Home / Current Issue / Paper 1718774
Aircraft Certification Rule Extraction and Dashboard System Using Python and FastAPI
Subject area: Science,Engineering and Technology · Area of research: Software Engineering
DOI: 10.64388/IREV9I12-1718774
Abstract
Aircraft certification documents together with Certification specifications (CS), Acceptable Means of Compliance (AMC), and Guidance Material (GM) comprise huge volumes of complicated and unstructured regulatory statistics. guide processing and navigation of those files is time-consuming, error-inclined, and inefficient for engineers and analysts involved in Aircraft certification activities. This paper offers the development of an Aircraft Certification Rule Extraction and Dashboard machine designed to automate the extraction, business enterprise, and visualization of certification policies from CS-23 regulatory files. The proposed system uses Python-based textual content processing strategies to extract rule content from PDF files and convert it into established system-readable records. sample matching and parsing strategies are used to become aware of rule numbers, titles, descriptions, AMC sections, and GM references. The extracted statistics is prepared into JSON layout and served thru FastAPI-based rest APIs. A frontend dashboard changed into advanced the usage of HTML, CSS, and JavaScript to permit customers to correctly browse certification documents, select subparts and guidelines, and consider corresponding AMC and GM data. The gadget also supports exporting certification data into PDF and DOCX record codecs, improving documentation and accessibility. The advanced framework reduces guide effort in dealing with certification specifications and improves traceability of regulatory information. The assignment demonstrates how cutting-edge backend technologies and dependent facts processing can improve regulatory workflow management in the aerospace area.
Keywords
Aircraft Certification, CS-23, FastAPI, Regulatory Document Parsing, Rule Extraction
References
[1] European Union Aviation Safety Agency (EASA), “Certification Specifications and Acceptable Means of Compliance for Normal Category AeroAircrafts (CS-23),” EASA Regulatory Documents, Cologne, Germany, 2023.
[2] European Union Aviation Safety Agency (EASA), “Acceptable Means of Compliance (AMC) and Guidance Material (GM) to CS-23,” EASA Publications, 2023.
[3] M. McKinney, “Data Structures for Statistical Computing in Python,” Proceedings of the 9th Python in Science Conference, pp. 56–61, 2010.
[4] S. Ramírez and J. Alvarez, “Automated Extraction of Regulatory Requirements from Aerospace Documents Using Text Parsing Techniques,” International Journal of Aerospace Informatics, vol. 11, no. 2, pp. 88–101, 2023.
[5] S. Kluyver et al., “Jupyter Notebooks – A Publishing Format for Reproducible Computational Workflows,” Proceedings of the 20th International Conference on Electronic Publishing, pp. 87–90, 2016.
[6] S. Matsumoto and Y. Nishimura, “Efficient PDF Content Extraction Using Structured Parsing Approaches,” IEEE International Conference on Document Analysis and Recognition (ICDAR), pp. 412–418, 2022.
[7] S. Tahrioui and M. Boulmalf, “Rule-Based Information Extraction from Semi-Structured Technical Documents,” Journal of Information Processing Systems, vol. 18, no. 4, pp. 921–934, 2022.
[8] S. Ramakrishnan and P. Narayanan, “RESTful API Development Using FastAPI and Python,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 3, pp. 215–223, 2023.
[9] D. Beazley and B. K. Jones, “Python Cookbook,” 3rd ed., O’Reilly Media, 2013.
[10] J. Grus, “Data Science from Scratch: First Principles with Python,” 2nd ed., O’Reilly Media, 2019.
[11] A. Verma and R. Kulkarni, “Automated Regulatory Compliance Analysis Using Structured Knowledge Models,” IEEE Access, vol. 11, pp. 55231–55245, 2023.
[12] K. Ganesan and T. Srinivasan, “Machine-Readable Regulatory Frameworks for Aerospace Compliance Systems,” International Journal of Software Engineering and Knowledge Engineering, vol. 33, no. 6, pp. 1005–1021, 2023.
[13] H. F. Li and P. Sharma, “Text Mining Approaches for Engineering Document Analysis,” Journal of Artificial Intelligence Research, vol. 74, pp. 611–629, 2022.
How to cite this paper
@article{1718774,
author = {Darshan S, Dr. Kavitha Devi CS, Smt. Lakshmi P},
title = {Aircraft Certification Rule Extraction and Dashboard System Using Python and FastAPI},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {12},
pages = {1114-1122},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1718774.pdf},
abstract = {Aircraft certification documents together with Certification specifications (CS), Acceptable Means of Compliance (AMC), and Guidance Material (GM) comprise huge volumes of complicated and unstructured regulatory statistics. guide processing and navigation of those files is time-consuming, error-inclined, and inefficient for engineers and analysts involved in Aircraft certification activities. This paper offers the development of an Aircraft Certification Rule Extraction and Dashboard machine designed to automate the extraction, business enterprise, and visualization of certification policies from CS-23 regulatory files. The proposed system uses Python-based textual content processing strategies to extract rule content from PDF files and convert it into established system-readable records. sample matching and parsing strategies are used to become aware of rule numbers, titles, descriptions, AMC sections, and GM references. The extracted statistics is prepared into JSON layout and served thru FastAPI-based rest APIs. A frontend dashboard changed into advanced the usage of HTML, CSS, and JavaScript to permit customers to correctly browse certification documents, select subparts and guidelines, and consider corresponding AMC and GM data. The gadget also supports exporting certification data into PDF and DOCX record codecs, improving documentation and accessibility. The advanced framework reduces guide effort in dealing with certification specifications and improves traceability of regulatory information. The assignment demonstrates how cutting-edge backend technologies and dependent facts processing can improve regulatory workflow management in the aerospace area.},
keywords = {Aircraft Certification, CS-23, FastAPI, Regulatory Document Parsing, Rule Extraction},
month = {June},
doi = {https://doi.org/10.64388/IREV9I12-1718774}
}