Evaluation of Quality Control Performance of Gem Premier 4000 Using Inbuilt Intelligent Quality Management on Sigma Scale (original) (raw)
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2015
The international standard ISO/IEC 17020 contains the requirements for work of various inspection bodies which aim to raise the level of their activities, as well as the confidence level of clients in performing inspection activities. Furthermore, the standard is aligned with other standards of series standards ISO/IEC 17000 and contains certain parts of guideline IAF/ILAC A4 as well as the mandatory parts of ISO/PAS documents. The standard is used as the reference document during an assessment process of inspection bodies by accreditation bodies. Based on the assessment, inspection bodies prove the fulfillment of the standard requirements, respectively their competencies for performing inspection activities and gain market confidence. The paper describes all necessary steps which should be taken into account by an inspection body so that it can fulfill general and technical requirements of the standard and demonstrate the necessary competencies for performing inspection activities....
ABM Proceedings, 2018
A competitive landscape pressurizes the steel producers business, zero defect requirements from customers forces the operators for additional efforts in process control and quality management. The so called quality related cost, which include also rework, cost for downgrading or even scrapping of material is already a remarkable lever in a plants profitability breakdown. The introduction of advanced state of the art grades in the product portfolio requires already a budgeting for the expenses for R&D and quality management. PQA has been developed as a process and quality management assurance software solution next to existing level 2 or level 3 automation systems. It is focusing on the analysis of process data, equipment information, in line quality measurement devices and trend analysis to obtain an answer whether the process is according to definition and expectation and whether the intermediate or final product can be shipped for further processing as prime material. Advanced analytics which are linked to an expert know how based configuration identifies deficiencies in the production and processing process. An intelligent, state of the art quality rating system evaluates tolerable deviations. PQA comprises the software platform including the database, data collector from the different sources in the production process and units and the configurator. The core element of the platform is the knowledge based expert know how package defining process and quality defining fundamentals. The paper describes the structure of the software package, it gives insights on the expert know how package, process and quality evaluation and points out the customer benefits, cost reduction, improvement yield, customer satisfaction increase. An outlook is given on the adaptation of big data analytics and the utilization of AI artificial intelligence modules with the vision of a self-adaptation.
Quality Management and Metrology in Intelligent Manufacturing
IFAC Proceedings Volumes, 2001
Quality management and metrology play an important and basic role in individual activities of different function enterprises in Multi-functions Integrated Factory MFIF to ensure the realisation of such systems. MFIF is an innovative concept and a new model for future enterprises which has been developed to meet the demands for cost-effective customer-driven design and manufacturing, to realise agile and optimal manufacturing production. In this paper, the intelligent quality assurance system and metrology in MFIF is outlined. Especially the off-line programming technique for intelligent coordinate measurement technique ICMT as basis for simultaneous quality assurance is described and an intelligent measuring cell for quality assurance, data collection and data evaluation in MFIF is proposed and discussed.
Introduction to Intelligent Quality Management
Quality Control - Intelligent Manufacturing, Robust Design and Charts, 2021
Intelligent manufacturing is becoming more and more attractive for industrial societies especially after the introduction of industry 4.0 where most of industrial operations are to be carried by robots equipped with intelligent capabilities. This explicitly implies that the manufacturing systems will entirely be integrated and all manufacturing functions including quality control and management will have to be made as much intelligent as possible in operating with minimum human intervention. This Chapter will present a brief overview of some implications about intelligent quality systems. It intends to provide the readers of the book to understand how the concept of artificial intelligence is to be embedded into quality functions. It is known that the interoperability is the rapid transformation requirement of industry specific operations. This requires the integration of quality functions to other manufacturing functions for sharing the quality related knowledge with other manufact...
