Laser Consumable Lifetime Prediction Using Pulse-Based Training Data
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Solution Overview
Problem
Existing methods for predicting the lifetime of consumables in laser devices, such as laser chambers and line narrowing modules, are inaccurate due to individual variations and rely heavily on empirical predictions by field service engineers, leading to potential early or late replacements that can disrupt production.
Innovation Solution
A machine learning-based approach is employed to create training data and models that predict the lifetime of consumables by associating lifetime-related information with oscillation pulses, using a consumable management server to analyze data and provide precise replacement timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If empirical prediction methods by field service engineers are used, then the prediction process is simple and quick, but the prediction accuracy is low and unreliable
Solution Approach 1:
The patent replaces the mechanical/empirical prediction method (field service engineer judgment) with an automated information processing system that collects operational data, calculates usage hours, and determines replacement timing through computer-based algorithms, thereby improving accuracy while maintaining ease of operation
Solution Approach 2:
The laser device automatically collects its own operational data (oscillation pulse numbers, operational hours) and uses this data to determine consumable replacement timing without requiring external expert judgment, enabling the system to self-monitor and self-manage maintenance scheduling
2Ease of manufacture
If fixed replacement schedules are used, then maintenance planning is simple, but early or late replacements occur causing production disruption
Solution Approach 1:
The patent transitions from static fixed replacement schedules to dynamic replacement timing that adapts to actual device usage patterns. The system continuously monitors operational data and adjusts replacement schedules based on real-time usage hours and pulse counts, preventing both early and late replacements while maintaining simple maintenance planning
Solution Approach 2:
The system implements feedback by continuously collecting operational data (oscillation pulses, usage hours) and using this information to determine optimal replacement timing. This closed-loop approach ensures replacements occur precisely when needed, avoiding production disruptions while keeping maintenance planning straightforward
Data Source
AI summary
A training data creation method according to an aspect of the present disclosure is used for machine learning of a learning model for predicting lifetime of a consumable of a laser device. The method includes acquiring first lifetime-related information including data of at least one lifetime-related parameter of the consumable recorded in association with each of numbers of oscillation pulses during a period from start of use to replacement of the consumable, determining a first deterioration degree of the consumable based on the number of oscillation pulses, determining a second deterioration degree of the consumable based on the at least one lifetime-related parameter, determining a third deterioration degree of the consumable based on the first deterioration degree and the second deterioration degree, and creating training data in which the first lifetime-related information and the third deterioration degree are associated with each other.