Sheet Metal Production Graphs for Dynamic Machine Capability Matching
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Solution Overview
Problem
Conventional production planning in sheet metal processing is often manual, leading to suboptimal material usage, machine tool utilization, and high production costs, with difficulty in responding to unforeseen events like machine tool failures.
Innovation Solution
A method involving a machine tool matrix and artificial intelligence, such as neural networks, to determine an optimized machining sequence for sheet metal components, considering machine tool capabilities and component properties, and an online platform for decentralized control and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual production planning is used, then production control can be performed with simple methods, but material usage, machine tool utilization, and production costs are suboptimal
Solution Approach 1:
The patent replaces manual mechanical production planning with an automated computer-based system that uses algorithms and machine learning models to generate optimized production schedules, thereby improving productivity while managing complexity through digital automation
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring production data, machine tool status, and material availability to dynamically adjust and optimize production plans, enabling the complex automated system to adapt to changing conditions and maintain high efficiency
2Reliability
If conventional production planning methods are used, then planning processes are simple to implement, but responsiveness to machine tool failures and unforeseen events is poor
Solution Approach 1:
The production planning system is designed to be dynamic rather than static, automatically adjusting schedules and resource allocation in response to machine tool failures and unforeseen events, thereby improving reliability while the automated nature manages the complexity of real-time adjustments
3Ease of manufacture
If automated production planning with machine learning models is implemented, then production costs and manufacturing time can be optimized, but system complexity increases
Solution Approach 1:
Complex algorithmic systems and machine learning models replace traditional manual planning methods, enabling optimized production schedules that reduce costs and manufacturing time while the automated computational approach manages the inherent complexity of these advanced models
Data Source
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AI summary
The invention relates to a method (10) for controlling sheet metal processing with multiple processing steps (26), wherein at least one production graph (36a) is created in a machine tool matrix (30), in which the individual processing steps (26) are assigned a respective at least one machine tool (28a-28j) suitable for carrying out the respective processing step (26), wherein the assignment occurs via a comparison of the basic processing capabilities of the respective machine tool (28a-28j) and its capability parameters with the basic characteristics and characteristic parameters of the sheet metal parts (12) to be produced. An online platform, in particular in the form of an online marketplace, can be provided for inputting the data required for the method (10) and for outputting the data generated by the method (10).