DIE SURFACE TEMPERATURE CASE STUDY

152 APPLYING STATISTICAL PROCESS CONTROL Figure 10.5 Thirty discrete process data curves for die surface temperature over one cycle equal number of elements for each cycle. temperature is reached with the bulk mass of the die. Typically, lubricants are sprayed onto the surface of the die between metal injections. Although this may cool the surface of the die quickly, once the spray stops, the surface temperature will rise back to the temperature of the bulk die mass. All the events described above can be associated with features illustrated in the process data curve. Often die surface temperature, if measured at all, is controlled by monitoring the maximum die temperature for each cycle. Al- though this information may prove useful, it offers little under- standing of the process in comparison to the entire process data curve. Through the examination of die temperature as a function of time, the understanding of the process is increased. Data for die surface temperature can be obtained by instru- menting a die with a thermocouple. During processing, analog data from the thermocouple can be recorded using a computerized data acquisition system. Such systems store the data in a digital format. Storing the data in this manner in reality creates a discrete model of the original continuous signal from the thermocouple. The time of each cycle will not be identical. To apply the finite element method, each process curve should be described with an equal number of elements. Thirty process data curves for die sur- face temperature as shown in Figure 10.5. After collecting the data, the average cycle time was calculated to be 20 s. To help correlate the elements to time, each process data curve has been

10.4 DIE SURFACE TEMPERATURE CASE STUDY

153 TABLE 10.1 Calculation of Upper and Lower Control Limits for Element 0.0 1 1000 16 1022 2 1011 17 1013 3 1023 18 1025 4 1030 19 1012 5 1014 20 1008 6 999 21 1024 7 983 22 997 8 976 23 973 9 972 24 982 10 970 25 994 11 982 26 1006 12 989 27 1001 13 1003 28 992 14 1018 29 987 15 1024 30 982 Note: average, 1000.40; standard deviation, 18.00; upper control limit, 1036.41; lower control limit, 964.39. Metal enters the die. broken into 200 discrete elements with each element equivalent to 0.1 on the x axis. Table 10.1 is composed of the discrete data from each of the 30 process data curves at element 0.0, when molten metal enters the die. The footnote to the table contains the average of these elements, the standard deviation, and the upper and lower control limits defined as two standard deviations above and below the average. Such calculations are to be performed for each set of 30 equivalent elements. Figure 10.6 is a plot of the process average curve, upper control limit curve, and lower control limit curve for each element. Note that the common-cause variation described by the control limit curves is not constant. The most common-cause variation can be found during die spray, which is enlarged in Figure 10.7. This shows that more variation in die surface temperature occurs during the die spray than during any other portion of the cycle. The process data curves should fall within the upper and lower control limit curves. Figure 10.8 is a plot of the process control limit curves and a process data curve exhibiting special-cause var- iation. The die surface temperature for the data curve plotted falls below the lower control limit curve after solidification. Statisti- 154 APPLYING STATISTICAL PROCESS CONTROL 400 500 600 700 800 900 1000 1100 -1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 Elements Temperature Process Average Upper Control Limit Lower Control Limit Wider Control Limits Figure 10.6 Process average curve, upper control limit curve, and lower con- trol limit curve for die surface temperature over one cycle. 450 460 470 480 490 500 510 520 14.0 15.0 16.0 17.0 18.0 Elements Temperature Process Average Upper Control Limit Lower Control Limit Figure 10.7 Process average curve, upper control limit curve, and lower con- trol limit curve for die surface temperature during die lubricant spray. cally, the cycle is ‘‘out of control’’ and the special-cause, which induced the variation, should be corrected.

10.5 APPLYING SPC TO HIGH INTEGRITY DIE

CASTING PROCESSES The figures presented in this chapter illustrate the benefits of mon- itoring dynamic process data curves and applying statistical meth- ods for controlling high integrity die casting processes. Although this discussion has been limited to one example, several other REFERENCES 155 400 500 600 700 800 900 1000 -1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 Time Element Temperature Process Curve Upper Control Limit Lower Control Limit Process Curve Fall Below the Lower Control Limit Figure 10.8 Process data curve exhibiting an ‘‘out-of-control’’ condition cycle exhibits special-cause variation. dynamic characteristics related to high integrity die casting pro- cesses can be analyzed using this technique: 1. plunger velocity, 2. shot sleeve temperature, 3. die cavity pressure, 4. hydraulic pressures, and 5. billet heating for semi-solid metalworking. These process data curves can be tracked and examined statisti- cally to focus improvement efforts. REFERENCES 1. Vinarcik, E., ‘‘Walter Shewhart,’’ Technology Century, October, 1999, p. 46. 2. Shewhart, W., Economic Control of Quality of Manufactured Products, D. Van Norstrand, New York, NY, 1931. 3. Robbins, T., ‘‘Signature-Based Process Control SPC Trending Evaluate Press Performance,’’ MetalForming, January 1995, pp. 44–50. 4. Vinarcik, E., ‘‘Finite Element Analysis of Process Data Curves for Statistical Process Control,’’ SAE Paper Number 970081, Society of Automotive En- gineers, Warrendale, PA, 1997. 5. Ishikawa, K., Guide to Quality Control, Kraus International Publications, White Plains, NY, 1986.