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Motivated by our previous work which proposed an improved microforce model considering the effects of cut-in and cut-off in robotic belt grinding operation, we further extend this novel idea into the analysis and optimization of the cut-in and cut-off phenomenon which seriously affects the surface contour accuracy and machining quality. Firstly, a practical material removal rate (MRR) model is introduced to characterize the material removal characteristics, especially at the cut-in and cut-off paths. Then, a strategy framework using the combination of active and passive force control, variable-feed finishing, and longitudinal grinding style is constructed to further enhance the surface profile accuracy and processing quality. Finally, robotic machining experiments of titanium alloy test workpiece are implemented to test the reliability and reasonability of the strategy framework. [ABSTRACT FROM AUTHOR]

Reliable prediction and evaluation of material removal (MR) is a continuing pursuit in the grinding process. Mechanistic and empirical MR models always suffer from inaccuracies and restricted applicability, whereas data-driven approaches remain deficient in sample dependence, generalization, and physical interpretability. This motivates us to develop a knowledge-wrapping method (KWM) to predict and characterize the material removal behavior in robotic belt grinding. A physical material removal profile model (PHY) that suits the robotic belt grinding is first presented by appealing to the Archard law and Hertzian theory. Next, the knowledge-wrapping matrix is designed to wrap the physical constraints into a form of design matrix by transforming the mechanistic and empirical models into a linear system. The knowledge-wrapping matrix and the experimental data are then connected via a likelihood function with the consideration of measuring noise, yielding a hybrid-driven learning process that preserves interpretability from PHY. Comparative experiments and MR mechanism interpretations are finally presented to demonstrate the effectiveness and superiority of the proposed method. [Display omitted] • The knowledge-wrapping method achieves reliable prediction and evaluation. • A mechanistic model is used to provide physical constraints to learning model. • Comparative experiments and mechanism interpretations demonstrate its superiority. • The proposed method can be easily deployed to other prediction and evaluation scenarios. [ABSTRACT FROM AUTHOR]

A tentative study from the perspective of abrasive grain geometry in this paper is conducted to investigate the specific energy and energy efficiency for clarifying the robotic belt grinding mechanisms. The energy efficiency model is established based on the friction coefficient model of the single spherical grain, then the experiments and simulation are implemented to energetically evaluate the microscale material removal mechanisms from the specific energy contributions. It has been demonstrated that the specific plowing energy is more predominant than both the specific cutting and sliding energy in robotic belt grinding, resulting in the energy efficiency ranges between 17 and 41 %. Both the large grain size and normal contact force can be taken as optimization strategies to maximize the energy efficiency for material removal. [ABSTRACT FROM AUTHOR]

The elastic state at contact wheel–workpiece interface is a critical issue during robotic belt grinding process that significantly influences the finishing profile accuracy. Establishing a reasonable undeformed chip-thickness (UCT) model that suits to this operation is considered a feasible approach to clarify the cutting mechanisms. In the present paper, an elastic state–driven robotic belt grinding chip-thickness model is established to predict the workpiece surface roughness. In this new model, the combined modulus of elasticity of the contact wheel is calculated according to the formula of Young's modulus, and the exponent with respect to the effects of linear and nonlinear deflection is further determined based on the energy balance hypothesis. Experiments are conducted to verify the reasonability of the improved chip-thickness model from the perspective of surface roughness, and the findings are likely to clarify the differences in material removal mechanism between wheel grinding and robotic belt grinding essentially. [ABSTRACT FROM AUTHOR]