Database Machine Research Articles

Design and Development of Database System for Turning and Milling Compound high-speed Machining

In order to realize the advantages of high-speed cutting machine and improve the efficiency of high-speed cutting, a turning and milling compound high-speed machining process database was studied. According to the data information of cutting lathes, workpiece materials, tools and high-speed machining parameters, the functional requirements, system roles and system data relationships of the database system were analyzed. Based on the above information, the functional module, basic database, user permission and system architecture were designed, and a compound high-speed machining database system which is suitable for lathes and milling machines was developed.

Machinability and Delamination Studies on Glass Fiber Reinforced Polymer Matrix Composite Cut by Abrasive Water Jet Machine

Abrasive water jet machining possesses inherent technological and manufacturing advantages unmatched by most machine tools. They are generally used in automotive, marine and aerospace industries for machining of composites. However, abrasive water jet cutting of GFRP laminates possesses several challenges, such as surface finish and delamination of the cutspecimen. Henceforth, more experimental work is needed to provide sufficient machinability databases for manufacturing industries. This paper presents an experimental study for cutting GFRP laminates. Different AWJM conditions including pressure, traverse speed and abrasive flow rate are analyzed using full factorial design of experiments. Machining process responses such as surface roughness (Ra), top and bottom kerf width, kerf taper, delamination of fibers have been evaluated using analysis of variance (ANOVA) technique. The optimum process parameters for AWJM of glass fiber reinforced polymers have beensuggested.

Data Mining in Genomics and Proteomics for a New Era of Computational Biology

Data mining refers to the processes of discovering large data sets which involves the methods of intersection of statistics, Data base systems and machine learning. Genomics refers to the study of structure, function, evolution, and mapping of genomes. Proteomics refers to study of proteins as proteins are the essential things for the growth of function and growth of living organisms. Data Mining in Genomics and proteomics correlates the interface of computer science along with the biological aspects which paves an easy and essential way for future biological researchers throughout the world.

Detection of Optic Disc Drusen in Children Using Ultrasound through the Lens and Avoiding the Lens-Point of Care Ultrasound Technique of Evaluation Revisited.

Aim: To assess whether the detection rate of optic disc drusen (ODD) in children with swollen optic discs varies if the ultrasound scan (USS) is performed through the lens or avoiding the lens. Methods: Retrospective review of the ultrasound machine database for all patients who underwent USS for swollen discs in the department of pediatric ophthalmology, UPMC Children’s Hospital of Pittsburgh. Only patients who had both fundus pictures and USS performed (through and avoiding the lens) were included in the study. Results: A total of 31 patients (62 eyes) were included in the study. USS detected ODD in 44% of eyes (27 of 62 eyes, 15 patients). In 82% of these eyes (22 of 27 eyes), the ODD were not detected initially when scanning was done through the lens but were only detected when scanning was performed avoiding the lens. Ten out of sixteen patients with no ODD on USS had another identifiable cause for disc elevation, including raised intracranial pressure and sleep apnea. Conclusion: Ultrasound is a sensitive diagnostic tool for detecting ODD. The rate of detection of ODD is increased when USS is done avoiding the lens in children where the ODD are usually buried and not as calcified as those found in adults. Under such circumstances, the reduced echogenicity is absorbed by the absorbent pediatric lens, thus limiting the detection rates when scanning through the lens.

AN INTEGRATED SYSTEM FOR PROCESS-FIXTURE LAYOUT DESIGN OPTIMISATION FOR CUBICAL PARTS

In this article, an integrated computer-assisted process-planning and computer-assisted fixture layout planning system is presented for the automatic generation of process and fixture maps. The part feature model is created using various algorithms and the geometric reasoning approach. The feature-based methodology based on the machining database is then applied for the generation of process maps. The setup scheme algorithm allocates each feature to a definite setup based on its location. The part geometric database and setup plan aids the fixture layout planning process. During fixture layout planning, the standard fixture rules are applied to determine the locating arrangement and feasible datum along with the suitable positions using a fixture database for initial fixture layout planning data. Visual C++ is used to implement the proposed methodology because it interacts with current computer-aided design software. Then a case study is presented to develop an initial fixture layout. Afterwards, the ANSYS parametric design language optimisation tool is applied to automatically optimise locator and clamp positions that yield minimum workpiece deformation. At the end, finite element analysis results depicting deformation magnitudes are presented.

