Manufacturing lead time prediction for extrusion tools with the use of Neural Networks

• Autorzy: Sajko Nika , Kovacic Simon , Ficko Mirko , Palcic Iztok , Klancnik Simon

Identyfikatory

DOI

10.24425/mper.2020.134931

• Języki publikacji: EN

Abstrakty

EN

Due to fast-paced technical development, companies are forced to modernise and update their equipment, as well as production planning methods. In the ordering process, the customer is interested not only in product specifications, but also in the manufacturing lead time by which the product will be completed. Therefore, companies strive towards setting an appealing but attainable manufacturing lead date. Manufacturing lead time depends on many different factors; therefore, it is difficult to predict. Estimation of manufacturing lead time is usually based on previous experience. In the following research, manufacturing lead time for tools for aluminium extrusion was estimated with Artificial Intelligence, more precisely, with Neural Networks. The research is based on the following input data; number of cavities, tool type, tool category, order type, number of orders in the last 3 days and tool diameter; while the only output data are the number of working days that are needed to manufacture the tool. An Artificial Neural Network (feed-forward neural network) was noted as a sufficiently accurate method and, therefore, appropriate for implementation in the company

Słowa kluczowe

EN

manufacturing lead time; Neural Network; artificial intelligence; extrusion

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2020
• Tom: Vol. 11, No. 3
• Strony: 48--55
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Sajko Nika

• Kaldera d.o.o., Kolodvorska ulica 33a, 2310 Slovenska Bistrica, Slovenia

autor

Kovacic Simon

• Kaldera d.o.o., Slovenia

autor

Ficko Mirko

• University of Maribor, Faculty of Mechanical Engineering, Slovenia

autor

Palcic Iztok

• University of Maribor, Faculty of Mechanical Engineering, Slovenia

autor

Klancnik Simon

• University of Maribor, Faculty of Mechanical Engineering, Slovenia

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