High electrical and thermal conductivity associated with high stiffness and strength offer tremendous opportunities to the development of a series of carbon nanotube incorporated composite materials for a variety of applications. In particular, a small amount of carbon fibers or carbon nanotubes in a non-conductive polymer will transform a composite into a conductive material, which reveals superb potential of their future application in electronic devices. The relation between the amount of carbon nanotubes in a polymer and the electrical conductivity of it can be studied experimentally as well as theoretically with various simulation models. A three-dimensional (3D) Monte Carlo simulation model using resistance network formation was developed to study the relation between the electrical conductivity of the polymer nanocomposite and the amount of carbon nanotubes dispersed in it. In this model, carbon nanotubes were modeled as curvy cylindrical nanotubes with various lengths and fixed tube diameter, all of which were randomly distributed in a non-conductive constrained volume, which represents polymer. The model can be used to find the volumetric electrical resistance of a constrained cubic structure by forming a comprehensive resistance network among all of the nanotubes in contact. As more and more nanotubes were added into the volume, the electrical conductivity of the volume increases exponentially. However, once the amount of carbon nanotubes reached about 0.1 % vt (volume percentage), electrical percolation was detected, which was consistent with the experimental results. This model can be used to estimate the electrical conductivity of the composite matrix as well as to acquire the electrical percolation threshold.

A supply chain is the series of activities that an organization uses to deliver value to its customers. In today's competitive environment, the globalization of markets has rapidly substituted the traditional integrated business. The competitive success of an organization no longer depends only on its own efforts, but relies on the efficiency of the entire supply chain. Therefore, building an effective supply chain is fast becoming paramount in today's marketplace. Distributed supply chain (DSC) simulation has been identified as one of the best means to test and analyze the performance of supply chains. The Generic Runtime Infrastructure for Distributed Simulation (GRIDS) is a middleware that supports the reuse and interoperation of DSC simulations. We report the experience on employing the GRIDS to support the distributed collaboration of an automobile manufacture supply chain simulation. Several advantages of GRIDS are also discussed which make it ideal middleware for DSC simulations.

This article presents an approach to extend the capabilities of vat photopolymerization (VPP) 3D printing using a robotic arm, with a focus on robust path planning. The robotic cell consists of a Mecademic Meca500 six-axis robot mounted on a Zaber X-LRQ300AP linear guide. The kinematic chain is inverted to reflect the logic of VPP: the world reference frame is fixed to the robot’s tool (the build plate), while the tool frame is attached to the polymerization zone. A virtual degree of freedom for screen image rotation is introduced, bringing the system to eight degrees of freedom. Inverse kinematics are solved under constraints (pose tolerance, joint limits, collision avoidance, and continuity) and evaluated using multi-criteria metrics: manipulability, normalized joint-limit margin, and positional/angular sensitivity. The algorithm follows a deterministic coarse-to-fine search procedure: discrete sweeping of global part orientations, initial sampling with Halton sequences, abd feasibility filtering on a sparsified trajectory, followed by refinement and multi-criteria ranking. The pipeline successfully discarded infeasible orientations and identified feasible printing trajectories for six of the seven benchmark parts, while the remaining case highlights a limitation that may be addressed in future improvements.