Characterization & modeling of chip flow angle & morphology in 2D & 3D turning process

Detta är en avhandling från Trollhättan : Uninversity West

Sammanfattning: Within manufacturing of metallic components, machining plays an important role and is of vital significance to ensure process reliability. From a cutting tool design perspective,  tool macro geometry  design  based on physics based  numerical modelling  is highly needed  that can predict chip morphology.  The chip morphology describes the chip shape geometry and the chip curl geometry. The prediction of chip flow and chip shape is vital in predicting chip breakage, ensuring good chip evacuation and lower surface roughness.  To this end, a platform where such a  numerical model’s chip morphology prediction  can be compared with experimental investigation is needed and is the focus of this work. The studied cutting processes are orthogonal cutting process and nose turning process. Numerical models that simulate the chip formation process are employed to predict the chip morphology and are accompanied by machining experiments. Computed tomography is used  to scan the chips obtained from machining experiments and its ability to capture the variation in  chip morphology  is evaluated.  For nose turning process,  chip  curl parameters during the cutting process are to be calculated. Kharkevich model is utilized in this regard to calculate the  ‘chip in process’ chip curl parameters. High speed videography is used to measure the chip side flow angle during the cutting process experiments and are directly compared to physics based model predictions. The results show that the methodology developed provides  the framework where advances in numerical models can be evaluated reliably from a chip morphology prediction capability view point for nose turning process. The numerical modeling results show that the chip morphology variation for varying cutting conditions is predicted qualitatively. The results of quantitative evaluation of chip morphology prediction shows that the error in prediction is too large to be used for predictive modelling purposes.

  Denna avhandling är EVENTUELLT nedladdningsbar som PDF. Kolla denna länk för att se om den går att ladda ner.