The pipelines used in the offshore extraction of oil and gas are connected by threaded joints. Any geometrical error or vibration marks left on the thread surface during the machining process can lead to stress concentration and fatigue failure of the joint. Such instances in the past have led to massive oil leakage and environmental disasters.

Threading is a form cutting operation resulting in wide chips with complex geometries. Multi-point inserts used in mass production can have different custom profiles on each tooth. The chip thickness as well as the effective oblique cutting angles, cutting force coefficients, and direction of local forces vary along the cutting edge. Since the tool moves one thread pitch over each spindle revolution, the vibration marks left by a tooth affect the chip thickness on the following tooth. Threading of oil pipes imposes additional complexities due to the flexural vibrations of thin-walled pipes, which lead to severe chatter instability.

This thesis develops a novel and generalized model to formulate, simulate, and optimize general multi-point threading processes. A systematic semi-analytical methodology is first proposed to determine the chip geometry for custom multipoint inserts with arbitrary infeed strategies. A search algorithm is developed to systematically discretize the chip area along the cutting edge considering the chip flow direction and chip compression at the corners. The cutting force coefficients are evaluated locally for each element, and the resultant forces are summed up over the engaged teeth.

Multi-mode vibrations of the tool and pipe are projected in the direction of local chip thickness, and the dynamic cutting and process damping forces are calculated locally along the cutting edge. A novel chip regeneration model for multipoint threading is developed, and stability is investigated in frequency domain using Nyquist criterion. The process is simulated by a time-marching numerical method based on semi-discretization. An optimization algorithm is developed to maximize productivity while respecting machine’s limits. The proposed models have been verified experimentally through real scale experiments.

The algorithms are integrated into a research software which enables the industry to optimize the process ahead of costly trials.