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Tool wear leads to a reduction in dimensional accuracy and surface quality, as well as unexpected sudden tool failure. A broken tool can cause irreparable damage to an expensive workpiece, resulting in increased operating costs and production delays. Since the mechanical strength of small-diameter drills is inadequate for the load and prone to breakage, tool condition monitoring and diagnosis is important to prevent sudden tool breakage, increase productivity, and promote automation in machining process. The present work is aimed to investigate a tool condition monitoring method based on the analysis of acoustic emission (AE) signals emitted during small-hole drilling. We propose DDM (Deep feature Distribution Modeling), a method for image-level anomaly detection and anomaly segmentation in time-series signal analysis. The peck drilling experiments on SKD61 steels were performed with high-speed steel (HSS) drills. The continuous wavelet transform (CWT) was applied to generate time-frequency (TF) image of the AE signals during the drilling process. The TF images were quantified as anomaly scores using the DDM, which establishes normality by fitting a multivariate Gaussian (MVG) to pre-trained deep features. The anomaly detection capability of the DDM and the convolutional autoencoder (CAE) was compared using dummy data for validation. The digital microscope was employed to measure tool wear. Chip morphology was also observed by the laser microscopy. As the tool wear progressed, the anomaly score increased or decreased, with several sharp increases observed between holes 3805 and 3869 just prior to tool failure. An increase in the width of the shear layer spacing of the chips was also observed just prior to failure. Changes in the anomaly score associated with tool wear were more clearly identified by creating anomaly maps. The present investigation shows that waveform processing of AE signals using the CWT and anomaly detection based on the DDM are efficient methods for tool condition monitoring. Our proposed approach makes it possible to visualize the differences in anomaly states using a more subdivided layer context by generating multiple anomaly maps with deep feature vectors obtained from multiple layers.

The Semanggol Formation has been studied widely and published by many, but only focus on areas of northern and southeast Kedah. Limited data have been studied and published on Perak area, especially Gunung Semanggol. The research is aimed to augment the presently limited knowledge on the hill, particularly the subsurface structure. In addition, new lithology such as a breccia formation with tens of meters width of what appears to be a breccia granitic matrix was identified in an abandoned quarry. Therefore, a study on the application of geophysical methods covering the Semanggol Formation located at Semanggol area has been carried out. The main aim of this study was to conduct a magnetic survey to study the subsurface and possibility of granitic bodies in Gunung Semanggol. The total magnetic field data were obtained by using a proton free-precision magnetometer, recording the total ground magnetic field along the transverse. There was a total of 184 magnetic stations established during the survey with a 500 m to 1 km spacing. Data reduction was processed using Oasis Montaj software to produce Total Magnetic Intensity (TMI), Reduction to Pole (RTE), Regional Anomaly and Residual Anomaly maps for qualitative interpretation. The interpreted field data gave the results of total component measurements of magnetic anomaly varying between minimum negative value around 180 nanoTesla (nT) and maximum around 510 nanoTesla (nT) as shown in TMI map. Besides, the regional magnetic map shows a gradual decrement of magnetic intensity towards east and southeast. And residual magnetic map reveals that the dominant orientation for magnetic lineaments in the NE-SW direction. However, geochemical tests have to be conducted to study the possibility of granitic bodies. [ABSTRACT FROM AUTHOR]

In applied geochemistry, obtaining quantitative descriptions of geochemical patterns and identifying geochemical anomalies are important. To identify and separate geochemical anomalies, several statistical methodologies (nonstructural and structural) are presented by researchers. In this study, four nonstructural methods including threshold assessment method based on median and standard deviation, median absolute deviation (MAD), P. N product and Sinclair's method are selected first and then U-statistic is considered as a structural method to compare their performance. Subsequently, the best method is used to assess prospective areas of Parkam district. Results show that P. N and Sinclair's methods are not always efficient. MAD method reduced the background well and roughly increased the correlation factor of points. However, U-statistic method includes both mentioned advantages meaning in addition to reducing outlier data effect, it regularizes anomalous values and also their dispersion is reduced significantly. It is possible to determine anomaly areas according to anomalous samples positioning so that denser areas are more important. Finally, lithogeochemical map of study area is generated for copper and molybdenum. In this map, the Cu mineralization which is delineated by this method is closely associated with the defined potassic alteration zone (according to alteration map of the study area), and also, the delineated Mo mineralization is exactly associated with the phyllic alteration and is spatially conformable with the zone defined for it. [ABSTRACT FROM AUTHOR]

There are several statistical methodologies presented for separating anomalous values from background leading to determination of anomalous areas. These methods range from simple approaches to complicated ones and include nonstructural and structural methods, subtraction separation method and so on. Structural methods take the sampling locations and their spatial relation into account for estimating the anomalous areas. The U-statistic method is one of the most important structural methods. It considers the location of samples and carries out the statistical analysis of the data without judging from a geochemical point of view and tries to separate subpopulations and also to determine anomalous areas. In the present study, several nonstructural methods including assessment of threshold based on median and standard deviation, median absolute deviation (MAD) and P.N product are used and U-statistic is considered as structural method to assess prospective areas of Parkam district. Results show that MAD method reduced background well and P.N method increased correlation of points. However, U-statistic method plays the role of both mentioned advantages meaning in addition to reducing outlier data effect, it regularizes anomalous values and also their dispersion is reduced significantly. It is possible to determine anomaly areas according to anomalous samples positioning so that denser areas are more important. Finally, lithogeochemical map of study area is provided for lead and zinc. [ABSTRACT FROM AUTHOR]