Control chart application for post heat treat processes

I have struggled with trying to find a method/solution/tool...or something to analyze a process in which dimensional product characteristics change after the parts are heat treated. Our company performs induction heat treat internally where only 2 parts are heat treated at a time, and we also send batches of parts out to subcontractors for carbonitride where thousands of parts might be furnace heat treated simultaneously. For some critical characteristics we have Xbar-R charts in use at the machining operation, and the process demonstrates a statistically controlled process. The problem arises when variations in material chemistry, quench rate, etc. cause these dimensions to both "grow" and "shrink" in sometimes significant amounts.
For our internal heat treating, we perform many different studies to try to control the process variables so that we can try to predict where to center the machining process in order to meet the specifications after heat treat. Then periodically we take samples on these dimensions as the heat treating is performed, and chart them using another Xbar-R chart to determine our capability indexes and whether the process is still in statistical control. We have seen some success and many frustrations with this method.
The problem is further complicated when we must try to position the machining process for large batches of parts which go to subcontractors for heat treat processing. One of the major obstacles is trying to develop a way of determining statistical control for batches of parts in which we are not running. We must rely on incoming sampling from these lots to try to determine compliance with the specifications, but determining statistical control and capability indexes from these samples doesn't quite make sense to me. Mainly because the sample we take is from a very large lot, and doesn't seem to be much different than if our customer were to sample our shipments, and try to determine if our process was in control or not.
My primary question I guess is: Are there better tools available for this type of analysis or maybe a better application of the control chart to determine statistical control. Any insight from anyone who has had similar experiences would be greatly appeciated.

[Don Winton]

The problem and frustrations you are experiencing are to be expected. You are monitoring the outputs to try to control the inputs. This will not work. The answers you seek may be beyond the capability of the control chart. You may require advanced statistical techniques.

The problem arises when variations in material chemistry, quench rate, etc. cause these dimensions to both "grow" and "shrink" in sometimes significant amounts.

It appears that you have several variables, but concentrate on three for now. One, article temperature. Two, quench temperature. Three, quench time. I believe that you will find that, if these three are looked at first, variation in material may not be significant. Perhaps not.

What you need is someone who can set up a designed experiment that considers these three variables first (after that, look at the material). Once you have determined how these variables affect your dimensions, then process control techniques can be utilized to control the inputs.

Alternatively, put controls on the three variables I mentioned above. Control article temperature to 'X' degrees, +/- range. Control quench temperature to 'X' degrees +/- range. Same for quench time. Place controls on these and use control charts for these inputs.

Drawing on my limited past experience, these controls may go something like this:

Place article in oven at 'X' degrees for one hour +/- 10 minutes. Remove articles and place in {Type of here. Oil, water, etc.} quench of 'X' degrees +/- 5 degrees. Leave articles in quench for 5 minutes +/- 1 minute.

I believe you will find that when you place controls on the inputs, the outputs will become more predictable. I would still advise the advanced statistical technique if you want to know the ideal inputs. I can help, if you like, but would need more details.

Hope this helps.

Regards,
Don

<CENTER><FONT COLOR="#0000FF"><FONT SIZE=2>Visit dWizard's Lair:<A HREF="http://www.ficom.net/members/donwinton/home.html"><IMG SRC="http://www.ficom.net/members/donwinton/druid.gif" HSPACE=6 BORDER=0 HEIGHT=85 WIDTH=55 ALIGN="CENTER"></A></FONT></FONT></CENTER>

[indianbro]

