Fundamental Approach to Dipmeter Analysis: ABSTRACT

The numbers of hard disk drive heads manufactured in Thailand have increased rapidly in the past few years, and one of the most important components of the hard disk drive head is the hard disk drive arm. This component has been produced in large amounts and has been a major income source for a case study company. The manufacturing process of the hard disk drive arm, especially the machining process, is highly complicated and also a main factor of defining the usability of the final product. However, during dimension inspections, many defected parts were detected, resulting in an overall decrease in productivity, sales and profit. Normally, parts are randomly chosen from each CNC machine to be inspected. If there is a defective product, that machine would be shut down and the tool settings will be reset. If the machine that has been producing defective products is inspected late into the inspection shifts, it would result in a considerable amount of defective parts produced before any corrective actions can be made. Therefore, this study presents an application of the integration between Multiple Attribute Decision Making (MADM) and Data Mining (DM) to define the inspection order for tooling adjustment of which the machines with higher risk of producing defective parts can be inspected and corrected before those with lower risk. The methodology is as follows. First, raw measurement data from each machine was collected and noise elimination was performed using the anomaly clustering method. Secondly, K-means clustering, after a machine performance hypothesis test to confirm that the performance of each machine is not equal, was opted for dividing the raw data into three clusters, consisting of good, normal, and bad machines. Finally, a daily CNC machine priority assessment model (CNC-MPAM) is developed based on the Simple Additive Weight (SAW) technique, resulting in a machine performance score for optimal ordering of machine tooling adjustment. Experimental results suggested that this proposed method is capable of machine adjustment ordering so that defective prone machines can be serviced sooner, reducing defective parts produced and improving overall productivity.

There are many different uses for the data that comes from a measurement. With the advent of new testing methodologies, data has become finely granular—allowing for precise analysis of the properties and performance of materials, geometries, and even simulations. Raw measurement data can be retained and compared directly to the output of complex acoustic models for development, improvement, evaluation, and eventually a form of calibration. Advances in technology are rapidly removing the limitations of computational power needed to create accurate models of acoustic phenomena in a timely and efficient manner. This allows a progression from limited particle simulations on single workstations, to large scale wave-based simulations using cloud-based GPU computing clusters. Performance will be evaluated, comparing their output to laboratory measurements, with the goal of creating better tools for acoustic design, prediction, experimentation, and development.There are many different uses for the data that comes from a measurement. With the advent of new testing methodologies, data has become finely granular—allowing for precise analysis of the properties and performance of materials, geometries, and even simulations. Raw measurement data can be retained and compared directly to the output of complex acoustic models for development, improvement, evaluation, and eventually a form of calibration. Advances in technology are rapidly removing the limitations of computational power needed to create accurate models of acoustic phenomena in a timely and efficient manner. This allows a progression from limited particle simulations on single workstations, to large scale wave-based simulations using cloud-based GPU computing clusters. Performance will be evaluated, comparing their output to laboratory measurements, with the goal of creating better tools for acoustic design, prediction, experimentation, and development.

The thruster fault diagnosis problem of the satellite attitude control system is investigated in this article. This challenging problem is first changed into the binary image classification issue. A deep learning based data-driven fault diagnosis approach is then presented. It benefits from this approach that the stuck-open and the stuck-close faults of thruster are detected, diagnosed, and located online with high accuracy. The proposed method is purely data-driven and directly implemented by using raw measurement data only. It is independent of the dynamics of the thruster and the mathematical model of the satellite attitude control system. The effectiveness of the proposed approach is finally demonstrated on a satellite example.

ThedilatometerprobedevelopedbySolexpertsAG (Switzerland)has beensuccessfully used for more than 30 years. Determination of the mechanical properties of the rock mass is based on real-time measurement of the applied pressure and the resulting deformation ‐ the change of the borehole diameter. The dilatometer probe consists of a metal core, which holds three potentiometeric displacement transducers placed in the centre of the core, Figure 1. The axes of the transducers are oriented 120˚ to each other and vertical distance between the transducers is 75 mm. The borehole wall is loaded by means of a reinforced one meter long packer sleeve fixed to the core of the probe at its both ends. The packer can be expanded by compressed nitrogen or air via a high-pressurehoseconnected to either nitrogen bottles or a compressor. The deformation of the borehole wall is directly measured by the transducers, which contact the borehole wall through steel pins with spherical heads. The installation of the probe into the borehole is usually done with a drilling rig. A dilatometer test has usually two to four loading cycles, as described in the example from the investigation for the Brenner Base Tunnel, Figure 2. At first,

