ISSN 2594-5297
55º Seminário de Laminação e Conformação — vol. 55, num.55 (2018)
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Resumo
"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."
"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."
Palavras-chave
Process, Product, Performance, Optimization, Quality management, Expert know how, Automation, digitization, Quality assessment, big data analytics, AI artificial intelligence, AHSS multiphase steel grades
Process, Product, Performance, Optimization, Quality management, Expert know how, Automation, digitization, Quality assessment, big data analytics, AI artificial intelligence, AHSS multiphase steel grades
Como citar
Kempken, Jens.
REACHING THE NEXT PERFORMANCE LEVEL IN SBQ PRODUCTION – PQA – SOFTWARE BASED QUALITY AND PROCESS MANAGEMENT
,
p. 244-253.
In: 55º Seminário de Laminação e Conformação,
São Paulo,
2018.
ISSN: 2594-5297
, DOI 10.5151/1983-4764-31520
