AE Sensor Monitoring for Powder Molding Machine Abnormalities
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Solution Overview
Problem
Existing machines used to mold powdery materials face challenges in detecting malfunctions or abnormalities in components, leading to potential deformation or damage that can impede production, and current maintenance strategies are either costly or inefficient.
Innovation Solution
A monitoring system utilizing acoustic emission (AE) sensors to detect abnormalities in machine components by sensing strain energy release as elastic waves, coupled with a controller to analyze signals and determine the need for maintenance before significant damage occurs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If corrective maintenance is used to replace members upon recognition of damage, then replacement cost is reduced, but machine operation rate decreases due to unexpected stoppages
Solution Approach 1:
The AE sensor detects abnormalities in members (such as rolls, shafts, or cam rails) before they cause malfunction by sensing elastic waves generated from strain energy release during deformation or damage. This preliminary detection enables planned replacement during scheduled maintenance periods, preventing unexpected production stoppages and improving machine operation rate.
2Reliability
If preventive maintenance is conducted by periodically inspecting and replacing members, then machine operation rate is maintained, but cost increases due to excessive inspections and replacement of still available members
Solution Approach 1:
The monitoring system provides real-time feedback on the actual condition of machine members through AE signal analysis. The controller determines whether each member has an indication of abnormality and outputs this information, enabling maintenance to be performed only when actually needed rather than on a fixed schedule. This feedback mechanism eliminates unnecessary maintenance activities and reduces maintenance costs while maintaining high machine operation rates.
3Reliability
If AE sensors are deployed to detect abnormalities in real-time, then machine operation rate improves by preventing unexpected failures, but device complexity increases
Solution Approach 1:
The machine members monitor their own condition by generating AE signals that indicate their structural state. The AE sensor and controller form a self-monitoring system that detects abnormalities without requiring external inspection or complex diagnostic equipment. This self-service approach enables reliable anomaly detection while keeping the monitoring system relatively simple.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables timely replacement of components, improving machine operation rates and reducing costs by detecting abnormalities early, thus ensuring efficient production of molded products.
Implementation Method 1
AE is a phenomenon that strain energy having been accumulated is released when a solid is deformed or destructed, and part of the strain energy is radiated as an elastic wave. The AE sensor senses its AE wave.
Data Source
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AI summary
A monitoring system for a molded product producing machine is configured to detect any indication of abnormality occurred to a predetermined member 14, 141, or 142 in a machine used to produce a molded product from a powdery material. The monitoring system includes an AE sensor 21 disposed in contact with or adjacent to the predetermined member 14, 141, or 142 in the machine, and a controller configured to receive a signal outputted from the AE sensor 21 during operation of the machine, determine whether the predetermined member 14, 141, or 142 has an indication of abnormality in accordance with the signal, and output information on a result of the determination.