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

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.

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Publication Details

Journal
Journal of Computer Science and Technology Studies
Published
2025-05-07
DOI
https://doi.org/10.32996/jcsts.2025.7.3.73
Citations
1
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
1.24
Controls
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article

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

Kiran Kumar Gunakala
1 citations
Journal of Computer Science and Technology Studies
Manufacturing Process and Optimization
1.24
article

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

Kiran Kumar Gunakala
article en
1 citations

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.

Journal of Computer Science and Technology StudiesVol. 7(3)
Sri Venkateswara University (IN)
Openalex Percentile: Top 20%
Manufacturing Process and Optimization
1.24
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AI-Driven Automation for Aerospace Manufacturing: Enhancing Quality Control through Integrated Systems — Kiran Kumar Gunakala · Journal of Computer Science and Technology Studies (2025) | TGRS Research Map | TGRS