Coating performance in high speed micro machining of H13 tool steel

The development and application of Titanium Aluminium Nitrate (TiAlN) coatings for cutting tools has led to dramatic tool life extension and the realisation of high speed machining for hardened materials. This results in longer tool life and makes it possible to employ higher cutting speeds and feed rates. In this study, a series of different TiAlN based coatings on micro grains solid carbides were tested on H13 Tool Steel. These advanced coatings are commercially available by coating manufacturer which are trade marks of Balzers UK. The aim of this experiment was to investigate the performance of micro tools coated with these coatings and compare with uncoated tools. The results will be used to determine whether coatings for micro tools will have any impact on the performance of the tools such as reducing cutting forces or improving machining quality. This will be achieved by means of analysing the cutting force data and 3-D surface roughness respectively. Result obtained shows that different coating had different performance, hence can be applied to specifically targeted machining operation. The results also highlight some of the differences in wear mechanism of micro tools

Solving Continuous Trajectory and Forward Kinematics Simultaneously Based on ANN

Robot movement can be predicted by incorporating Forward Kinematics(FK) and trajectory planning techniques. However, the calculations will becomecomplicated and hard to be solved if the number of specific via points is increased.Thus, back-propagation artificial neural network is proposed in this paper to overcomethis drawback due to its ability in learning pattern solutions. A virtual 4-degreeof freedom manipulator is exploited as an example and the theoretical results arecompared with the proposed method

Predicting the motion of a robot manipulator with unknown trajectories based on artificial neural network

Mathematically, the motion of a robot manipulator can be computed through the integration of kinematics, dynamics, and trajectories calculations. However, the calculations are complex and only can be applied if the configuration of the robot and the characteristics of the joint trajectories are known. This paper introduces the use of artificial neural networks (ANN) to overcome these shortcomings by solving nonlinear functions and adapting the characteristics of unknown trajectories. A virtual six-degree-of-freedom (DOF) robot manipulator is exploited as an example to show the robustness of the developed ANN topology