Prediction of die casting process parameters by using an artificial neural network model for zinc alloys
Yarlagadda, Prasad K. (2000) Prediction of die casting process parameters by using an artificial neural network model for zinc alloys. International Journal of Production Research, 38(1), pp. 119-139.
Pressure die casting is an important production process. In pressure die casting, the ® rst setting of process parameters is established through guess work. Experts use their previous experience and knowledge to develop a solution for a new application. Due to rapid expansion in the die casting process to produce better quality products in a short period of time, there is ever increasing demand to replace the time-consuming and expert-reliant traditional trial and error methods of establishing process parameters. A neural network system is developed to generate the process parameters for the pressure die casting process. The system aims to replace the existing high-cost, time-consuming and expertdependent trial and error approach for determining the process parameters. The scope of this work includes analysing a physical model of the pressure die casting ® lling stage based on governing equations of die cavity ® lling and the collection of feasible casting data for the training of the network. The training data were generated by using ZN-DA3 material on a hot chamber die casting machine with a plunger diameter of 60 mm. The present network was developed using the MATLAB application toolbox. In this work, the neural network was developed by comparing three di€ erent training algorithms: i.e. error backpropagation algorithm; momentum and adaptive learning algorithm; and Levenberg± Marquardt approximation algorithm. It was found that the Levenberg± Marquardt approximation algorithm was the preferred method for this application as it reduced the sum-squared error to a small value. The accuracy of the developed network was tested by comparing the data generated fromthe network with those of an expert froma local die casting industry. It was established that by using this network the selection of process parameters becomes much easier, so that it can be used by a novice user without prior knowledge of the die casting process or optimization techniques. 1. Introduction Pressure die casting is an important production process that is extensively used to produce castings for the electrical, electronic and automobile industries. The process has its origins in type casting machines developed in 1822. The process showed its production potential as early as the mid 1800s when it had reached a high level of automation and mechanical efficiency. In 1894, the ® rst die casting machine was developed, in which molten metal was forced through an inclined port and out of the nozzle into the die by the central ram actuated by a lever. During the past two decades, the pressure die casting process has become an essential casting production process for the engineering industry. High production rate, excellent surface ® nish and good mechanical properties of the ® n- Revision received March 1999.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Journal Article|
|Additional Information:||For more information please refer to the publisher's website (link above) or contact the author: firstname.lastname@example.org|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MANUFACTURING ENGINEERING (091000) > Manufacturing Processes and Technologies (excl. Textiles) (091006)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
|Copyright Owner:||Copyright 2000 Taylor & Francis|
|Copyright Statement:||First published in International Journal of Production Research 38(1):pp. 119-139.|
|Deposited On:||09 Mar 2007|
|Last Modified:||15 Jan 2009 07:22|
Repository Staff Only: item control page