Automation Of Quality Control Research Articles

Robotization of the IR-Thermographic technique – impact on the visualisation quality and considerations on the data workflow

Quality control automation is becoming increasingly popular in industrial production lines. Active thermography techniques are well-regarded for their adaptability, providing fast, non-contact, and full-field non-destructive evaluation. Automating thermographic evaluation can increase assessment speed and repeatability without sacrificing inspection accuracy. By using a robot arm to manipulate the thermographic setup, it becomes possible to inspect large components and refine scans on suspicious zones, even in parts with complex geometries. In this study, the performance of a new thermographic inspection platform is compared with a conventional setup to showcase the potential improvements. A plastic curved-shaped sample with artificial flat bottom hole defects is used as a benchmark for the comparison. The advantages and disadvantages of robotizing the infrared non-destructive setup are analyzed, and the impact of the data workflow and future research activities are also discussed.

Automation of quality control of digital topographic maps at the scale 1:50 000 of the Main State Topographic Map in Ukraine

Abstract. This research examines the problem of automation of quality control of digital topographic maps at the scale 1:50 000 of the Main State Topographic Map in Ukraine for the creation and maintaining the seamless topographic database for national needs, which is located on the Geoportal to ensure the development of the National Spatial Data Infrastructure (NSDI) in Ukraine. The authors determined stages of quality control of digital topographic maps at the scale of 1:50 000 for the automation one according to ISO 19157:2013 Geographic Information – Data quality. The realized automated quality control of geodatabases of updated digital topographic maps at a scale of 1:50 000 provides high speed and quality of validation: validation of quality data elements; validation of quality metadata; validation of positional accuracy. The software package “Validate” was developed for automation quality control geodatabases of digital topographic maps at the scale of 1:50 000 using the Python programming language to verify logical consistency, compliance with the rules of topological relationships between features on the map, availability and content of metadata. The “Validate” can be used for automation quality control geodatabase for any map scale, performing setting according to the requirements of the geospatial models.

Automating the optical identification of abrasive wear on electrical contact pins

Abstract The automation of quality control in manufacturing has made great strides in recent years, in particular following new developments in machine learning, specifically deep learning, which allow to solve challenging tasks such as visual inspection or quality prediction. Yet, optimum quality control pipelines are often not obvious in specific settings, since they do not necessarily align with (supervised) machine learning tasks. In this contribution, we introduce a new automation pipeline for the quantification of wear on electrical contact pins. More specifically, we propose and test a novel pipeline which combines a deep network for image segmentation with geometric priors of the problem. This task is important for a judgement of the quality of the material and it can serve as a starting point to optimize the choices of materials based on its automated evaluation.

Automation of Quality Control Process without the Usage of Complex Information Systems

Brief analysis of the actual problems of modern quality management and the importance of product quality are highlighted in this article. An overview of the application of information technology in quality management and existing class of information systems that are used for quality control are given. Implementation of algorithm of quality control without the use of complex IS and the assessment of economic effect of the algorithm’ implementation are presented. Finally, a pathway for partial quality control automation without the usage of information systems is suggested.

Printing quality control automation

One of the most important problems in the concept of standardizing the process of offset printing is the control the quality rating of printing and its automation. To solve the problem, a software has been developed taking into account the specifics of printing system components and the behavior in printing process. In order to characterize the distribution of ink layer on the printed substrate the so-called deviation of the ink layer thickness on the sheet from nominal surface is suggested. The geometric data construction the surface projections of the color gamut bodies allows to visualize the color reproduction gamut of printing systems in brightness ranges and specific color sectors, that provides a qualitative comparison of the system by the reproduction of individual colors in a varying ranges of brightness.

Vision system in quality control automation

Measurement of surface roughness is one of the quality control processes, usually carried out off line. Contact type surface roughness measurement method is commonly used in quality control. The processes consume lot of time with human interaction. In order to reduce or to eliminate non value added time, effective quality inspection tool and automation of the processes has to be utilized. An attempt has been made to automate the process with integration of vision camera in capturing the image of the component surface. The image process technique has the advantage of analyzing the single captured image for multiple area measurement. Hence, the in-line quality control of each component surface roughness measurement is ensured. The automation process involves component movement, image capturing, image processing, and decision making, using sensors, actuators and microcontroller. The proposed in-line quality control of surface roughness with vision system has been successfully developed. The designed automated system has fulfilled the objectives in respect of the scope of the present work.

