AI Functional Test Failure Prediction for Manufacturing Designs
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Solution Overview
Problem
Functional testing in manufacturing often detects failures after products are manufactured, leading to extensive debug and troubleshooting, which negatively impacts production throughput, cycle time, and cost, highlighting the need for predicting parametric failure modes before product design and manufacturing are complete.
Innovation Solution
A Functional Test Failure Prediction engine, comprising a processor-implemented model that receives product and manufacturing design information, applies product-specific tests, and uses a comparator and AI learning module to predict potential failures, providing a graphical user interface for output and feedback loop for continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If functional testing is performed after manufacturing, then product performance validation is achieved, but production throughput decreases and cycle time increases
Solution Approach 1:
The patent applies preliminary action by performing functional testing during the design phase before manufacturing. The system executes test cases against design models to identify potential failures early, allowing design modifications without impacting production throughput or cycle time.
Solution Approach 2:
The patent uses copying by creating virtual models and simulations of the product design. These digital copies are subjected to functional testing, allowing validation of product performance without manufacturing physical prototypes, thereby maintaining high production throughput while ensuring reliability.
2Reliability
If functional testing is performed after manufacturing, then product performance validation is achieved, but manufacturing cycle time increases
Solution Approach 1:
The system performs functional testing during the design phase before manufacturing begins. By executing test cases against design models in advance, the patent eliminates the need for time-consuming post-manufacturing testing, thereby reducing manufacturing cycle time while maintaining product performance validation.
Solution Approach 2:
The patent replaces physical manufacturing and testing with computational modeling and simulation. Virtual models substitute for physical prototypes, and software-based testing replaces hardware testing, dramatically reducing the time required for performance validation without compromising reliability.
3Reliability
If functional testing is performed after manufacturing, then product performance validation is achieved, but product cost increases
Solution Approach 1:
The patent performs functional testing during the design phase before manufacturing. By identifying and correcting performance issues early in the design process, the system avoids costly rework, scrap, and warranty claims that would otherwise occur after manufacturing, thereby reducing overall product cost while maintaining validation reliability.
Solution Approach 2:
The patent uses virtual models and simulations to validate product performance, replacing expensive physical prototyping and testing. This digital copying approach significantly reduces material costs, equipment usage, and labor expenses associated with traditional post-manufacturing testing, while maintaining thorough performance validation.
4Difficulty of detecting and measuring
If extensive debug and troubleshooting is performed on failed products, then failure causes are identified, but production throughput decreases
Solution Approach 1:
The patent performs functional testing during the design phase to identify potential failure modes before manufacturing. By detecting and addressing design-related failures early, the system eliminates the need for extensive debug and troubleshooting during production, thereby maintaining high failure cause identification capability while preserving production throughput.
Data Source
AI summary
A functional test failure prediction (FTFP) engine. The engine includes: a plurality of inputs, capable of receiving at least: a product design; a manufacturing design for the product design; a plurality of specified functional parameters for the product design; bills of materials for the product design; and prior outcome feedback. Also included are: at least one algorithm for virtually applying a plurality of product-specific tests to the product design and the manufacturing design; a comparator capable of comparing an outcome of the algorithm to the specific functional parameters; at least one learning module capable of learning from at least the actual application of the product-specific tests; a feedback loop to provide at least the comparator outcome and the learning of the learning module back to the plurality of inputs as the prior outcome feedback; and a graphical user interface output capable of providing at least the outcome of the comparator.