Artificial IntelligenceBased Hole Quality Prediction in MicroDrilling Using Multiple Sensors. The prevalence of microholes is widespread in mechanical, electronic, optical, ornaments, microfluidic devices, etc. However, monitoring and detection tool wear and tool breakage are imperative to achieve improved hole quality and high productivity in microdrilling. The various multisensor signals are used to monitor the condition of the tool. In this work, the vibration signals and cutting force signals have been applied individually as well as in combination to determine their effectiveness for toolcondition monitoring applications. Moreover, they have been used to determine the best strategies for toolcondition monitoring by prediction of hole quality during microdrilling operations with 0.4 mm microdrills. Furthermore, this work also developed an adaptive neuro fuzzy inference system (ANFIS) model using different time domains and wavelet packet features of these sensor signals for the prediction of the hole quality. The best prediction of hole quality was obtained by a combination of different sensor features in wavelet domain of vibration signal. The modelxe2x80x99s predicted results were found to exert a good agreement with the experimental results.

Reliability Assessment of Mining System Based on Time Mining Data The degradation of mechanical systems is a typical phenomenon accompanying most systems. When considering dependability, safety and costeffectiveness, the degradation may result in serious consequences. Direct advancing degradation in all technical systems is not easy to observe. In order to do so, related information is used, e.g. the study of diagnostic and operation measuressignals. This article presents a study of the deterioration of a mining head with multitool knives. There is a dataset containing the records of the drilling head behaviour in standard operation. The records contain typical operation characteristics such as moment and power for both sharp and blunt knives. Degradation modelling of the studied mining head knives is performed with stochastic continuous diffusion processes. They are Pearson type, GaussMarkov type and Levy type processes. The achieved results are expected to be used for the observation of i) the first passage time of degradation critical value), ii) the prediction of residual useful life, and iii) the rationalization of in field operation and maintenance.