Vision system in quality control automation

Measurement of surface roughness is one of the quality control processes, usually carried out off line. Contact type surface roughness measurement method is commonly used in quality control. The processes consume lot of time with human interaction. In order to reduce or to eliminate non value added time, effective quality inspection tool and automation of the processes has to be utilized. An attempt has been made to automate the process with integration of vision camera in capturing the image of the component surface. The image process technique has the advantage of analyzing the single captured image for multiple area measurement. Hence, the in-line quality control of each component surface roughness measurement is ensured. The automation process involves component movement, image capturing, image processing, and decision making, using sensors, actuators and microcontroller. The proposed in-line quality control of surface roughness with vision system has been successfully developed. The designed automated system has fulfilled the objectives in respect of the scope of the present work.

USING THIRD PARTY DATA TO UPDATE A REFERENCE DATASET IN A QUALITY EVALUATION SERVICE

Abstract. Nowadays it is easy to find many data sources for various regions around the globe. In this 'data overload' scenario there are few, if any, information available about the quality of these data sources. In order to easily provide these data quality information we presented the architecture of a web service for the automation of quality control of spatial datasets running over a Web Processing Service (WPS). For quality procedures that require an external reference dataset, like positional accuracy or completeness, the architecture permits using a reference dataset. However, this reference dataset is not ageless, since it suffers the natural time degradation inherent to geospatial features. In order to mitigate this problem we propose the Time Degradation & Updating Module which intends to apply assessed data as a tool to maintain the reference database updated. The main idea is to utilize datasets sent to the quality evaluation service as a source of 'candidate data elements' for the updating of the reference database. After the evaluation, if some elements of a candidate dataset reach a determined quality level, they can be used as input data to improve the current reference database. In this work we present the first design of the Time Degradation & Updating Module. We believe that the outcomes can be applied in the search of a full-automatic on-line quality evaluation platform.

MAV-seq: Platform for the NGS Data Workflow Management and Automation

Event Abstract Back to Event MAV-seq: Platform for the NGS Data Workflow Management and Automation Zeeshan Ahmed1* 1 The Jackson Laboratory, Genomic Medicine, United States The increasing amount of unparalleled heterogeneous genomic data generated today necessitates a robust platform for dealing with the practical issues of genomic data interoperability, structure, standardization, security, quality, pre-processing, governance development, long-term support, management of exponential growth of genomic applications and their datasets of enormous size and diversity. Addressing most of these living challenges and practical issues to the genomic big data club, here, we present a new scientific platform i.e. MAV-seq (Ahmed et al., 2016), towards automated management and processing of Next Generation Sequencing (NGS) data. MAV-seq (Management, Analysis, Visualization of Sequence data) is an interactive, user friendly, cross platform, secure, encrypted, automated, customized, centralized, multi-roles based database application for the management of sample repertoires and automation of the data pre-processing of epigenomic and transcriptomic data. It supports: • Study data management • Experiments & projects data management • Centralized sample metadata management • Centralized NGS data management • Automation of NGS data quality checking • Automation of NGS data pre-processing • GUI based access to the data clusters for NGS data transfer and management • Customized data export and sharing • Efficient data linking, tracking, querying and searching • Extraction, classification and loading of data from different formats • Users data and control management • Data security and encryption • Event management and logging • Centralized and modular data administration • Privatization and globalization of data MAV-seq (Figure 1) is a secure database management system, which deals with the security threats including privilege abuse, weak authentication, weak system configuration, backup, front and back end system vulnerabilities. It applies different data encryption algorithms to encode data and provides controlled system’s access to the users based on their roles and privileges. It provides easy to use interfaces for raw data management, operational data management, user data management and analysis of genomic data, which includes: classification, tracking, processing, querying and visualization of data. MAV-seq is a product line application, developed following different bioinformatics methods, software engineering principles, Butterfly paradigm (Ahmed et al., 2014), human computer interaction guidelines and big data analytics. MAV-seq integrates various genomic data quality check and pre-processing pipelines (e.g. ATAC-seq, ChIP-seq, mRNA-seq, tRNA-seq, WES, WGS etc.) with user-friendly graphical interface to enable biologist with no programming experience to process their NGS datasets. It requires Java Runtime Environment to be installed on in-use operating system (e.g. Windows, MacOSX etc.) with all integrated applications to be downloaded and installed in data cluster and referenced genome for mapping. With this platform, we aim to simplify management and storage of NGS datasets including the standardization and automation of quality control and basic processing steps. MAV-seq is very simple and easy to learn platform, which does not require bioinformatics and programming abilities. Figure 1 Acknowledgements We acknowledge The Jackson Laboratory for Genomic Medicine for the financial support and ownership of this research and development.

