Machine Learning Prediction for Manufacturing Time and Cost

The patent replaces human-based mechanical estimation processes with automated machine learning models that process digital models and transaction data. The system substitutes human cognitive judgment with algorithmic predictions based on regression models trained on historical manufacturing transactions, thereby improving accuracy while removing the inconsistency inherent in human experience-based methods.

The patent introduces machine learning models as an intermediary between digital manufacturing models and production decisions. These models serve as a mediator that translates digital model features into predictive manufacturing metrics, bridging the gap between design data and production planning while maintaining objectivity and consistency.

The patent implements dynamic prediction capabilities where the machine learning models adapt to different digital models and manufacturing scenarios. The system processes varying input features from different digital models and adjusts predictions based on the specific characteristics of each case, maintaining both consistency through standardized methodology and adaptability through feature-based differentiation.

The patent applies local quality by extracting specific features from digital models that are relevant to particular manufacturing aspects. The system identifies and processes different feature sets based on the specific manufacturing context, allowing standardized methodologies to be applied locally to each unique case while maintaining overall consistency.

The patent implements self-service capabilities where the system automatically processes digital models and generates predictions without requiring manual intervention. The machine learning models autonomously extract features from digital models, apply trained algorithms, and produce predictions, thereby dramatically improving productivity while the automated nature handles the complexity internally.

The patent uses digital models as copies of physical manufacturing objects to perform predictions. By working with digital representations rather than physical prototypes or manual measurements, the system accelerates the prediction process while the computational infrastructure manages the underlying complexity of processing these digital copies.