Connected Tire Preparation Using Digital Twins and Real-Time Feedback
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Solution Overview
Problem
The tire industry faces challenges in responding quickly to automaker needs, optimizing manufacturing processes to minimize waste and reduce greenhouse gas emissions while maintaining profitability, and improving tire quality.
Innovation Solution
A connected system comprising a database and multiple processing units interconnected by a network, utilizing digital twins, real-time data analysis, and automated operations to optimize tire preparation steps such as mixing, assembly, and vulcanization, minimizing waste and emissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional batch-based empirical calculations are used for tire preparation, then the process is simpler to manage, but productivity and response time to automaker needs are reduced
Solution Approach 1:
The tire preparation process is divided into multiple independent processing units (X1, X2, ..., Xn), each capable of performing specific operations such as mixing, assembly, testing, or vulcanization. This segmentation allows parallel processing of different tire elements, significantly increasing productivity while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system transitions from batch-based empirical calculations to real-time data-driven parameter optimization. Each processing unit collects and analyzes data continuously, adjusting processing parameters dynamically based on actual measurements and digital twin simulations, enabling faster response to automaker requirements.
2Manufacturing precision
If real-time data analysis and digital twins are implemented, then manufacturing precision and quality are improved, but device complexity increases
Solution Approach 1:
Digital twins are created as virtual copies of each processing unit and tire element. These digital models replicate the physical system's behavior, allowing virtual testing and optimization without affecting actual production. This enables high manufacturing precision through simulated parameter optimization while keeping the physical system relatively simple.
Solution Approach 2:
The system implements continuous feedback loops where data from sensors and processing units is collected, analyzed, and used to adjust processing parameters in real-time. This feedback mechanism ensures high tire quality through constant monitoring and adjustment, while the automated nature of the feedback reduces operational complexity.
3Productivity
If multiple processing units with interconnection are used, then productivity and optimization capability are improved, but loss of time for data communication and coordination increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and pre-analyzing data at each processing unit before items are transferred. Digital twins simulate processing outcomes in advance, allowing optimization to occur before actual production steps, thereby minimizing coordination time while maximizing productivity.
Solution Approach 2:
Data collection, analysis, and processing occur continuously throughout the manufacturing process rather than in discrete batches. This continuous operation ensures that all processing units are constantly optimized based on real-time data, maintaining high productivity without significant time loss to communication and coordination.
Data Source
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AI summary
The present invention relates to a method for preparing a tire element in a connected system, the connected system comprising a database and n processing units X(i), with i = 1...n and n ≥ 3, the n units being interconnected by a network, and each designed to perform one or more tire preparation steps. The present invention further relates to a system for implementing said method for preparing a tire element and to a tire element that can be obtained or produced by said method.