Industry-led mechatronics degree development in regional Australia

This paper presents a technique that was used in the recent development of a new Mechatronics degree in Australia. This technique addressed the local industry needs and the available resources for a well-balanced Mechatronics degree program. The degree development was based on project-based learning and industry engagement. The development of the new Mechatronics degree was made possible via a State Government grant of AU$2.4 Million which was matched by industry contribution of AU$10 Million in cash and in-kind. Since industry was a major stake holder in this degree, a specific industry survey was conducted to check the desired graduates attributes, from industry point of view. The results of this survey is also included in this papers. In addition, the program also addressed the regional industry's challenge of retaining qualified engineers via a clear pathway program for students knowledge and skills development. This paper presents industry's anticipated outputs of the academic Mechatronics program. In addition the paper also discusses the mechanisms adopted for the development of this new degree. The developed fully integrated Mechatronics program was founded on the realisation that if a person undertook a mechanical degree followed by an electronics degree followed by a computer science degree, that person is, still, NOT a Mechatronics engineer

Optimum grasp force and resistance to slippage

This paper presents an analysis and experimental results as part of the research into the optimal rate of grasp force application in precision grasping. It also offers the concept of resistance to object rotation in the robot gripper, which in turn contributes to the resistance to object slippage during robotic object manipulation. It is envisaged that this knowledge will be useful to researchers and designers of robotic grippers, especially those for industrial applications

Friction-based slippage and tangential force detection in robotic grasping

© 2015 IEEE.This paper presents a newly developed parallel gripper prototype capable of sensing grasp force, tangential force and slippage. In this design the gripper itself is used as part of the sensing strategy rather than just being simply a structural support for other sensors. The sensing capability of this gripper is simple in design and reliable. The sensing strategy can be customised to specific applications such as the ability to handle large loads while maintaining its ability to detect slippage as reliably as when handling lighter loads

Novel tire inflating system using extreme learning machine algorithm for efficient tire identification

Tire inflators are widely used all around the word and the efficient and accurate operation is essential. The main difficulty in improving the inflation cycle of a tire inflator is the identification of the tire connected for inflation. A robust single hidden layer feed forward neural network (SLFN) is, thus, used in this study to model and predict the correct tire size. The tire size is directly related to the tire inflation cycle. Once the tire size is identified, the inflation process can be optimized to improve performance, speed and accuracy of the inflation system. Properly inflated tire and tire condition is critical to vehicle safety, stability and controllability. The training times of traditional back propagation algorithms, mostly used to model such tire identification processes, are far slower than desired for implementation of an on-line control system. Use of slow gradient based learning methods and iterative tuning of all network parameters during the learning process are the two major causes for such slower learning speed. An extreme learning machine (ELM) algorithm, which randomly selects the input weights and biases and analytically determines the output weights, is used in this work to train the SLFNs. It is found that networks trained with ELM have relatively good generalization performance, much shorter training times and stable performance with regard to the changes in number of hidden layer neurons. The result represents robustness of the trained networks and enhance reliability of the mode. Together with short training time, the algorithm has valuable application in tire identification process