Technology Selection System for Vehicle Part Production
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Solution Overview
Problem
Vehicle manufacturers face challenges in selecting optimal technologies for part production due to rising commodity prices and varying cost structures, leading to inefficient and costly decision-making processes across different divisions within a company.
Innovation Solution
A computer-implemented method and system that standardizes datasets of technologies, generates CAD models, and applies optimization algorithms using multiple data sources to determine and recommend optimized technologies for part manufacturing, considering cost and performance metrics, and transmits these recommendations for use in producing manufactured parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional separate decision-making processes are used in different divisions, then each division can maintain its own operational autonomy, but the overall technology selection becomes inefficient and costly due to lack of standardization
Solution Approach 1:
The system segments the technology selection process into distinct functional modules: data collection module, standardization module, optimization module, and recommendation module. Each module handles specific tasks independently, allowing parallel processing while maintaining overall process integration through standardized data interfaces.
Solution Approach 2:
The system creates a universal standardized dataset format that can accommodate multiple data sources including sourcing data, manufacturing data, and design data. This universal format enables different divisions to contribute their specialized data while maintaining compatibility across the entire selection process, eliminating the need for separate decision-making procedures in each division.
2Measurement precision
If comprehensive data from multiple sources is collected and analyzed, then the technology selection becomes more accurate and optimized, but the data processing complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary standardization of all incoming data from multiple sources before optimization analysis. Data from sourcing, manufacturing, and design sources are pre-processed into a unified standardized format with consistent structures and units, eliminating the need for complex real-time data transformation during optimization and reducing computational complexity.
Solution Approach 2:
The standardized dataset acts as an intermediary layer between multiple data sources and the optimization algorithms. This intermediate standardized format simplifies the interface between diverse data sources and complex optimization processes, allowing accurate multi-source data integration without directly increasing optimization system complexity.
3Ease of manufacture
If optimization algorithms are applied to balance cost and performance metrics, then the technology selection achieves better cost-effectiveness, but the computational time and processing resources increase
Solution Approach 1:
The system pre-processes and standardizes all input data before applying optimization algorithms, organizing cost and performance metrics into structured formats that optimization algorithms can process efficiently. This preliminary organization reduces the computational burden during optimization execution and accelerates the overall process.
Solution Approach 2:
The system transforms multiple cost and performance parameters into a standardized optimization framework where diverse metrics are converted into comparable parameter formats. This parameter transformation enables efficient optimization algorithm execution by reducing the complexity of multi-criteria decision-making while maintaining the ability to balance cost and performance effectively.
Data Source
AI summary
Various embodiments may include a computerized system and method for determining a selection of technologies or processes for use in part production. A dataset of technologies or processes may be considered and standardized. Generic CAD models may be generated from the standardized dataset. Optimization metrics for each generic CAD model may be received. Data from a sourcing, a manufacturing and a design data source may also be received. An optimization algorithm may be used for each generic CAD model based on the optimization metrics and the data from the data sources. One or more optimized CAD models may be generated and one or more optimized technologies or processes for use in part manufacturing may be obtained. The optimized technologies or processes may be based on the one or more optimized CAD models. The optimized technologies or processes may be transmitted for selection and use in producing manufactured parts.