Topology optimization with accessibility constraint for multiaxis machining Abstract   In this paper, we present a topology optimization (TO) framework to enable automated design of mechanical components while ensuring the result can be manufactured using multiaxis machining. Although TO improves the partxe2x80x99s performance, the asdesigned model is often geometrically too complex to be machined and the asmanufactured model can significantly vary due to machining constraints that are not accounted for during TO. In other words, many of the optimized design features cannot be accessed by a machine tool without colliding with the part (or fixtures). The subsequent postprocessing to make the part machinable with the given setup requires trialanderror without guarantees on preserving the optimized performance. Our proposed approach is based on the wellestablished accessibility analysis formulation using convolutions in configuration space that is extensively used in spatial planning and robotics. We define an inaccessibility measure field (IMF) over the design domain to identify nonmanufacturable features and quantify their contribution to nonmanufacturability. The IMF is used to penalize the sensitivity field of performance objectives and constraints to prevent formation of inaccessible regions. Unlike existing discrete formulations, our IMF provides a continuous spatial field that is desirable for TO convergence. Our approach applies to arbitrary geometric complexity of the part, tools, and fixtures, and is highly parallelizable on multicore architecture. We demonstrate the effectiveness of our framework on benchmark and realistic examples in 2D and 3D. We also show that it is possible to directly construct manufacturing plans for the optimized designs based on the accessibility information.

ContextDependent Search for Generating Paths for Redundant Manipulators in Cluttered Environments We present a contextdependent bidirectional treesearch framework for pointtopoint path planning for manipulators. Conceptually, our framework is composed of six modules: tree selection, focus selection, node selection, target selection, extend selection and connection type selection. Each module consists of a set of interchangeable strategies. By exploiting synergistic interaction between these strategies and selecting appropriate strategies based the contextual cues from the search state, we show an instance of our framework that computes highquality solutions in a variety of complex scenarios with a low failure rate. We also show that some popular path planning methods in the literature can be easily represented in our framework. We compare our approach with these popular methods in a diverse set of test scenarios. We report a 15fold reduction in failure rate coupled with at least a 26% drop in solution suboptimality when compared to the best of the alternative methods.