Production Equipment Sizing Using Digital Twin Consumption Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing approaches to sizing production equipment for manufacturing physical components are subjective, computationally intensive, and often inaccurate, leading to inefficiencies such as overproduction or underproduction.
Innovation Solution
The development of optimization software that uses computationally efficient models to automatically select and deploy appropriate production equipment based on consumption rates at specific production locations, reducing resource consumption and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional subjective methods are used to size production equipment, then equipment selection can be made with simple judgment, but the accuracy of equipment sizing is poor and leads to overproduction or underproduction
Solution Approach 1:
The patent creates virtual copies of production locations through digital twins that replicate physical production environments. These digital models include consumption rate data, production parameters, and equipment characteristics, enabling accurate equipment sizing through simulation and analysis without requiring complex manual calculations or subjective judgment
Solution Approach 2:
The patent introduces an intermediary optimization system that acts as a mediator between production location requirements and equipment selection. This system processes consumption rate data, runs simulations, and provides equipment recommendations, eliminating the need for direct subjective judgment while maintaining methodological simplicity for end users
2Measurement precision
If complex computational methods are used to optimize equipment selection, then accuracy improves, but computational time and resource consumption increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing consumption rate data and creating digital twins of production locations before equipment selection is needed. Historical data is analyzed and stored in optimized formats, allowing rapid equipment sizing calculations when decisions are required without performing complex computations in real-time
Solution Approach 2:
The patent transforms complex equipment selection problems into simplified parameter comparisons by using digital twins to pre-calculate optimal equipment specifications. Consumption rate parameters, production volumes, and equipment capabilities are standardized and compared directly, avoiding iterative complex optimizations while maintaining high accuracy
3Productivity
If production equipment is sized without accurate consumption rate models, then equipment deployment is straightforward, but resource waste occurs through overproduction or underproduction
Solution Approach 1:
The patent implements feedback mechanisms where digital twins continuously monitor and update consumption rate data from actual production locations. This feedback loop refines the models over time, enabling increasingly accurate equipment sizing recommendations that optimize production efficiency while minimizing energy consumption and resource waste
Solution Approach 2:
The patent enables production locations to self-service their equipment sizing needs by providing them with access to their own digital twins and consumption rate data. The system automatically analyzes their specific requirements and recommends appropriately sized equipment, eliminating the need for external consulting while ensuring optimal resource utilization
Data Source
AI summary
A manufacturing process for physical components can be optimized using some techniques described herein. For example, a system can access a group of models, where each model corresponds to a respective type of production location. The system can select a model, from the group of models, that corresponds to a particular type of production location selected by a user. The system can select a particular type of production equipment based on the selected model. The system can then execute one or more computing operations to facilitate deployment of the particular type of production equipment at the particular type of production location. This can help ensure that appropriately sized production equipment is installed at the production location.