Advantages in Using a Stock Spring Selection Tool that Manages the Uncertainty of the Designer Requirements

TL;DR

This paper demonstrates that a computer-assisted stock spring selection tool, which uses multi-criteria analysis and fuzzy logic to handle requirement uncertainties, significantly improves design accuracy and flexibility over manual methods.

Contribution

It introduces a novel automated spring selection algorithm that manages uncertainty and enhances design process efficiency compared to manual search.

Findings

Assisted search yields significantly better spring selection results.

The tool improves the precision and flexibility of the design process.

Automated analysis effectively handles data uncertainty with fuzzy logic.

Abstract

This paper analyses the advantages of using a stock spring selection tool that manages the uncertainty of designer requirements. Firstly, the manual search and its main drawbacks are described. Then a computer assisted stock spring selection tool is presented which performs all necessary calculations to extract the most suitable spring from within a database. The algorithm analyses data set with interval values using both multi-criteria analysis and fuzzy logic. Two examples, comparing manual and assisted search, are presented. They show not only that the results are significantly better using the assisted search but it helps designers to detail easily and precisely their specifications and thus increase design process flexibility.