Knowledge-based multi-level aggregation for decision aid in the machining industry

In the context of Industry 4.0, data management is a key point for decision aid approaches. Large amounts of manufacturing digital data are collected on the shop floor. Their analysis can then require a large amount of computing power. The Big Data issue can be solved by aggregation, generating smart and meaningful data. This paper presents a new knowledge-based multi-level aggregation strategy to support decision making. Manufacturing knowledge is used at each level to design the monitoring criteria or aggregation operators. The proposed approach has been implemented as a demonstrator and successfully applied to a real machining database from the aeronautic industry.

Review on effect machining parameters on performance of natural fibre–reinforced composites (NFRCs)

In the modern years, natural fibre composites have been converted into significant materials in many industries such as automotive, aerospace and and so on. Several types of natural fibre composites, particularly plant-based fibre composites, have been developed and tested. However, their mixed nature, engineer’s requirement of experience, an understanding of machinability databases, limit setting and trouble in manufacturing are barriers to extensive use of composites. The final shape of the natural fibre–reinforced composites (NFRCs) are obtained by conventional and unconventional machining. Machining of these composites generates confront due to the heterogeneous and anisotropic nature. Different methodologies and tools are intended to overcome the machining defects. In this article, a wide range of literature review on machining of NFRCs is examined with focus on conventional and unconventional machining operation. This article also discusses the influences of machining parameters and optimum conditions for machining of NFRCs.

A REAL TIME COMMIT PROTOCOL BASED ON PRIORITY FOR DRTDBS

With the rapid boom within the subject of information technological understanding, everything is becoming online and need of allotted database application is growing. To coordinate the transaction’s execution several methodologies have been proposed. Distributed real-time database device (DRTDBS) deals with numerous troubles that degrade the machine performance, priority inversion are truly certainly one of them. In DRTDBS primarily based programs, the vital goal is to lessen the amount of transactions missing their ultimate deadline by way of the usage of minimizing commit time. This paper proposes. a real-time commit protocol based mostly on priority to clear up the inversion problem in allotted real-time environments. Focus of this protocol is to lessen the commit processing time through reducing messages and time overhead. The real time overall performance of this protocol is measured with the help of distributed database machine simulation. The effects show the substantial improvement in actual time device overall performance with none consistency problem.

Abrasive Water Jet Machining of Multidirectional CFRP Laminates

Abrasive water jet machining (AWJM) is widely used in aerospace, marine and automotive industries for trimming composites. However, AWJM demonstrates some challenges when cutting carbon fibre reinforced plastic (CFRP) composites materials such as cut accuracy and quality. More experimental work is needed to provide sufficient machinability databases for manufacturing engineers. This paper presents an experimental study and statistical analysis for cutting 2 lay-up configurations of multidirectional CFRP laminates. Different AWJM conditions including jet pressure, feed rate, and standoff distance are experimented using full factorial design of experiments. Machining process responses such as top and bottom kerf width, kerf taper, machinability and surface characteristics have been evaluated using analysis of variance (ANOVA) technique. A process cost model for the AWJM is presented.

New face features to detect multiple faces in complex background

We propose in this paper a novel method for fast detecting faces even in the presence of constraints such as variation in illumination, human skin tone and facial expression, pose, and background (indoor or outdoor). Our system processes color images in a manner that would decrease the area of a face that must be scanned and for this, a parametric model based on Gaussian mixture models (GMM) applied to segmented regions of skin color. To select, relevant and minimum features from the faces candidates firstly, the variance based Haar-like features are extracted than merged with local binary patterns (LBP) features previously extracted. The resulting fused vectors construct Support Vector Machine database training to achieve a high detection rate. To verify the effectiveness of the proposed method, we carried out a serial of detailed experiments on three difficult face detection datasets (Caltech, BAO and UCD) which contain images featuring both single and multiple faces, presented in a variety of positions and featuring complex backgrounds, both indoor and outdoor. Experimental results have shown that our approach gives better results (91.04%) than those obtained by systems based on primitive Haar-like features and AdaBoost, providing a higher detection rate of 16.51%. Furthermore, the shorter detection time of our method is guaranteed by reducing the dimension of feature vectors and by limited search of faces on only the skin-detected regions and not on the entire image.

