Temat: data control - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Specificity of SPC procedures application in foundry in aspect of data acquisition and data exploration
Autorzy:: Ignaszak, Z.
Sika, R.
Powiązania:: https://bibliotekanauki.pl/articles/380308.pdf
Data publikacji:: 2012
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: technologia informatyczna
przemysł odlewniczy
statystyczne sterowanie procesem
karta kontrolna Shewharta
akwizycja danych
pozyskiwanie danych
information technology
foundry industry
statistical process control
shewhart control charts
data acquisition
data mining
Opis:: The paper presents an analysis of SPC (Statistical Process Control) procedures usability in foundry engineering. The authors pay particular attention to the processes complexity and necessity of correct preparation of data acquisition procedures. Integration of SPC systems with existing IT solutions in area of aiding and assistance during the manufacturing process is important. For each particular foundry, methodology of selective SPC application needs to prepare for supervision and control of stability of manufacturing conditions, regarding specificity of data in particular 'branches' of foundry production (Sands, Pouring, Metallurgy, Quality).
Źródło:: Archives of Foundry Engineering; 2012, 12, 4; 65-70
1897-3310
2299-2944
Pojawia się w:: Archives of Foundry Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Introducing Advanced Data Analytics in Perspective of Industry 4.0 in a Die Casting Foundry
Autorzy:: Perzyk, M.
Dybowski, B.
Kozłowski, J.
Powiązania:: https://bibliotekanauki.pl/articles/381424.pdf
Data publikacji:: 2019
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: application of information technology
foundry industry
mechanical properties
die casting
process control
data analytics
zastosowanie technologii informatycznych
przemysł odlewniczy
właściwości mechaniczne
odlewanie
kontrola procesu
analityka danych
Opis:: The paper presents some aspects of a development project related to Industry 4.0 that was executed at Nemak, a leading manufacturer of the aluminium castings for the automotive industry, in its high pressure die casting foundry in Poland. The developed data analytics system aims at predicting the casting quality basing on the production data. The objective is to use these data for optimizing process parameters to raise the products’ quality as well as to improve the productivity. Characterization of the production data including the recorded process parameters and the role of mechanical properties of the castings as the process outputs is presented. The system incorporates advanced data analytics and computation tools based on the analysis of variance (ANOVA) and applying an MS Excel platform. It enables the foundry engineers and operators finding the most efficient process variables to ensure high mechanical properties of the aluminium engine block castings. The main features of the system are explained and illustrated by appropriate graphs. Chances and threats connected with applications of the data-driven modelling in die casting are discussed.
Źródło:: Archives of Foundry Engineering; 2019, 1; 53-57
1897-3310
2299-2944
Pojawia się w:: Archives of Foundry Engineering
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Methodology for Diagnosing the Causes of Die-Casting Defects, Based on Advanced Big Data Modelling
Autorzy:: Okuniewska, A.
Perzyk, M. A.
Kozłowski, J.
Powiązania:: https://bibliotekanauki.pl/articles/2126910.pdf
Data publikacji:: 2021
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: foundry industry
die casting
castings defects
process control
data analytics
application of information technology
przemysł odlewniczy
odlewanie ciśnieniowe
wady odlewów
kontrola procesu
analiza danych
zastosowanie technologii informatycznych
Opis:: The purpose of this paper was to develop a methodology for diagnosing the causes of die-casting defects based on advanced modelling, to correctly diagnose and identify process parameters that have a significant impact on product defect generation, optimize the process parameters and rise the products’ quality, thereby improving the manufacturing process efficiency. The industrial data used for modelling came from foundry being a leading manufacturer of the high-pressure die-casting production process of aluminum cylinder blocks for the world's leading automotive brands. The paper presents some aspects related to data analytics in the era of Industry 4.0. and Smart Factory concepts. The methodology includes computation tools for advanced data analysis and modelling, such as ANOVA (analysis of variance), ANN (artificial neural networks) both applied on the Statistica platform, then gradient and evolutionary optimization methods applied in MS Excel program’s Solver add-in. The main features of the presented methodology are explained and presented in tables and illustrated with appropriate graphs. All opportunities and risks of implementing data-driven modelling systems in high-pressure die-casting processes have been considered.
Źródło:: Archives of Foundry Engineering; 2021, 21, 4; 103--109
1897-3310
2299-2944
Pojawia się w:: Archives of Foundry Engineering
Dostawca treści:: Biblioteka Nauki

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