Signal Quality in Audiovisual Digital Preservation

Audiovisual files hold a signal or an image, and so have a quality dimension that has no equivalent in text files. Digital Preservation projects provide guidance for optimizing the preservation of ‘significant properties’, but audiovisual content also have purely technical dimensions to preserve: signal fidelity and image quality. For digital libraries, automation of signal quality control is necessary, both at time of original input and for every migration or publication thereafter – because manual checking does not scale to large collections. The paper reviews quality control automation and presents a graphical approach to tracking quality over an item's entire life cycle.

Automated cement plant quality control

Increasing production and quality demands in the cement industry have resulted in the need for improvement in the speed and accuracy of quality-control results. This need has been addressed many technological advances in quality-control automation in recent years. Two distinct methods of sampling and analysis, in-situ and extractive, have gradually developed. In-situ analysis occurs in the field at the process equipment, with the results transmitted through a computer control system. This article discusses the characteristics and differences in analyzer and control technologies that utilize the two different methods of obtaining a result.

Automation of gas chromatography instruments. Part II. A knowledge-based system for performance assessment

A knowledge-based system that uses experimentally determined relationships between a gas chromatography (GC) system and the properties of a standard test mixture for automation of quality control and diagnosis of GC instruments is proposed. The approach is to run standard test samples regularly, the results are analyzed automatically, and the system performance is evaluated by the expert-system-based software. When the GC system is set up for standard tests, four Relative Performance Indices are derived that relate the response of this specific instrument to the chemical or physical properties of the samples of interest. Possible component faults are diagnosed in order to ensure that the system operates under perfect condition. This new system can detect faulty components in a GC system and trace the system changes over time. The study indicates that the performance of instruments can be automatically monitored by measuring the changes in the parameters extracted from a standard test sample.

Automatic High Voltage Continuity Test for Winding Wires

Abstract The paper describes a whole quality control automation for a manufacturing plant of winding wires. Every section contains several production machines which produces from 8 to 24 wires simultaneously. In a first step, we develope a high voltage continuity test apparatus connected to a microprocessor board. Theboard´s function is to save the results coming from eight test apparatuses and preprocess them for further transmission. From 1 to 3 boards may be placed into a 24rack which has a keyboard and an alfanumerical display for on-machine programming and visualization. This device also provides visual and acoustic alarms. The control unit receives data from the microprocessor boards by RS-485 links (up to 16 microprocessor boards forevery link) and generates a single label with the quality parameters for every coil. From the control unit or fromtheon-machine device it is possible to change many parameters such as test voltage or alarms configuration.

A Multi-processor Approach for the Automation of Quality Control in an Overall Production Control System

A modular standardized DNC-System is described which is based on the CAMAC Standard. Real Time FORTRAN is used to allow easy adaption of the system to individual production requirements. In a second step the Operating Data Acquisition is added. Quality control is a substantial part of the total production costs of the plant, where fuel controllers for aircraft jet engines are manufactured. The automation of the quality control with its test rigs is achieved by a Multi-Micro Computer System, thus forming an overall Production Control System.