Systematic Proteogenomic Approach To Exploring a Novel Function for NHERF1 in Human Reproductive Disorder: Lessons for Exploring Missing Proteins.

One of the major goals of the Chromosome-Centric Human Proteome Project (C-HPP) is to fill the knowledge gaps between human genomic information and the corresponding proteomic information. These gaps are due to "missing" proteins (MPs)-predicted proteins with insufficient evidence from mass spectrometry (MS), biochemical, structural, or antibody analyses-that currently account for 2579 of the 19587 predicted human proteins (neXtProt, 2017-01). We address some of the lessons learned from the inconsistent annotations of missing proteins in databases (DB) and demonstrate a systematic proteogenomic approach designed to explore a potential new function of a known protein. To illustrate a cautious and strategic approach for characterization of novel function in vitro and in vivo, we present the case of Na(+)/H(+) exchange regulatory cofactor 1 (NHERF1/SLC9A3R1, located at chromosome 17q25.1; hereafter NHERF1), which was mistakenly labeled as an MP in one DB (Global Proteome Machine Database; GPMDB, 2011-09 release) but was well known in another public DB and in the literature. As a first step, NHERF1 was determined by MS and immunoblotting for its molecular identity. We next investigated the potential new function of NHERF1 by carrying out the quantitative MS profiling of placental trophoblasts (PXD004723) and functional study of cytotrophoblast JEG-3 cells. We found that NHERF1 was associated with trophoblast differentiation and motility. To validate this newly found cellular function of NHERF1, we used the Caenorhabditis elegans mutant of nrfl-1 (a nematode ortholog of NHERF1), which exhibits a protruding vulva (Pvl) and egg-laying-defective phenotype, and performed genetic complementation work. The nrfl-1 mutant was almost fully rescued by the transfection of the recombinant transgenic construct that contained human NHERF1. These results suggest that NHERF1 could have a previously unknown function in pregnancy and in the development of human embryos. Our study outlines a stepwise experimental platform to explore new functions of ambiguously denoted candidate proteins and scrutinizes the mandated DB search for the selection of MPs to study in the future.

Blind image forensics using reciprocal singular value curve based local statistical features

In this article, passive contrast enhancement detection technique is presented using block based reciprocal singular value curve features. Contrast enhancement operation changes the natural statistics of the image and variation in singular value curve is exploited for constructing the feature vector for forgery detection. Various statistical features using reciprocal singular value curve are extracted after multilevel 2-Dimensional wavelet decomposition. Fisher criterion is employed to choose the most discriminating and to discard the redundant features. Experimental results are presented using gray scale, G component and C b image database and support vector machine classifier. Robustness against anti-forensic algorithm and JPEG compression is also presented. The algorithm outperforms all the existing feature based blind contrast enhancement detection methods in terms of detection accuracy.

Surface roughness analysis when diamond turning optical grade rapidly solidified aluminium RSA 905

Ultra-high precision machining is used intensively in the photonics industry for the production of various optical components. Aluminium alloys have proven to be advantageous and are most commonly used over other materials to make various optical components. Recently, the increasing demand from optical systems for optical aluminium with consistent material properties has led to the development of newly modified grades of aluminium alloys produced by rapid solidification in the foundry process. These new aluminium grades are characterised by their finer microstructures and refined mechanical and physical properties. However, the machining database of these new optical aluminium grades is limited and more research is still required to investigate their machinability performance when they are diamond turned in ultrahigh precision manufacturing environment. This work investigates the machinability of rapidly solidified aluminium RSA 905 by varying diamond-turning parameters and measuring the surface roughness over a cutting distance of 4 km. In addition, a predictive surface roughness model was developed. The machining parameters varied in this study were the cutting speed, feed rate and depth of cut. The results showed a common trend of decrease in surface roughness with increasing cutting distance. The lowest surface roughness R a result obtained after 4 km in this study was 3.2 nm. This was achieved using a cutting speed of 1750 rpm, feed rate of 5 mm/min and depth of cut equal to 25 µm. The work carried out provides a reference for machinists and researchers using the material allowing expected results to be predicted and appropriate machining parameters to be selected based on the surface roughness requirements.

