AI-Driven Automation for Aerospace Manufacturing: Enhancing Quality Control through Integrated Systems

Authors

  • Kiran Kumar Gunakala Sri Venkateswara University, India

DOI:

https://doi.org/10.32996/jcsts.2025.7.3.73

Keywords:

Aerospace manufacturing, artificial intelligence, quality control, computer vision, digital thread

Abstract

Aerospace manufacturing represents one of the most demanding precision engineering environments, requiring exacting quality control measures to ensure component integrity. Traditional manual inspection processes face significant challenges, including fatigue-induced errors, inconsistency between operators, and limited defect detection capabilities, particularly for microscopic anomalies in advanced composite materials. The integration of artificial intelligence through SAP's enterprise technology stack offers a transformative solution, enabling real-time defect detection with unprecedented accuracy and consistency. This comprehensive integration architecture connects SAP AI Core's computer vision capabilities with the SAP Integration Suite and S/4HANA Manufacturing, creating an end-to-end quality assurance ecosystem. Implementation across leading aerospace manufacturers demonstrates substantial improvements in defect detection accuracy, inspection speed, and cost efficiency. Beyond immediate operational benefits, these systems contribute to enhanced aircraft safety through comprehensive digital thread capabilities and predictive quality interventions, representing a fundamental advancement in aerospace manufacturing quality assurance.

Downloads

Published

2025-05-07

Issue

Section

Research Article

How to Cite

Kiran Kumar Gunakala. (2025). AI-Driven Automation for Aerospace Manufacturing: Enhancing Quality Control through Integrated Systems. Journal of Computer Science and Technology Studies, 7(3), 644-650. https://doi.org/10.32996/jcsts.2025.7.3.73