Product Optimization Design of Electromagnetic Emission Net Catcher Based on TRIZ Theory Using Scalable Computing

Abstract

Improving the effectiveness and security of electromagnetic interference (EMI) management in different contexts relies heavily on optimising electromagnetic emission net catchers. Scalable computing in conjunction with the TRIZ (Theory of Inventive Problem Solving) provides a methodical strategy for invention, facilitating the methodical resolution of problems and enhancements to the design of intricate engineering systems. Electromagnetic interactions, design parameter precision, and improved material integration make electromagnetic emission net catcher design and optimisation difficult. Large-scale simulations and data processing require scalable computing technologies to simulate and analyse these systems. Using scalable computing, this research presents Automated Decision Inspection Optimization System (ADIOS), based on TRIZ theory, to optimise the design of electromagnetic emission net catchers. Finding and fixing design issues is effortless with ADIOS because it combines TRIZ with machine learning, analytics, and big data. Process and analyse massive datasets efficiently due to the system’s usage of a distributed computing architecture, which handles vast computational workloads. The suggested ADIOS framework can be used in aerospace, telecommunications, and automotive industries where EMI management is critical. Electronic systems operate better, interfere less, and meet strict regulatory criteria by optimising electromagnetic emission net catchers. The ADIOS framework’s efficacy and scalability are assessed using simulation analysis. The outcomes prove that the system can efficiently and accurately handle complicated design scenarios. The investigation shows that ADIOS can optimise design parameters and come up with new ideas to improve electromagnetic emission net catchers. The proposed method increases the Electronic System Performance ratio of 99.25%, Electromagnetic Interference Management ratio of 98.41%, Efficiency ratio of 98.21%, Scalable Computing ratio of 96.31%, and Design Processes ratio of 96.24% compared to existing methods.