Originally Posted by Mark W

I have struggled with trying to find a method/solution/tool...or something to analyze a process in which dimensional product characteristics change after the parts are heat treated. Our company performs induction heat treat internally where only 2 parts are heat treated at a time, and we also send batches of parts out to subcontractors for carbonitride where thousands of parts might be furnace heat treated simultaneously. For some critical characteristics we have Xbar-R charts in use at the machining operation, and the process demonstrates a statistically controlled process. The problem arises when variations in material chemistry, quench rate, etc. cause these dimensions to both "grow" and "shrink" in sometimes significant amounts.
For our internal heat treating, we perform many different studies to try to control the process variables so that we can try to predict where to center the machining process in order to meet the specifications after heat treat. Then periodically we take samples on these dimensions as the heat treating is performed, and chart them using another Xbar-R chart to determine our capability indexes and whether the process is still in statistical control. We have seen some success and many frustrations with this method.
The problem is further complicated when we must try to position the machining process for large batches of parts which go to subcontractors for heat treat processing. One of the major obstacles is trying to develop a way of determining statistical control for batches of parts in which we are not running. We must rely on incoming sampling from these lots to try to determine compliance with the specifications, but determining statistical control and capability indexes from these samples doesn't quite make sense to me. Mainly because the sample we take is from a very large lot, and doesn't seem to be much different than if our customer were to sample our shipments, and try to determine if our process was in control or not.
My primary question I guess is: Are there better tools available for this type of analysis or maybe a better application of the control chart to determine statistical control. Any insight from anyone who has had similar experiences would be greatly appeciated.

[FONT=Arial][FONT=Arial][SIZE=7][SIZE=7][FONT=Book Antiqua]Mark,
The first thing that caught my attention was "grow" and "shrink" which should not be. A properly quenched and tempered steel should always grow. This has to do with the microstructure that develops or should develop. I once did an investigation for SKF, Altoona, PA, where there bearing races were not "cleaning up" in grind - they were shrinking. I knew immediately that the heat treat was wrong. I found they were unknowingly overheating in one zone of the furnace and causing an undesiriable microstructure, retained austenite, which caused the shrinkage. These parts should have grown 0.2 to 0.3 percent.
The second thing I'd like to comment on is controlling the size change. The traditional SPC approach assumes the operator can correct an "out-of-control" condition. In other words some characteristic you are producing at that machine. It is not the intent of heat treatment to produce or control a dimension. Although it may be possible to relate cause and effect of heat treat variables to size change, any size change in incidental to the heat treatment and NOT a purposely produced charectistic. I might suggest that some distortion may be occuring. Distortion is not the same as size change but may cause a certain dimension to grow or shrink. Distortion is a completely different animal - much tougher to work with.

Maybe these comments will shed some light. Hang in there.

[Govind]

Originally Posted by Mark W

The problem is further complicated when we must try to position the machining process for large batches of parts which go to subcontractors for heat treat processing. One of the major obstacles is trying to develop a way of determining statistical control for batches of parts in which we are not running. We must rely on incoming sampling from these lots to try to determine compliance with the specifications, but determining statistical control and capability indexes from these samples doesn't quite make sense to me.

Mark, You are right about the calculation of indices doesn't make sense-from a different perspective.

I just want to add that trying to determine Cp,Cpk capability indices from incoming samples may be misleading as you may not know if the supplier's process was in a Statistical Process control ( Stable). If they handed over their SPC charts demonstrating stability, then you can validate those data and use appropriately.Even if they did not plot the SPC charts, getting their data "time Sequence" will help you make meaningful decisions.

Originally Posted by Mark W

Mainly because the sample we take is from a very large lot, and doesn't seem to be much different than if our customer were to sample our shipments, and try to determine if our process was in control or not.

This is to do with the homogeneity of the lot. Try identifying the variables that could potentially impact homogeneity, stratify lot by Variable, measure and obtain the mean and variance. Perform Test of Hypothesis like t-test, F-Test to see if the mean,variations are significant. You need to finally arrive at a single most dominant variable that influence homogeneity and classify the lots based on this variable and not necessarily by Date of Manufacture or Date of Shipping,etc.

But as mentioned by you, by comparing the samples within a large lot does not say if the process was in control or not. You can say if the samples taken from the same lot are statistically different at a given level of confidence.

Govind.

[Tom W]

As a heat treater I would suggest that you go back to your heat treater and ask them to provide you with data that supports the variables in the process. Time / Temperature / Hardness / Microstructure / Uniformity studies / probe checks / leak-ups / etc. that you need to ensure consistent processing. JMHO (Any quality heat treater will be more than happy to supply this to you - if not give me a call.)