Product Design Support System for High-Reliability Rocket Engines
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Solution Overview
Problem
Conventional design methods for high-technology products like rocket engine systems fail to meet high-reliability design requirements due to their inability to efficiently integrate systematic risk management, simulation technologies, and detailed failure mode analyses within a short development timeframe.
Innovation Solution
A product design support system and method that utilizes an input device, output device, data hold device, and computer main body to execute programs for orthogonal table creation, dimensions determination, simulation, evaluation index computation, response surface modeling, and design solution optimization, enabling rapid design and development with high reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional one-point designing method is used, then design process is simple, but reliability requirement cannot be met
Solution Approach 1:
The patent segments the design process into multiple phases (conceptual design, basic design, detailed design, development test) with systematic risk management and FMEA at each stage. This segmentation allows comprehensive reliability assessment while maintaining manageable complexity through structured progression.
Solution Approach 2:
The patent performs preliminary risk identification and FMEA analysis before final design completion. By conducting systematic risk management and failure mode analysis in advance during conceptual and basic design phases, potential reliability issues are identified and addressed before detailed design and manufacturing, preventing problems rather than reacting to them.
2Reliability
If systematic risk management and detailed FMEA are implemented, then reliability is improved, but development time increases
Solution Approach 1:
The patent performs preliminary risk identification and FMEA analysis during conceptual and basic design phases before detailed design and manufacturing. By conducting systematic risk management and failure mode analysis in advance, potential reliability issues are identified and addressed early, preventing costly redesigns and delays during later development stages.
Solution Approach 2:
The patent implements continuous feedback loops where FMEA results and risk assessment outcomes are fed back into the design process. Evaluation results from each design phase are used to refine and improve subsequent design iterations, allowing systematic reliability improvement while maintaining efficient development through targeted refinements rather than exhaustive reanalysis.
3Measurement precision
If thorough risk identification and systematic analysis are performed, then design adequacy is improved, but design efficiency decreases
Solution Approach 1:
The patent segments the analysis process into focused activities at each design phase: systematic risk identification in conceptual design, detailed FMEA in detailed design, and verification testing in development test phase. This segmentation allows thorough assessment where needed while maintaining overall design efficiency through phase-appropriate analysis depth.
Solution Approach 2:
The patent applies appropriate levels of analysis intensity to different design phases and components. Not all components require the same level of FMEA detail - critical components receive thorough analysis while less critical components use streamlined assessment methods. This partial action approach maintains design adequacy for critical areas while preserving overall design efficiency.
Data Source
AI summary
The invention relates to a product design support system for supporting product design business so as to design and develop a product in a short time. The system creates an L-row orthogonal table according to set design parameters, executes many times, for each of L sets of design parameter groups, a virtual prototyping operation by adjusting the dimensional tolerance of each part, processes averages and variances of L sets of evaluation indexes obtained by the virtual prototyping operations, to form a response surface and response surface model, makes a factorial effect diagram of design parameters for each evaluation index, examines the factorial effect diagram, forms optional combinations of design parameters sensitive to the evaluation indexes, applies the combinations to the response surface model, forms many design solutions by optionally combining all design parameters that may achieve design target values, conducts filtering to extract a maximum likelihood design solution candidate group that achieves specified evaluation index limit values out of the design solutions, selects a maximum likelihood design solution group from the solution candidate group, and presents the same to a user.