The main aim of well log interpretation is to determine the reservoir geological and petrophysical properties downhole as well as properties fluids and their distributions in a reservoir, and identification of hydrocarbon from well logs is the most important job. Neural networks, with their great capability in adaptation and expressing arbitrary complexity, seem especially suited to solve for the reservoir problems where there widely exist uncertainty, fuzziness and nonlinearity. Much work has been published on the identification of lithology and lithofacies from well logs using neural networks. But very few, and almost no work could be found to deal with identifying hydrocarbon from well logs in this neural network approach In this paper, a supervised neural network (a multi-layer perceptron (MLP)) and two unsupervised neural networks (a self-organizing mapping (SOM) net and a fuzzy neural network (FNN) are used to identify oil from well log, and almost each of them can distinguish between oil, water, oil-water transitionand dry zones directly from well logs, but with different accuracy. A case study from a Chinese oil field is given to show their advantages and limitations, and their implementation is also given. The associated oil test results are available to demonstrate their dependability And it is shown that there is a very good agreement between results oil test and oil identification from both LP and FNN. Introduction Generally, the main task of well log interpretation is to determine the geological and petrophysical properties of reservoir rock formation as well as properties of formation fluids and their distributions. Obviously, well logs are the fundamental information source of well log interpretation, and the eventual aim of well log interpretation in oil exploration is to determine both roperties of subsurface fluids and their distribution, which is not directly from well logs and usually based on the predetermined geological and / or petrophysical properties from well logs. During this conventional log interpretation process, it often needs selection of empirical formula and various empirical parameters, which makes the actual log interpretation extremely complicated and subjective, and eventually lead to the extreme difficulty in enhancing the chance of success of well log interpretation. Well log interpretation, in certain viewpoint, is to invert subsurface geological and petrophysical properties, as well as properties of subsurface fluids and their spatial distributions from well logs. And well log responses measured with wireline tools, which reflect the properties of the subsurface reservoir, are in extremely complex relationship with various reservoir properties, because of the high heterogeneity and anisotropy widely existent in reservoir medium. Thus the solution of the conventional log inversion is not of singularity. So, primarily, log analysts could simply interpret well logs qualitatively, aided with their field experience obtained from success and fail by trial and error, that is, visual log interpretation of oil. Because of advances of logging technology and improvement of various experiment techniques as well as introduction of advanced scientific technology such as computers, the log analysts began to quantitatively determine both the properties of subsurface fluids and their distributions from well logs, according to various empirical formula obtained by experiments and various mathematical and physical models established on basis of theoretical analysis, which eventually made well log interpretation grow to be a largely integrated engineering science, that is, the computerized og interpretation stage started when the identi

A new and still experimental method for measuring the absolute in-situ stress field in crustal rocks offers several advantages compared to existing in-situ stress measurement techniques. It employs optical holography to record strain-relief displacements in a borehole environment. We call the prototype instrument the holographic stressmeter. It operates in an uncased borehole where it drills strain-relieving side holes into the borehole wall. An interference holographic recording system records the resulting displacements onto film. The reconstructed interference holograms contain sufficient information in their fringe patterns to determine the three-dimensional vector displacements due to strain relief at every point surrounding the side hole. Assuming isotropic, homogeneous, linearly elastic rock, these displacements define the three stresses acting at the borehole wall at a single point. The three non-vanishing stresses acting at each of three points, distributed azimuthally, on the borehole wall provide sufficient constraint to determine all six components of the desired far-field or virgin-rock stress tensor. The holographic stressmeter employs an on-board side hole drilling system to produce strain relief. Thus it should be economical to operate and it is not restricted to shallow depths as are overcoring techniques. Furthermore, recording the whole displacement field resulting from drilling the side hole reduces the potential contamination of the measurement by residual stress mechanisms which often affect point strain measurements using foil resistance gauges. To date a complete stress determination in the field has not been attempted. However, a prototype stressmeter has demonstrated repeatedly that the stability necessary to conduct the measurement using this approach is attainable. Results from field deployment show that the stressmeter can make qualitatively correct measurements at one azimuth in a borehole. Modifications to make measurements at the three azimuths required for a complete determination of the stress tensor components should be easily achieved. We propose additional improvements to permit measuring rock elastic properties in situ to enable accurate, quantitative stress determinations. The theoretically predicted precision of the stress component magnitudes using this measurement approach is estimated at 20%.

Larger well diameters allow higher groundwater abstraction rates. But particularly for the construction of wells at greater depth, it may be more cost‐efficient to only expand the borehole in the target aquifer. However, current drilling techniques for unconsolidated formations are limited by their expansion factors (<2) and diameters (<1000 mm). Therefore, we developed a new technique aiming to expand borehole diameters at depth in a controlled manner using a low‐pressure water jet perpendicular to the drilling direction and extendable by means of a pivoting arm. During a first field test, the borehole diameter was expanded 2.6‐fold from 600 to 1570 mm at a depth of 53.5 to 68 m and equipped with a well screen to create an expanded diameter gravel well (EDGW). In keeping with the larger diameter, the volume flux per m screen length was two times higher than conventional 860 mm diameter wells at the site in the subsequent 3 year production period. Although borehole clogging was slower on a volumetric basis and similar when normalized to borehole wall area, rehabilitation of particle clogging at the borehole wall was more challenging due to the thickness of the gravel pack. While jetting the entire borehole wall before backfilling holds promise to remove filter cake and thus limit particle clogging, we found that a second borehole (expanded 4.1‐fold to 2460 mm) collapsed during jetting. Overall, the EDGW technique has potential to make the use of deeper unconsolidated aquifers economically (more) feasible, although further understanding of the borehole stability and rehabilitation is required to assess its wider applicability.

