The Optimum Design For The Casting System And Process Condition Parameter Of Aluminium Alloy Die Casting

It is regarded that the defects such as poor filling, blisters, sunken, hot cracking etc, which are caused by the high speed, high pressure and short filling time of die casting process, have great relation with the design of the die-casting die and the selection of process parameters. Traditionally, the optimum design of die and process parameters were obtained using experience law or trial and error method, which have been viewed as low efficiency and the result can not be guaranteed.Firstly, the design of a large plane of aluminum alloy product (used as heating component on a hamburger machine) was proposed and analyzed using CAE software, Folw-3D flow analysis package. The result shows that a pair of runner type fan-shaped pour is superior to the single runner fan-shaped pour. Then, TAGUCHI experiment design method was adopted to design the test, and the orthogonal table and Signal-To-Noise ratio (SN) were used to obtain the optimum process parameters. by the important cause analysis, in which pressurization, high-speed injection speed, the injection speed of low-speed, the distance at a high speed, injection ACC pressure, fusion temperature, cooling temperature of die, spraying dosage etc, were selected as control parameters, Establishing two levels of item 1, the others adopt three levels .L18 (21*37) orthogonal table was used on the experiment design.From the result of TAGUCHI experiment, we found that the combination of optimum parameter of process condition are, adopt pressurization, high-speed injection speed 3.2m/s, low-speed injection speed 0.2m/s, the distance at a high speed-170mm, injection ACC pressure 12.5mpa, fusion temperature-670℃,cooling temperature of die-230℃,spraying dosage 180ml. Through the substantiating experiment, the SN ratio of new process and quality characteristic of its product have got obvious improved. produce in accordance with the new design parameter , The new statistics shows that the scrapping ratio has dropped from 15.1% to 2.59%, the loss of money has dropped from $134,000 per month to$27,000 per month, reducing $117,000 bad losses per month.