Deep Learning-Based Adaptive Optimization Models for Complex Part Machining Parameters
In the context of interdisciplinary integration between advanced manufacturing and artificial intelligence, the optimization of complex part machining parameters has become a key challenge in intelligent production systems. Combining mechanical engineering, data science, and control theory, this study proposes a deep learning-based adaptive parameter optimization model to enhance the controllability and coordination of multi-objective machining performance. The model integrates nonlinear mapping between machining parameters and performance, surrogate prediction mechanisms, and strategy evolution algorithms to form a closed-loop optimization system spanning from data acquisition to parameter iteration. The study employs a deep regression network to achieve high-dimensional coupling modeling between process parameters (e.g., spindle speed, feed rate, cutting depth) and performance metrics (e.g., surface roughness, machining time, energy consumption, tool life). A multi-task surrogate structure enhances the model’s generalization and differentiable prediction capabilities. Furthermore, uncertainty modeling and weighted loss functions improve adaptability to operational variations and target response accuracy. Building upon this foundation, a constraint-driven parameter update mechanism was designed, dynamically adjusting the gradient-guided optimization strategy to establish a multi-round iterative evolution process. An industrial-grade CNC platform was utilized to establish a real-world testing environment, collecting multi-source features including force/vibration, temperature, current, and images. Comparative and ablation experiments were conducted to evaluate the model’s optimization capabilities and structural contributions across multiple objective performance metrics. Analysis indicates that the adaptive optimization model demonstrates significant advantages in accelerating parameter adjustment convergence and enhancing performance robustness, validating the construct’s deployability and practical value under complex operating conditions.