Random Forest and Deep Learning Hybrid Model for Analyzing Steel Lattice Strength and Durability

Strength necessitates structured arrangements of nano-sized interphase precipitates to save weight in HSLA steel vehicle structures. The composition of the alloy, the rate of ferrite formation, crystallographic structure, temperature, and the morphology and dimensions of precipitates influence interphase precipitation. Automotive manufacturers are pursuing lightweight chassis and bodywork to adhere to more stringent environmental regulations. Data preprocessing, feature extraction, and model training are advised for the investigation of steel lattice structural strength. Data is cleansed and organised for analysis during preparation. Fundamental characteristics are derived during feature extraction for classification. Binary classification with KRFDL algorithms and Spark distributed computing categorises normal and pathological occurrences. CNN and LSTM deep learning algorithms enhance classification by identifying atypical events like assaults. The experimental results indicate that the proposed method can classify alterations in steel lattice structures and their strength with an accuracy of 96.52%. The results illustrate how interphase precipitation enhances the strength and reduces the weight of HSLA steel. KRFDL and deep learning algorithms enhance the analysis of steel lattice strength in automotive material engineering. The method precisely classifies structural anomalies to assist manufacturers in optimising steel composition for performance and environmental adherence.