Natural Language Processing for Automated Engineering Report Generation

Abstract

The rapid growth of digital engineering systems has led to an increasing volume of technical data that requires efficient documentation and reporting. Manual preparation of engineering reports is time-consuming, prone to inconsistencies, and requires significant domain expertise. Natural Language Processing (NLP) offers a promising solution for automating the generation of structured, accurate, and coherent engineering reports from raw technical data. This study explores the application of NLP techniques for automated engineering report generation, focusing on improving efficiency, consistency, and readability of technical documentation. The methodology involves collecting heterogeneous engineering data such as sensor outputs, simulation results, experimental observations, and system logs. This data is preprocessed using text normalization, tokenization, and feature extraction techniques to convert structured and unstructured inputs into machine-readable formats. Advanced NLP models, including Transformer-based architectures such as BERT and GPT variants, are employed to generate context-aware and domain-specific technical narratives. Template-based structuring is combined with deep learning models to ensure logical flow and adherence to engineering report standards. The performance of the system is evaluated based on metrics such as linguistic accuracy, coherence, completeness, and human evaluation scores. The results demonstrate that NLP-based systems can effectively generate high-quality engineering reports with minimal human intervention while maintaining technical accuracy and clarity. It is also observed that transformer-based models outperform traditional rule-based and statistical approaches in terms of contextual understanding and fluency.