Minimizing Unplanned Downtime and Machine Stops
MISSION
Our Reporting System for Aoki machines tackles OEE Losses by minimizing downtime due to Unplanned Stops, Slow Cycles, and Production Quality Issues. With real-time monitoring and data analysis, our system enhances machine efficiency and quality control, maximizing overall productivity.
Experience improved performance and reduced disruptions with our tailored reporting solution for Aoki machines.
What is OEE Losses?
OEE losses refer to the specific factors or events that contribute to the reduction in Overall Equipment Efficiency (OEE). These losses can be categorized into three main types:
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Availability Losses: These losses occur due to any event that causes the equipment to be unavailable for production when it was scheduled to be running. Some examples of availability losses include equipment breakdowns, planned maintenance or repairs, changeovers, and tooling adjustments. These events result in downtime, reducing the overall availability of the equipment.
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Performance Losses: Performance losses are related to factors that cause the equipment to operate at a slower speed or lower efficiency than its maximum capability. Common performance losses include slow cycles, minor stoppages, idling or reduced speed due to suboptimal machine settings, equipment wear and tear, or inadequate operator skills or training.
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Quality Losses: Quality losses occur when the equipment produces products that do not meet the required quality standards. This includes defects, rework, scrap, or any process or product-related issues that lead to a decrease in the overall quality of the output.
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By identifying and analyzing these losses, manufacturers can pinpoint specific areas for improvement and take actions to mitigate them. This analysis helps in optimizing equipment performance, reducing downtime, enhancing quality, and ultimately improving the Overall Equipment Efficiency (OEE).
Advantage of "Machine Reporting System for Aoki Machines" is for troubleshooting.
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Quick recovery from Aoki machine troubles can be done by utilizing machine data.
FEATURES
Optimizing Thresholds for Minimizing Downtime and Enhancing Product Quality
Threshold setting provides the following value:
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Detailed information on each threshold's abnormal value, including the specific value, when it occurred, and on which machine.
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Automated email notifications sent to users, ensuring prompt awareness of threshold breaches and enabling timely action.
Dashboard (Optional)
for calculating OEE (Actual Operation time)
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The dashboard displays the following values:
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Status indicating whether each machine is currently running or not.
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Operating hours recorded for each machine.
Error Root Cause Analysis with In/Out Information
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The In-Out data provides the following values:
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Snapshot of In-Out data captured when a machine problem occurs, enabling Aoki engineers to identify the root cause of the problem.
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Presentation of the most likely In-Out information that may have caused the machine to stop.
Harnessing Error Logs to Reduce Unplanned Downtime
The error log provides the following information:
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Details of the specific error that occurred, including the error description.
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Timestamp indicating when the error occurred.
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Identification of the machine on which the error was registered.
Real-time Trend Analysis of Cycle Time etc.
The real-time data displays the following values:
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Real-time data from the past few days for selected DM address values, including cycle time and oil temperature.
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Ability to export the real-time data to a CSV file for further analysis and in-depth evaluation.
System Overview
By attaching our edge device to the Aoki Machine, data acquisition from the machine begins. The acquired information is stored on a Cloud server and can be accessed through a web interface.
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When the machine encounters a stoppage, our developed algorithm automatically analyzes the status of the In/Out signals immediately preceding the stoppage. It provides suggestions on which process may be faulty and caused the machine to stop. This enables you and Aoki Engineers to swiftly identify the root cause of the problem, facilitating quick issue resolution within a short timeframe.
Use Cases
Company A
(Threshold setting)
A company has implemented our solution across four injection molding machines to monitor various parameters using our web system. The web system allows users to view the monitored data as follows:
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The collected data is stored in their local server and utilized for preventive maintenance and quality control of their products. Key factors such as molding temperature, cycle time, and other relevant parameters are checked to ensure optimal performance and quality.
Company B
(OPC-UA protocol)
A company has deployed our solution across 20 injection molding machines to monitor multiple parameters using their existing OPC-UA client. The monitored data can be accessed through the OPC-UA client software, providing the following information:
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The collected data is stored in their local server and utilized for quality control of their products. Key factors such as molding temperature, cycle time, and other relevant parameters are checked to ensure consistent product quality.
Company C
(Thermo Camera)
A company has implemented our additional solution for detecting molding temperature to enhance their quality control processes. Thermo cameras, purchased from another vendor, are utilized to capture snapshots during each molding cycle. Our solution incorporates object detection capabilities for each bottle and utilizes machine learning to calculate the maximum temperature of each bottle.
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By leveraging this technology, the company can effectively monitor and analyze the temperature distribution within the molded bottles. This enables them to ensure consistent product quality by identifying any deviations or anomalies in the molding temperature. Our solution enhances their quality control efforts and facilitates proactive measures to maintain the desired temperature parameters throughout the production process.
Partners
CONTACT
Inquiries
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