Bivariate quality control using two-stage intelligent monitoring scheme
2014
In manufacturing industries, it is well known that process variation is a major source of poor quality products. As such, monitoring and diagnosis of variation is essential towards continuous quality improvement. This becomes more challenging when involving two correlated variables (bivariate), whereby selection of statistical process control (SPC) scheme becomes more critical. Nevertl~eless, the existing traditional SPC schemes for bivariate quality control (BQC) were mainly designed for rapid detection of unnatural variation with limited capability in avoiding false alarm, that is, imbalanced monitoring performance. Another issue is the difficulty in identibing the source of unnatural variation, that is, lack of diagnosis, especially when dealing with small shifts. In this research, a scheme to address balanced monitoring and accurate diagnosis was investigated. Design consideration involved extensive simulation experiments to select input representation based on raw data and statistical features, artificial neural network recognizer design based on synergistic model, and monitoring-diagnosis approach based on twostage technique. The study focused on bivariate process for cross correlation function, p = 0.1-0.9 and mean shifts, p = k0.75-3.00 standard deviations. The proposed two-stage intelligent monitoring scheme (2s-IMS) gave superior performance, namely, average run length, ARLl= 3.18-16.75 (for out-of-control process), ARLO = 335.01-543.93 (for in-control process) and recognition accuracy. RA = 89.5-98.5%. This scheme was validated in manufacturing of audio video device component. This research has provided a new perspective in realizing balanced monitoring and accurate diagnosis in BQC.
Anais do Seminário de Automação & TI, 2017
Driven by customers, especially from the automotive sector, the supply of 100% prime quality has become essential. Furthermore the implication of quality costs and the yield of first class products define the steel companies' profitability and overall success significantly. The SMS group together with its subsidiary MET/Con has developed a comprehensive solution to assess the product quality over the production and processing chain. Process and production parameters originating from various data sources of the process automation are examined on their impact of quality related characteristics.Expert know how and comprehensive operational experience is translated into a quality guideline and a regulation framework as the sensitive core of the PQA solution.Once implemented, the system identifies immediately steel grade specific, deviations from ideal standard and proposes corrective actions including a final evaluation for each product at each processing step.The X-Pact® PQA (Product Quality Assessment) system is a substantial tool for root cause quality analysis and provides the required support to the production team. It will become an integral part of the corporate quality management system.
IRJET-Literature Review on Implementation of Total Quality Management
- In the present scenario of highly competitive business environment in domestic as well as global market, implementation of Total Quality Management (TQM) concept has become an important business style and a key survival tool, both for manufacturing and service industries, from large scale to small scale, for achieving the business goal and market value. TQM has been adopted by a good number of large scale industries for the achieve there goal and mission of the company. However, negligible units of Small and Medium Enterprises (SME) has adopted TQM. Especially in developing countries like India, though SMEs play an important role in the economic growth of the country and the demand of the growing population, SMEs are still resistant to adopt TQM. Many Indian SMEs, under the pressure from the customer, have set up suitable QMS (Quality Management System) for getting ISO 9001 certification leading to Quality Assurance, but have not adopted TQM due to the lack of the awareness about it. As such, understanding the causes behind their resistant in TQM implementation has become very important. In this review paper a number of literatures on the study on the Critical Factors for successful implementation of TQM and the causes responsible for resistant of the SME in adopting TQM. The result will motivate and help them in future research to remove or minimized the barriers of SMEs in implementing TQM to achieve the business excellence.
Practical Concepts of Quality Control
2012
Also he has been a visiting researcher in Karlsruhe University, Germany. He has published more than 37 research papers in national and international journals and is author of two books. Also, he has been awarded a Silver medal in 16th National Mathematics Olympiad in Iran and he has bens ranked 1st in the graduate national university comprehensive exam in System Management in Iran. He has been ranked 47th among all high school graduates in Iran. His areas of interest include: reliability, quality control, quality engineering and operations research Contents Preface XI
Application of 7 Quality Control (7 QC) Tools for Continuous Improvement of Manufacturing Processes
— In this paper a review of systematic use of 7 QC tools is presented. The main aim of this paper is to provide an easy introduction of 7 QC tools and to improve the quality level of manufacturing processes by applying it.QC tools are the means for Collecting data , analyzing data , identifying root causes and measuring the results. these tools are related to numerical data processing .All of these tools together can provide great process tracking and analysis that can be very helpful for quality improvements. These tools make quality improvements easier to see, implement and track.