Process planning for 2 1/2D pocket machining: a novel framework

The process for 2 1/2 D pocket machining generally considers the geometry of 2 1/2D pocket as a primative machining feature. But the non-unique mapping of the geometry of 2 1/2 pocket to the machining steps introduces ambiguities in the design and planning phases. Process planning consists of different tasks, such as Tool Selection, Tool Path Generation and Machining Parameter Selection, with individual research issues. Most of the prior research effort treats optimization of each task as isolated research topic. Only a few attempts had been made to generate the complete process plan by integrating the optimized tasks, that too in a sequential manner. It is well known that the cutting process geometry and kinematics link the different tasks together through the process parameters, like cutting tool diameter, cutting depths, etc. In this paper, a "Novel Integrated Process Planning Framework" is proposed to simultaneously optimize all the process planning tasks for different elemental features. To avoid any ambiguity between the parts design and the process planning phase, the geometry of 2 1/2 D pocket has been separated into Elemental Machining Surfaces (EMS): Bottom, Wall, and corner. This separation allows representing the 2 1/2 D pocket machining as a well defined problem where all the planning tasks for each EMS are considered together to generate the entire process plan. The framework incorporates a bottom-up ingration approach, where the planning tasks for each EMS are simultaneously optimized at the bottom level. Plans for different EMS are integrated at a higher level to obtain the optimal process plan for the entire 2 1/2 D pocket. Integrated process planning of other machining features as well as higher levels of planning can also ve generated by the proposed framework. The proposed framework approach avoids the use of complex algorithms and associated high computation costs by carrying out the integration of the process plans at different levels by facilitating the optimization of a manageable number of parameters with meaningful physical basis.Peer reviewed: YesNRC publication: Ye