Amirkabir Journal of Mechanical Engineering

Amirkabir Journal of Mechanical Engineering

Regression Modeling and Experimental Analysis of Critical Time in the First Phase of Three-Dimensional Manipulation Using a Design-of-Experiments-Based Simulation Approach

Document Type : Research Article

Authors
1 Department of Manufacturing Engineering, Faculty of Engineering, Arak University, Arak, Iran
2 Department of Industrial Engineering, Faculty of Engineering, Arak University, Arak, Iran
Abstract
Atomic Force Microscopy (AFM) is one of the principal tools in nanotechnology, with extensive applications in biological, medical, and materials engineering fields. Using AFM enables precise investigation of mechanical properties of biological structures. In this study, the manipulation process of biological nanoparticles during the initial motion phase was investigated through a combination of numerical analysis and experimental testing using an (AFM). The main objective was to examine the effect of cantilever and tip geometric parameters on the critical time of the first phase and to develop an accurate predictive model. The geometric parameters including height, thickness, length, and width along with the particle radius were considered as influencing factors. A regression model was derived based on the effect of these parameters on critical time. Statistical analysis of the proposed model demonstrated a high level of accuracy and reliability, with a coefficient of determination (R²) of 95.10%, showing full agreement with the experimental results. Furthermore, contour plots were employed to model system behavior under combined variable variations. The results revealed that increasing tip height and particle radius leads to a longer critical time. Additionally, the variation diagram of the manipulation force indicated that the total force applied to the particle increases continuously up to approximately 60 milliseconds before reaching a steady state. This point corresponds to critical force threshold, confirming validity of the proposed regression model.
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