Conveying Machine Speed Control for Article Stability
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Solution Overview
Problem
Conventional conveying machines face challenges in conveying articles at higher speeds without causing impacts, spills, shape loss, or deviation, as they do not dynamically adjust speeds based on the article's state during acceleration and deceleration.
Innovation Solution
A controller equipped with a machine learning device that observes and learns from conveyance operation data and article state data to determine optimal conveyance speeds, using a state observation unit, determination data acquisition unit, and learning unit to associate these variables and adjust the conveyance operation accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the conveyance speed is increased to improve productivity, then the conveyance efficiency is improved, but the conveyance article may fall, receive impact, spill, lose its shape, or deviate from its position
Solution Approach 1:
The conveying machine implements dynamic speed adjustment based on the state of the conveyance article. The control unit modifies acceleration and deceleration speeds in real-time according to detected article states (such as whether the article is stacked, liquid-filled, or fragile), allowing the system to operate at higher speeds when safe and reduce speeds when necessary to prevent damage
Solution Approach 2:
The system incorporates a detection unit that continuously monitors the state of the conveyance article and provides feedback to the control unit. This feedback mechanism enables the control unit to adjust conveyance parameters dynamically, resolving the contradiction between high-speed operation and article stability by making real-time decisions based on actual article conditions
2Reliability
If the conveyance speed is set appropriately to prevent impact and damage, then the conveyance article stability is maintained, but the conveyance speed cannot be increased sufficiently
Solution Approach 1:
The system changes operational parameters (acceleration speed and deceleration speed) based on the detected state of the conveyance article. Different article states trigger different parameter settings, allowing the system to optimize both speed and stability by adapting parameters to match the specific conveyance conditions
Data Source
AI summary
A machine learning device provided in a control unit observes, as state variables representing a current state of an environment, conveyance operation data indicating a state of a conveyance operation of a conveying machine and conveyance article state data indicating a state of the conveyance article, and acquires, as determination data, conveyance speed determination data indicating an appropriateness determination result relating to a conveyance speed of the conveyance article and conveyance article state determination data indicating an appropriateness determination result relating to variation in the state of the conveyance article. The conveyance operation data and the conveyance article state data are then learned in association with each other by using the state variables and the determination data.