Applications of Data Mining in Banking Sector

The data mining (DM) is a great task in the process of knowledge discovery from the various databases. In the corporate sectors, every system has the tough competition with the other system with respect to their value for the business and the financial improvement. Data mining, a dynamic and fast-expanding field, which applies the advanced data analysis techniques, from machine learning, statistics, artificial intelligence or database systems to find out relevant trends, patterns and relations present within the data, information impossible to observe manually. This paper presents the applications of data mining in the banking sectors. It contains a general overview of data mining, providing a definition of the concept, primary data mining techniques and mentioning the main fields for which the data mining can be applied. It also presents the banking business sector which can benefit from the use of DM tools, along with their use cases i.e., retail and insurance banking sector. Also the commercially available DM tools and their key features are discussed within the paper. With the analysis of DM and the business areas that can successfully apply it, it also presents the main features of the DM solution by using the DM software and data languages i.e., based on survey of users throughout the world that can help to improve the customer experiences and decision making that can be applied for the banking system and the architecture, with its main components for the solution, customer segmentation, banking profitability factor, marketing, risk management and customer relations management and the fraud detection. Keywords: Architecture; Banking system; Business; Data mining; Data warehouse.

A Hybrid Machining Database System Using Case-Based Reasoning and Fuzzy Technology

In order to fulfil the actual request of machining process to the machining database system, according to the characteristics and requirements of machining process, the fuzzy technology and case-based reasoning method were introduced to the design of machining database system. The reasoning process of case-based reasoning method and three main aspects of the fuzzy model were discussed. The match of cases and fuzzy comprehensive evaluation were introduced. The function of high-speed machining database was analyzed and the structure of the database was described. The users can use the database to acquire the overall solutions about high-speed machining.

切削参数对新型光学铝级金刚石车削刀具磨损的影响

Critical components in optical devices and optical measuring systems are mainly produced through the ultra-high precision machining. Aluminium alloys have been proven to be advantageous and very commonly used in the photonics industry. This ever-increasing use and demand in optics have led to the development of newly modified grades of aluminium alloys produced by rapid solidification in the foundry process. The newer grades are characterised by the finer microstructures and can improve mechanical and physical properties. Their main inconvenience currently lies in their having a very limited machining database as there is not enough adequate research contributions on their performance in terms of machinability when diamond turned. This paper investigates the machinability of rapidly solidified aluminium RSA 905 by varying diamond cutting parameters and measuring the diamond tool wear over a cutting distance of 4 km. The machining parameters varied were cutting speed, feed rate and depth of cut. The results show that the cutting speed is the most influential parameter on the diamond tool wear. The highest tool wear of 12.2 μm was achieved at the spindle speed of 500 rpm, the feed rate of 25 mm/min and the depth of cut of 15 μm. The lowest tool wear of 2.45 μm was recorded at the spindle of 1750 rpm speed, the feed rate of 5 mm/min and the depth of cut of 5 μm. Generally, a combination of higher cutting speeds, lower feed rates and smaller depths of cut caused less diamond tool wear. Statistical analysis was performed to develop a model for the diamond tool wear. Wear maps were generated from the model to identify zones where the cutting parameters produced the least wear. The results prove the rapidly solidified aluminium is a superior alternative to traditional aluminium alloys and can also provide a reference for machinists using this material.

Effective Leveraging of Targeted Search Spaces for Improving Peptide Identification in Tandem Mass Spectrometry Based Proteomics.

In shotgun proteomics, peptides are typically identified using database searching, which involves scoring acquired tandem mass spectra against peptides derived from standard protein sequence databases such as Uniprot, Refseq, or Ensembl. In this strategy, the sensitivity of peptide identification is known to be affected by the size of the search space. Therefore, creating a targeted sequence database containing only peptides likely to be present in the analyzed sample can be a useful technique for improving the sensitivity of peptide identification. In this study, we describe how targeted peptide databases can be created based on the frequency of identification in the global proteome machine database (GPMDB), the largest publicly available repository of peptide and protein identification data. We demonstrate that targeted peptide databases can be easily integrated into existing proteome analysis workflows and describe a computational strategy for minimizing any loss of peptide identifications arising from potential search space incompleteness in the targeted search spaces. We demonstrate the performance of our workflow using several data sets of varying size and sample complexity.

