AI Feed Control Server for Adaptive Machine Tool Load Balancing
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Solution Overview
Problem
Existing machine tools lack efficient methods for dynamically adjusting feed amounts and tool rotations to optimize production based on real-time operational conditions and changing demands, leading to suboptimal productivity and potential operational inefficiencies.
Innovation Solution
A server utilizing AI models to receive load values from machine tools, determine recommended feed changes, tool rotations, and cut-in amounts, and transmit control signals to adjust operations accordingly, considering factors like worker cycles, product types, and production schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional fixed feed amount control is used in machine tools, then the control system is simple and easy to operate, but productivity is suboptimal and cannot adapt to changing production demands
Solution Approach 1:
The patent introduces a server as an intermediary component between the machine tool and the control system. The server receives operational data from the machine tool, processes it through AI models to determine optimal feed amounts, and sends control signals back. This intermediary architecture enables advanced adaptive control capabilities without requiring complex embedded systems within the machine tool itself, thus improving productivity while managing system complexity.
Solution Approach 2:
The patent replaces traditional mechanical or fixed-program control systems with an AI-based adaptive control system. Instead of using predetermined feed schedules or simple feedback mechanisms, the system employs machine learning models that continuously learn from operational data and dynamically optimize feed amounts, enabling the system to adapt to varying production conditions and improve productivity.
2Productivity
If AI-based adaptive feed amount control is implemented, then production efficiency is optimized and delivery deadlines are met, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the control system into distinct functional modules: data collection from machine tools, AI model processing on the server, and control signal generation. This segmentation allows each component to be optimized independently and facilitates easier maintenance and upgrading. The AI models are trained separately and deployed as services, reducing the complexity burden on the real-time control portion of the system.
Solution Approach 2:
The patent implements preliminary action by pre-training AI models offline using historical operational data before deployment. The models are trained in advance to recognize patterns and optimize feed amounts for various machining conditions. This preliminary training eliminates the need for complex real-time learning computations during actual machining operations, reducing computational requirements while maintaining high production efficiency.
3Reliability
If real-time operational data is continuously monitored and processed, then abnormal operations are detected and productivity is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent implements a feedback mechanism where the server continuously receives operational data from machine tools, processes it through AI models, and sends control signals back to adjust feed amounts in real-time. This closed-loop feedback system enables abnormal operations to be detected and corrected promptly, improving reliability. The feedback is optimized to process only critical deviations from normal operation patterns, reducing data processing time while maintaining effective monitoring.
Data Source
AI summary
A server for providing an artificial intelligence (AI)-based adaptive feed amount, includes a memory, a communication module that communicates with at least one machine tool, and at least one processor that communicates with the memory and the communication module, wherein the at least one processor is configured to receive an initial reasonable load value from a first machine tool in a unit factory, to obtain at least one of a first recommended feed change amount, a first recommended tool or base-material rotation speed, or a first recommended cut-in amount by inputting a target load value and the initial reasonable load value of the first machine tool to a first AI model, and to transmit, to the first machine tool.