Abstract: A cellular automata neural network method for process modeling of film-substrate interactions utilizes a cellular automaton system having variable rules for each cell. The variable rules describe a state change algorithm for atoms or other objects near a substrate. The state change algorithm is used to create a training set of solutions for training a neural network. The cellular automaton system is run to model the film-substrate interactions with the neural network providing the state change solutions in place of the more computationally complex state change algorithm to achieve real-time or near real-time simulations.

Abstract: In the design of the process to machine discrete mechanical parts, the sequence of removing material is arrived at to ensure that the resulting part is of the correct geometry and finish, and the process is safe, feasible and accomplished in minimum time. For complex parts, an experienced machinist makes use of inductive methods to relate similar part material, geometries together with interdependencies and their associated machining sequences which have produced quality parts in the past with minimum time expended, or deductive methods to generate a sequence by relating feature attributes (relative size and position) and relations (intersections and common tooling). Of interest is the interaction between the two methods because their coupling enables a self-improving design system to be realized. A feature-based solid modelling software environment provides the elements of a symbolic language for describing a discrete mechanical part in terms of its product and process design.

Abstract: A bathroom scale apparatus is disclosed. The scale apparatus has a bottom surface. A plurality of downwardly extending projections are located on the bottom surface. The projections engage a carpeted surface to provide a stable platform.