage level at appraisal, LM ・ ・ Damage level model, RD ・ ・ Main spindle load time series data during actual machining, RR ・ ・ Real Data that collects data for each rotation of the tool at the time of machining, RS ··· Maximum or average value of difference between normal data model output and input at actual machining, RL · · Tool damage level at actual machining , AS ... Information about how much damage level each abnormal process will be executed.

Claims (2)

A machine tool for detecting abnormalities in rotating tools,
Normal data preprocessing means for converting time-series data collected during normal processing into data that is a set of data in a certain section;
Normal data learning means for constructing a model that can calculate how far the data at the actual processing is from the data at the normal processing by unsupervised learning from the data that is a set of data for each predetermined section at the time of normal preprocessing,
Appraisal data preprocessing means for converting the data for each predetermined section from the appraisal data collected in order to appraise the damage level of the tool,
Appraisal data abnormality score calculation means for inputting data that is a set of data for each predetermined section of the pre-examination data to the pre-normal data model and outputting as an abnormal score based on the difference between the output and the input,
A damage level appraisal means for assigning the damage state of the tool to a finite number of damage levels after one machining;
Damage level learning means for constructing a model in which the pre-assessment data is converted into data that is a set of data for each predetermined section as input and the previous damage level is learned as a teacher input;
Real data pre-processing means for dividing time series data collected at the time of actual processing into data that is a set of data for each predetermined section;
Actual data abnormality score calculation means for inputting data for each predetermined section at the time of previous actual machining to the previous normal data model, and outputting as an abnormal score based on the difference between the output and the input,
A damage level calculation means for inputting the abnormal score of the previous actual data to the previous damage level learning model and outputting the damage level;
An abnormal process switching execution means for setting the number of damage levels at which each of the desired abnormal processes is executed;
What is claimed is: 1. A machine tool comprising abnormality process switching execution means for executing an abnormality process according to a previous damage level.   The machine tool according to claim 1, wherein a maximum value or an average value of a difference between output and input is used as the abnormality score.

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