Coherent Beam Combining (CBC) is a promising technique for enhancing laser output power by accurately aligning the phase and position of multiple laser beams. The Stochastic Parallel Gradient Descent (SPGD) algorithm is commonly used in CBC systems due to its simplicity and scalability. However, its dependence on fixed control parameters can result in slow convergence rates and diminished control stability. To overcome these challenges, this study introduces an adaptive SPGD algorithm that dynamically adjusts the perturbation amplitude and learning rate based on the real-time value of the objective function. This approach accelerates convergence during the initial stages by increasing control inputs when the objective function value is low, while ensuring stability as the function nears its maximum in later stages. Numerical simulations of 7-channel and 19-channel CBC systems revealed that the adaptive SPGD algorithm reduced average iteration counts by 26.4% and 18.1%, respectively, compared to the basic SPGD. Furthermore, the overall control performance improved, achieving high beam combining efficiency with reduced total computation time. This proposed algorithm serves as a straightforward yet effective enhancement to the conventional SPGD method, improving both convergence speed and stability.

NC machining data, which cause excessive cutting force, accelerate tool wear, reduce the roughness of machined surfaces, and in severe cases, result in tool breakage and material waste. Thus, the cutting conditions should be optimized according to the material-spindle speed-feed rate combination. However, it is very difficult to perfectly predict and optimize the dynamic characteristics of machining, such as tool vibration and wear, and spindle thermal deformation. Further, predicted tool paths are accompanied by machining errors. This study proposes an advanced adaptive control method that can balance the machining load, improve tool life, and reduce machining time. The proposed method 1) synchronizes the spindle load and NC-data and stores it, 2) analyzes the stored data to create a reference curve that can balance the machining load, 3) adjusts the tool feed rate using a reference curve, 4) engages rapid traverse when the load is small, and 5) applies an approach feed rate when the tool approaches a workpiece, reducing the impact on the tool when the tool meets the workpiece. Case examples proved that the use of the proposed balanced load reduced machining time and increased tool life.