Serap TANRIVERDİ, Levent AYDIN

Optimization of Process Parameters for Green Composites in Abrasive Water Jet Machining Process Using Neuro-Regression Analysis

This study aims to develop a design procedure for optimizing the abrasive water jet machining (AWJM) process in green composites. Multiple non-linear neuro-regression analysis has been performed methodically to overcome insufficient approaches to modeling-design-optimizing green composites in AWJM. First, the model generation process is carried out according to three criteria: linearity, order, and functions used in the model. Next, R^2_training, R^2_testing, and R^2_validation values have been checked for the validity of the models. Then, the machining parameters have been optimized by applying a numerical non-linear global optimization algorithm, Simulated Annealing. Pressure within the pumping system (PwPS), stand-off distance (SoD), and nozzle speed (NS) are design variables; surface roughness (Ra) and process time (PT) are objective functions of introduced mathematical optimization problems. The numerical result shows that the optimum process parameters obtained are PwPS (150 MPa), SoD (3.5 mm), and NS (125 mm/min). This novel optimization approach is also feasible for another modeling design optimization problem. The proposed design can be used as a systematic framework for parameter optimization in environmentally conscious manufacturing processes.

Keywords:

Abrasive water jet machining, green composite neuro-regression, optimization,

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