Shared and service-oriented CNC machining system for intelligent manufacturing process

To improve efficiency, reduce cost, ensure quality effectively, researchers on CNC machining have focused on virtual machine tool, cloud manufacturing, wireless manufacturing. However, low level of information shared among different systems is a common disadvantage. In this paper, a machining database with data evaluation module is set up to ensure integrity and update. An online monitoring system based on internet of things and multi-sensors “feel” a variety of signal features to “percept” the state in CNC machining process. A high efficiency and green machining parameters optimization system “execute” service-oriented manufacturing, intelligent manufacturing and green manufacturing. The intelligent CNC machining system is applied in production. CNC machining database effectively shares and manages process data among different systems. The prediction accuracy of online monitoring system is up to 98.8% by acquiring acceleration and noise in real time. High efficiency and green machining parameters optimization system optimizes the original processing parameters, and the calculation indicates that optimized processing parameters not only improve production efficiency, but also reduce carbon emissions. The application proves that the shared and service-oriented CNC machining system is reliable and effective. This research presents a shared and service-oriented CNC machining system for intelligent manufacturing process.

Engineering database hardware and software together

Since its inception, Oracle's database software primarily ran on customer configured off-the-shelf hardware. A decade ago, the architecture of conventional systems started to become a bottleneck and Oracle developed the Oracle Exadata Database Machine to optimize the full hardware and software stack for database workloads. Exadata is based on a scale-out architecture of database servers and storage servers that optimizes both OLTP and Analytic workloads while hosting hundreds of databases simultaneously on the same system. By using database specific protocols for storage and networking we bypass limitations imposed by conventional network and storage layers. Exadata is now deployed at thousands of Enterprises including 4 of the 5 largest banks, telecoms, and retailers for varied workloads such as interbank funds transfers, e-commerce, ERP, Cloud SaaS applications, and petabyte data warehouses. Five years ago, Oracle initiated a project to extend our database stack beyond software and systems and into the architecture of the microprocessor itself. The goal of this effort is to dramatically improve the performance, reliability and cost effectiveness of a new generation of database machines. The new SPARC M7 processor is the first step. The M7 is an extraordinarily fast conventional processor with 32-cores per socket and an extremely high bandwidth memory system. Added to its conventional processing capabilities are 32 custom on-chip database co-processors that run database searches at full memory bandwidth rates, and decompress data in real-time to increase memory bandwidth and capacity. Further, the M7 implements innovative fine-grained memory protection to secure sensitive business data. In the presentation we will describe how Oracle's engineering teams integrate software and hardware at all levels to achieve breakthrough performance, reliability, and security for the database and rest of the modern data processing stack.

Form Accuracy of Optical Mould Inserts Made from Rapidly Solidified Aluminium Alloys

Plastic optical components and lenses produced in mass quantities are usually manufactured using high-precision plastic injection technology. For that, high-precision plastic moulds with aluminium optical inserts made with extremely high dimension accuracy and high optical surface quality are used. Ultra-high precision single-point diamond turning have been successfully used in shaping optical mould inserts from various aluminium grades such as traditional 6061. However, extreme care should be taking when selecting machining parameters in order to produce optically valid surfaces before premature tool wear takes place especially when the machined optical materials has inadequate machining database. The current experimental study looks at the effect of cutting conditions on optical surfaces made from aluminium. The study embarks on helping establish some diamond machining database that helps engineers select the most favourable cutting parameters. The papers reports on the accuracy and surface finish quality received on an optical surface made on mould inserts from a newly developed aluminium alloy. Rapidly solidified aluminium (RSA) grades have been developed recently to address the various problems encountered when being cut by single-point diamond turning operation. The material is characterised by its extremely fine grained microstructure which helps extend the tool life and produce optical surfaces with nanometric surface finish. It is found the RSA grades can be successfully used to replace traditional optical aluminium grades when making optical surfaces. Surface finishes of as low as 10 nanometres and form accuracy of less than one micron can be achieved on RSA.