Geometrical quality prediction of machining process by parallel kinematic machines through deformation modelling and error compensation

While they bring advantages for high-speed, high-precision aerospace, automotive, and medical manufacturing applications, PKMs face adoption barriers due to their nonlinear kinematics, complex stiffness characteristics, and challenges in predicting machining performance. This thesis addresses these issues through three primary research goals: creating a stiffness-based deformation model, predicting machined part quality, and compensating for the errors due to compliance deformations.<br><br>A semi-analytical stiffness prediction model was developed to address the first goal, incorporating gravity effects through experimental data-based parameter optimisation.

A novel experimental approach was introduced to isolate gravity-induced deformations from those caused by cutting forces. The second goal, predicting part quality, was achieved by developing a geometrical error prediction model that estimates deviations between the actual and intended geometry of the workpiece, considering the predicted stiffness and cutting forces. Finally, an error compensation method for the Exechon X-Mini machine tool was devised to counter compliance deformations and validated through slot milling experiments.<br>