Human-Machine Interface-Based Robotic Wheel Chair Control

This chapter presents the P300-based human machine interference (HMI) systems control robotic wheel chair (RWC) prototype in right, left, forward, backward, and stop positions. Four different targets letters are used to elicit the P300 waves, flickering in the low frequency region, by using oddball paradigms and displayed on a liquid crystal display (LCD) screen by Lab-VIEW. After the pre-processing and taking one second time window, feature is extracted by using discrete wavelet transform (DWT). Three different classifiers—two based on ANNs pattern recognition neural network (PRNN) and feed forward neural network (FFNN) and the and other one based on support vector machine (SVM)—are used. Those three techniques are designed and compared with the different accuracies among them.

Optimization of the setup position of a workpiece for five-axis machining to reduce machining time

Five-axis machining is commonly used for complicated features due to its advantage of rotary movement. However, the rotary movement introduces nonlinear terms in the kinematic transform. The nonlinear terms are related to the distance between the cutter location (CL) data and the intersection of the two rotary axes. This research studied the possible setup positions after the toolpaths have been generated, and the objective was to determine the optimal setup position of a workpiece with minimal axial movements to reduce the machining time. We derived the kinematic transform for each type of five-axis machines, and then, defined an optimization problem that described the relationship between the workpiece setup position and the pseudo-distance of the axial movements. Eventually, an optimization algorithm was proposed to search for the optimal workpiece setup position within the machinable domain, which is already concerned with over-traveling and machine interference problems. In the end, we verified the optimal results with a case study with a channel feature, which was real cutting on a table-table type five-axis machine. The results show that we can save the axial movements up to 16.76% and the machining time up to 10.70% by setting up the part at the optimal position.

Reliability Prediction of Computing Network with Software and Hardware Failures

In this paper, we investigate the reliability and queueing performance indices for the fault-tolerant computing network having a finite number of unreliable operating components with the provision of warm standby components. Operating and standby components are governed by dedicated software which is also prone to random failure. On failure of operating components, available standby component(s) may switch from the standby state to operating state with negligible switchover time. The switchover process may also fail due to some automation hindrance. The computing network is also subjected to common cause failure in lieu of external cause. The studied redundant fault-tolerant computing network is framed as a Markovian machine interference model with exponentially distributed inter-failure times and service times. For the reliability prediction of the computing network, various performance measures, namely, mean-time-to-failure (MTTF), reliability/availability, failure frequency, etc., have been formulated in terms of transient-state probabilities which we have obtained using the spectral method. To show the practicability of the developed model, numerical simulation has been done. Sensitivity analysis of reliability and other indices of the computing network with respect to different network parameters has been presented, and results are summarized in the tables and graphs. Finally, future scope and concluding remarks have been included.

Application of Statistical Quality Control in Testing Labs for Estimation of Machine Interference

The performance and maintenance of testing laboratories is a prime issue. The quality of coal test results not only depends on performance of individual results but it also depends on the performance of various tests in the same laboratory. Machine interference is a significant problem in many manufacturing system and testing equipment. The variation of results for testing equipment may be due to various factors which need to calculate the uncertainty of measurement to show the accuracy of the machine. In case of coal testing laboratory, the plant layout and surrounding environment affects the performance of the system. The machine interference comes under variable causes which may affect the result. This chapter proposes a methodology for constructing system performance measures, finding out the various factors responsible for variations in result. The chapter deals with estimation of machine interference existence using variable control chart approach for coal testing equipment. The analysis of results for such machine interference will be useful and significant for system designers and practitioners.