ML Measurement Parameter Control for Variable Object Placement
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Solution Overview
Problem
Existing measurement systems face challenges in efficiently determining the placement position of objects with variations in placement positions, sizes, and product types, leading to increased takt time and potential sensor damage due to fixed low speeds required to accommodate variations.
Innovation Solution
A measurement operation parameter adjustment apparatus and method utilizing a machine learning device that observes and learns from measurement operation parameter data and time data to adjust measurement parameters, such as start position and travel speed, based on current environmental states, enabling efficient placement position measurement across varying conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the measurement operation start position is set with a margin to accommodate placement position variations, then the system can handle variations in object placement, but the takt time increases due to the conservative positioning
Solution Approach 1:
The measurement operation start position is dynamically adjusted based on the actual placement position detected by the sensor. Instead of using a fixed conservative position, the system determines the optimal start position for each measurement operation based on real-time detection of the object's actual location, allowing the system to adapt to variations without increasing takt time.
Solution Approach 2:
The system uses feedback from the sensor detection to adjust the measurement operation parameters. The sensor detects the actual placement position, and this information is fed back to the control device, which then adjusts the start position and travel speed for the measurement operation, creating a closed-loop system that optimizes performance.
2Reliability
If the sensor travel speed is set to a fixed low speed to prevent collision damage, then sensor and object safety is improved, but measurement efficiency decreases due to increased measurement time
Solution Approach 1:
The sensor travel speed is dynamically adjusted based on the detected placement position and individual sensor characteristics. The control device determines an appropriate travel speed for each measurement operation, allowing faster speeds when safe and slower speeds when needed to prevent collision, rather than using a fixed conservative speed for all operations.
Solution Approach 2:
The system changes the travel speed parameter based on detected conditions and sensor individual differences. By adjusting this critical parameter dynamically, the system optimizes the balance between safety and efficiency, allowing high-speed measurement when conditions permit and reducing speed only when necessary to prevent damage.
3Adaptability or versatility
If the measurement operation start position is uniformly set with a margin, then all object types can be measured, but it is difficult to reduce takt time to match actual production circumstances
Solution Approach 1:
The measurement operation start position is dynamically determined based on the actual placement position of each object rather than using a uniform conservative position. The control device adjusts the start position for each measurement operation based on real-time detection, enabling the system to handle different product types while optimizing measurement speed to match actual production requirements.
4Productivity
If the sensor travel speed varies among individual sensors, then each sensor can operate at its optimal speed, but collision risk increases without proper compensation
Solution Approach 1:
The system incorporates feedback mechanisms that detect actual placement positions and sensor response characteristics. This feedback information is used to adjust travel speed parameters for each individual sensor, compensating for individual differences while maintaining safe operation. The control device uses detected information to determine appropriate speeds that maximize productivity while preventing collision.
Data Source
AI summary
A measurement operation parameter adjustment apparatus that enables efficient measurement of the placement position of an object to be measured even in the case where there are variations in the placement positions, the sizes, and the product types of objects to be measured includes a machine learning device. The machine learning device observes measurement operation parameter data representing the measurement operation parameter of the measurement operation and measurement time data representing time taken to perform the measurement operation as a state variable representing a current environmental state and performs learning or decision-making using a learning model obtained by modeling adjustment of the measurement operation parameter based on the state variable.