PCB Milling Anomaly Detection Using Speed and Current Signals
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Solution Overview
Problem
Conventional milling machines fail to detect anomalies and potential failures in real-time, leading to unexpected downtimes and reject products, as they only recognize errors after they occur, causing delays and inefficiencies in manufacturing processes.
Innovation Solution
A method and device using a trained adaptive algorithm, specifically a neural network, to monitor the rotational speed and electrical supply current of milling machines, detecting anomalies and predicting failures, allowing for proactive maintenance and minimizing operational downtimes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional error detection methods are used, then errors are detected after they occur, but this leads to unexpected downtimes and reject products
Solution Approach 1:
The system performs preliminary analysis of operational data using a trained learning algorithm to detect anomalies before they develop into actual errors. By continuously monitoring parameters like rotational speed and supply current, the system identifies deviation patterns early, enabling preventive maintenance before machine failure or product rejection occurs
Solution Approach 2:
The system implements continuous feedback loops where operational data is constantly fed into the learning algorithm, which compares current operations against learned normal patterns. When deviations are detected, the system provides immediate feedback through notifications to operators, enabling real-time corrective action before errors manifest
2Reliability
If real-time monitoring with learning algorithms is implemented, then failures can be predicted early, but this increases device complexity
Solution Approach 1:
The learning algorithm performs self-training by automatically learning normal operational patterns from historical data without requiring manual programming of detection rules. The system self-adjusts to different machine states and workpiece types, reducing the need for complex configuration and maintenance while improving prediction accuracy over time
Solution Approach 2:
The monitoring system is designed to handle multiple machine types and operational parameters through a single unified learning algorithm framework. The system can adapt to different milling machines, workpiece materials, and operational conditions using the same core technology, reducing overall system complexity while maintaining versatility
Data Source
Figure 1~3
AI summary
The invention relates to a method for monitoring a milling process of a printed circuit board, having the steps of: (a) detecting (S1) the rotational speed of a milling head (2) of a milling machine (1) and at least one other operating parameter of the milling machine (1) during the milling process, wherein the other operating parameter is an electric supply current for operating the milling machine, and (b) analyzing (S2) the detected rotational speed and the detected operating parameter using a trained adaptive algorithm for detecting anomalies during the milling process.