Abstract: The present invention describes a device incorporating artificial intelligence and corresponding methods for recommending an optimal machinability data selection, especially with machine performance degradation. The device comprises of a first component, which feeds the system with necessary inputs. A second component, which is the main processing unit, acts as an inference engine to predict the outputs. The last component interprets the outputs, conveys the processed outputs to target location and converts them into necessary tasks. The inputs are identified as the machining operations, work piece material, machining tool type, and depth of cut. The input includes machine performance characteristics as well, that is the degradation level of the machine which interrelates with machine vibration and surface finishing. The outputs are the machining parameters, comprising of the optimal cutting speed and feed rate. The inference engine can be established with fuzzy logic, neural network or neural-fuzzy.

Abstract: The pendulum impact test rig is designed and developed for simulating impact conditions experienced by structural components in real-world crash or full-scale crash test. The test rig can be adopted for crash testing individual vehicle component. The test rig comprises of a base plate which is anchored to a concrete ground, a pendulum supporting structure positioned on the base plate, a pendulum member constructed from structural T-beam that permanently secured to a rotatably shaft, and a test item holding device releasably mounted on the base plate. The pendulum member includes a striker which is releasably attached to the lower end portion of the pendulum, fixed masses permanently secured at the lower end portion of the pendulum, and additional masses detachably attached immediately above the fixed masses. With present preferred embodiment, the center of percussion is controlled within the striking zone.

Abstract: The pendulum impact test rig is designed and developed for simulating impact conditions experienced by structural components in real-world crash or full-scale crash test. The test rig can be adopted for crash testing individual vehicle component. The test rig comprises of a base plate which is anchored to a concrete ground, a pendulum supporting structure positioned on the base plate, a pendulum member constructed from structural T-beam that permanently secured to a rotatably shaft, and a test item holding device releasably mounted on the base plate. The pendulum member includes a striker which is releasably attached to the lower end portion of the pendulum, fixed masses permanently secured at the lower end portion of the pendulum, and additional masses detachably attached immediately above the fixed masses. With present preferred embodiment, the center of percussion is controlled within the striking zone.

Abstract: The present invention describes a device incorporating artificial intelligence and corresponding methods for recommending an optimal machinability data selection. The device comprises of a first component, which feeds the system with necessary inputs. A second component, which is the main processing unit, acts as an inference engine to predict the outputs. The last component interprets the outputs, conveys the processed outputs to target location and converts them into necessary tasks. The inputs are identified as the machining operations, work piece material, machining tool type, and depth of cut. The outputs are the machining parameters, comprising of the optimal cutting speed and feed rate. The inference engine can be established with fuzzy logic, neural network or fuzzy-neural network.