Press Load Distribution Monitoring for Die Abnormality Detection
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Solution Overview
Problem
Conventional methods for detecting abnormalities in press machines are limited to stages where peak loads occur, failing to detect issues in stages with varying load timings, and cannot effectively manage die abnormalities or product accuracy defects within the allowable eccentric load range.
Innovation Solution
A press machine and method that involve detecting load values, storing them with die identification, calculating eccentric loads for predetermined angles, generating distribution data, determining abnormalities based on these loads and data, and notifying users of any detected issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional method using peak load detection is used, then press machine protection is achieved, but die abnormality and product accuracy defect detection is not possible
Solution Approach 1:
The patent segments the press cycle into multiple stages (first press stage, second press stage, etc.) and analyzes eccentric load at each stage separately. This allows detection of die abnormalities at specific working stages without requiring peak load conditions, thereby enabling precise detection of die abnormalities and product accuracy defects while maintaining press protection capability.
Solution Approach 2:
The patent performs preliminary analysis by storing load values for one cycle in association with die identification information before actual abnormality occurrence. By pre-processing and storing data, the system can later analyze eccentric load distribution across different press stages to detect early signs of die abnormality and product accuracy defects before they become critical.
2Measurement precision
If conventional peak load-based detection is used, then abnormality detection at peak stage is possible, but abnormality detection at other stages is not possible
Solution Approach 1:
The patent divides the press cycle into multiple discrete stages and calculates eccentric load for each stage separately. This segmentation enables the system to detect abnormalities at any specific stage (first press stage, second press stage, etc.) independently, providing versatile detection capability across the entire press cycle rather than being limited to peak load moments.
Solution Approach 2:
The patent introduces a new dimension of analysis by examining eccentric load not just at peak load moments but across the entire press cycle timeline. By plotting eccentric load against press angle and comparing with allowable eccentric load diagrams at multiple stages, the system adds temporal and angular dimensions to the detection process, enabling comprehensive abnormality detection throughout all working stages.
3Device complexity
If conventional method is used, then simple detection is maintained, but comprehensive die management and early detection capability is lost
Solution Approach 1:
The patent implements a feedback mechanism where detected eccentric load values at each press stage are compared with pre-stored allowable eccentric load diagrams. This feedback loop enables continuous monitoring and early detection of die abnormalities and product accuracy defects. The system provides feedback information to users about the condition of dies and product quality, enabling early action before critical failures occur, while maintaining relatively simple detection hardware.
Data Source
AI summary
A press machine includes: a detection unit that detects a load value during press working on a workpiece material; a storage unit that stores load values for one cycle of the press machine detected by the detection unit in association with identification information of a die attached to the press machine during detection of the load values; a calculation unit that calculates eccentric loads for predetermined press angles based on the stored load values for one cycle of the press machine and generates eccentric load distribution data for each die; a determination unit that determines an abnormality based on the eccentric loads for the predetermined press angles obtained based on the load values for one cycle of the press machine detected by the detection unit and the eccentric load distribution data corresponding to a die attached to the press machine during the detection of the load values; and a notification unit that notifies of the abnormality based on a determination result of the determination unit.