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Machine learning method, machine learning device, machine learning program, communication method, and film-forming device

A machine learning and film forming device technology, applied in machine learning, instruments, general control systems, etc., can solve problems such as difficult and easy film forming conditions

Pending Publication Date: 2022-02-22
KOBE STEEL LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, it is difficult to easily determine appropriate film-forming conditions

Method used

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  • Machine learning method, machine learning device, machine learning program, communication method, and film-forming device
  • Machine learning method, machine learning device, machine learning program, communication method, and film-forming device
  • Machine learning method, machine learning device, machine learning program, communication method, and film-forming device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach 1

[0026] figure 1 It is an overall configuration diagram of a film formation apparatus applied to the machine learning system according to Embodiment 1. The film forming device 30 is a device for forming a hard film on a workpiece (object to be plated), which is a base material of a cutting tool, by an arc ion plating method. The arc ion plating method is a kind of ion plating method that uses vacuum arc discharge to evaporate solid materials. The arc ion plating method is suitable for forming a film on a cutting tool because the ionization rate of the vaporized material is high and a film having excellent adhesion can be formed. The hard film is, for example, TiN, TiAlN, TiCN, CrN or the like.

[0027] The film forming apparatus 30 includes a vacuum exhaust system 510 , a heating and cooling system 520 , an evaporation source system 530 , a workbench system 540 , a process gas system 550 , an etching system 560 , and a chamber 570 .

[0028] The vacuum exhaust system 510 inc...

Embodiment approach 2

[0092] The film forming apparatus 30 of Embodiment 2 is an apparatus for forming a decorative film on a workpiece for the purpose of improving the decorativeness. The workpiece is, for example, accessories such as watches and necklaces, casings of mobile phones, bumpers of automobiles, and the like. The decorative film is, for example, TiN, TiAlN, TiCN, CrN, DLC (diamond-like carbon), or the like. The machine learning system of Embodiment 2 is a system that performs machine learning on appropriate film-forming conditions for the decorative film.

[0093] In the second embodiment, the same components as those in the first embodiment are assigned the same reference numerals, and descriptions thereof are omitted. In addition, in Embodiment 2, the structure of the film forming apparatus 30 is the same as figure 2 Similarly, the processing of the film forming device 30 is the same as image 3 Similarly, an example of film-forming conditions is the same as Figure 4 same. In E...

Embodiment approach 3

[0097] The film forming apparatus 30 of Embodiment 3 is an apparatus for forming a protective film for protecting a workpiece. The workpiece is, for example, a cutting tool, a mold for injection molding, a propeller, or the like. The protective film is, for example, TiN, TiAlN, TiCN, CrN or the like. The machine learning system of Embodiment 3 is a system that performs machine learning on appropriate film-forming conditions for a protective film.

[0098] In the third embodiment, the same components as those in the first embodiment are assigned the same reference numerals, and descriptions thereof are omitted. In addition, in Embodiment 3, the structure of the film forming apparatus 30 is the same as figure 2 Similarly, the processing of the film forming device 30 is the same as image 3 Similarly, an example of film-forming conditions is the same as Figure 4 same. In Embodiment 3, a major difference from Embodiment 1 lies in physical quantities.

[0099] Figure 7 It...

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Abstract

In the present invention: state variables including at least one physical quantity pertaining to film formation performance evaluation and at least one film formation condition are observed; compensation for determined results for the at least one film formation condition is calculated on the basis of the state variables; a function for determining the at least one film formation condition from the state variables is updated on the basis of the compensation; the film formation condition that yields the greatest compensation is determined by repeatedly updating the function; the at least one film formation condition is at least one from among a first parameter pertaining to a vacuum exhaust system, a second parameter pertaining to a heating / cooling system, a third parameter pertaining to a vapor source system, a fourth parameter pertaining to a table system, and a fifth parameter pertaining to a process gas system; and the at least one physical quantity is at least one from among a film quality property, a mechanical property, and a physical property pertaining to a coating film.

Description

technical field [0001] The present invention relates to a technique for learning film-forming conditions by machine learning. Background technique [0002] In recent years, in order to manufacture cutting tools with high wear resistance, hard films such as TiN, TiAlN, and CrN are formed by physical vapor deposition (PVD) on the base material of the cutting tool (for example, Patent Document 1 ). In order to manufacture a tool with high wear resistance, it is required to determine film formation conditions appropriately. [0003] However, conventionally, film formation conditions have been determined by the long experience of skilled technicians. Therefore, it is difficult to easily determine appropriate film-forming conditions. [0004] prior art literature [0005] patent documents [0006] Patent Document 1: Japanese Patent Laid-Open Publication No. 2014-114507 Contents of the invention [0007] The present invention was made to solve the above-mentioned problems, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): C23C14/32C23C14/54
CPCC23C14/32C23C14/54C23C14/325C23C14/541C23C14/52G06N20/00C23C14/24G05B13/0265
Inventor 国末晃伸高桥哲也中岛洋介
Owner KOBE STEEL LTD