Machine Learning Product Design for Cost-Optimized Component Selection
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Solution Overview
Problem
Existing product design processes are inefficient and time-consuming, often leading to suboptimal selections of components due to personal biases and non-exhaustive evaluation, resulting in higher costs and delayed production.
Innovation Solution
A machine learning-based framework that automates and optimizes product design by identifying components correlated to specified features, analyzing duplicate parts, and suggesting substitutes to reduce costs and improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual design processes are used with personal bias, then designer familiarity with parts is improved, but manufacturing cost and product quality are worsened
Solution Approach 1:
The patent replaces manual designer selection with an automated machine learning system that analyzes part specifications, requirements, and historical data to objectively determine optimal parts. This substitution eliminates personal bias and familiarity-based decisions, directly addressing the contradiction by improving manufacturing cost and quality outcomes while maintaining ease of use through automation.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between design requirements and part selection. This intermediary processes requirements, evaluates multiple parts against criteria, and recommends optimal selections, thereby removing the designer's personal bias while maintaining the design process's accessibility and ease of operation.
2Ease of operation
If manual design processes are used, then designer control is improved, but design time is worsened
Solution Approach 1:
The patent implements partial automation where the machine learning system handles time-consuming analysis and evaluation tasks, while the designer retains control over final decisions and requirement definitions. This partial action approach reduces design time by automating exhaustive searches and evaluations while preserving designer control and oversight.
Solution Approach 2:
The machine learning system serves as an intermediary that performs exhaustive part evaluations and recommendations, freeing the designer from time-consuming manual searches while maintaining designer control over the process and final decisions. The system handles the computational burden, allowing rapid iteration and evaluation of multiple options.
3Manufacturing precision
If exhaustive part evaluation is performed, then optimal part selection is improved, but processing time is worsened
Solution Approach 1:
The patent performs preliminary actions by pre-processing and indexing part specifications, maintaining updated databases of part characteristics, and pre-evaluating parts against common criteria. This preliminary preparation enables rapid exhaustive evaluation during the actual design process, achieving optimal part selection without excessive processing time during critical design phases.
Solution Approach 2:
The patent replaces manual exhaustive evaluation with automated machine learning systems that can efficiently process and compare large numbers of parts against multiple criteria simultaneously. This computational substitution enables truly exhaustive evaluation of all viable parts within reasonable timeframes, achieving optimal selection that would be impractical through manual processes.
4Ease of manufacture
If automated machine learning selection is used, then manufacturing cost is improved, but design complexity is worsened
Solution Approach 1:
The patent creates a universal machine learning system that handles multiple functions: part selection, cost analysis, specification matching, and recommendation generation. This multi-functional system consolidates what would otherwise require multiple separate tools and processes, reducing overall system complexity while achieving cost optimization through automated, comprehensive evaluation.
Data Source
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AI summary
Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support optimized product design processes. During the design process, information identifying a set of features for a product design are received and evaluated against machine learning logic to identify a set of components that includes components corresponding to the set of features. One or more candidate components may be identified as alternatives to one or more set of components based on the characteristics, and modifications to optimize (e.g., reduce cost, weight, etc.) the set of components may be determined based on at least one design metric and the one or more candidate components. A final set of components that are optimized with respect to the at least one design metric may be output.