Real-time local smoothing for five-axis linear toolpath considering smoothing error constraints

Five-axis machine tools are widely applied in machining parts with complex structure or geometric shape because of the two additional rotary axes. Meanwhile, the solution space of the tool orientations is significantly enlarged. Interference is considered in most traditional methods to compute feasible tool orientation space. However, since the surface quality requirements keep increasing, machining stability should be seriously confirmed. This paper proposes a new tool orientation planning method by considering both interference and chatter avoidance. Interference-free tool orientation space is first computed at each cutter location point (CLP). Then the machining stability is evaluated to eliminate the tool orientations leading to machining chatter from the interference-free tool orientation space. Based on this interference-and-chatter-free tool orientation space, a tool orientation smoothing strategy is implemented. The proposed method is tested by two cases and the experimental comparison results show that interference and chatter avoidance can be achieved. [Submitted 26 January 2018; Accepted 27 March 2018]

Convolutional neural network-based power system transient stability assessment and instability mode prediction

In this paper we propose the method that detects moving objects in autonomous navigation vehicle using LRF sensor data. Object detection and tracking methods are widely used in research area like safe-driving, safe-navigation of the autonomous vehicle. The proposed method consists of three steps: data segmentation, mobility classification and object tracking. In order to make the raw LRF sensor data to be useful, Occupancy grid is generated and the raw data is segmented according to its appearance. For classifying whether the object is moving or static, trajectory patterns are analysed. As the last step, Markov chain Monte Carlo (MCMC) method is used for tracking the object. Experimental results indicate that the proposed method can accurately detect moving objects.

Presented in this paper is a method for four-axis rough milling of sculptured surface turbine blade. The proposed method consists of two steps, cutter-contact (CC) points generation and tool orientation planning. In the first stage, CC points for trimmed blade surface are generated in the parametric domain and then they are converted into the physical domain to get flow line toolpaths. In the second stage, optimized representative tool orientations are generated first, and then interpolation algorithm is employed to determine all tool orientations. With this method, smooth tool orientations can be obtained. Finally, computational examples were implemented to generate symmetrical spiral four-axis rough milling toolpath for the turbine blade disk. Simulation results show that the proposed method is efficient and it can improve the utilization of four-axis machine tools in the milling of turbine blade.

Abstract. Expedition 364 was a joint IODP and ICDP mission-specific platform (MSP) expedition to explore the Chicxulub impact crater buried below the surface of the Yucatán continental shelf seafloor. In April and May 2016, this expedition drilled a single borehole at Site M0077 into the crater's peak ring. Excellent quality cores were recovered from ∼505 to ∼1335 m below seafloor (m b.s.f.), and high-resolution open hole logs were acquired between the surface and total drill depth. Downhole logs are used to image the borehole wall, measure the physical properties of rocks that surround the borehole, and assess borehole quality during drilling and coring operations. When making geological interpretations of downhole logs, it is essential to be able to distinguish between features that are geological and those that are operation-related. During Expedition 364 some drilling-induced and logging-related features were observed and include the following: effects caused by the presence of casing and metal debris in the hole, logging-tool eccentering, drilling-induced corkscrew shape of the hole, possible re-magnetization of low-coercivity grains within sedimentary rocks, markings on the borehole wall, and drilling-induced changes in the borehole diameter and trajectory.

Upper Dalan Member and Kangan Formation between the Zagros Mountains and offshore Fars, Iran: depositional system, biostratigraphy and stratigraphic architecture

Acoustic source localization based on time difference of arrival (TDOA) measurements from spatially separated sensors is an important problem in wireless sensor networks (WSNs). While extensive research works have been performed on algorithm development, limited attention has been paid in how to form the sensor pairs. In the literature, most of the works adopt a centralized sensor pairing strategy, where only one common sensor node is chosen as the reference. However, due to the multi-hop nature of WSNs, it is well known that this kind of centralized signal processing method is power consuming since raw measurement data is involved in the transmissions. To reduce the requirements for both network bandwidth and power consumptions, we propose an in-network sensor pairing method to collect the TDOA measurements while guaranteeing the quality of source localization. The solution involves finding a minimal sized dominating set (MSDS) for a graph of the muti-hop network. It has been proved that in-network sensor pairing can result in the same Cramer-Rao-Bound (CRB) as the centralized one but at a far more less communication cost. Furthermore, the structure of the proposed in-network sensor pairing coincides with the decentralized source localization, which is an important application of our method.