Tooling Product News

PART II: A Systematic Approach to Diagnosing Mold Filling and Part Quality Variations

By: David A. Hoffman, Director of Technical Sales
Beaumont Technologies, Inc.

Last month we discussed a five-step methodology of assigning Flow Groups to a mold in order to help separate out the root causes of mold filling and part quality variations. Based on the simplified pressure drop equation (Figure 1), it was determined that the root causes could be grouped into two main categories: (1) steel variations and (2) viscosity variations (shear imbalances).

From this, we could then assign a root cause to each variation in filling or part quality based on whether the variation was within a Flow Group or between Flow Groups:

REMINDER:

Steel Imbalance: variation WITHIN a Flow Group
Shear Imbalance: variation BETWEEN the Flow Groups

This month we are going to walk through a case study on a 16-cavity cold runner mold used to produce some type of electrical connector. The company was having problems with filling imbalances, flash, short shots, and dimensional variations (sound familiar to anyone?). The company sent in the mold balance data using the typical format of calculating a percent imbalance by comparing the heaviest cavity to the lightest cavity. Using traditional imbalance calculations, the maximum imbalance was determined to be whopping 50.8% between the cavities (Figure 2).

Ok, great…now what? What would you suggest to fix the imbalances? How do you know, by calculating only one number, what the root causes of the variation are? A pretty difficult task for sure. This simplified method certainly gives you a percent imbalance, but it gives you very little information to determine the root causes of the variations.

Now let’s take this mold and look at it through a different set of eyes. We are going to tear the data apart, re-organize it, and calculate a great deal of more useful information.

First we need to assign a quadrant letter designation (A, B, C, D) and then determine the Flow Groups and which cavities belong to each Flow Group. This 16-cavity runner layout actually produces four sets of Flow Groups (Figure 3). The next step is to take the data collected by the short shot analysis and re-organize and graph it according to Flow Group designations. This allows us to calculate steel imbalances within each Flow Group and shear imbalances between the Flow Groups.

This advanced mold balance analysis gives the user more useful information to help determine the root causes of the mold filling and part quality variations. The shear imbalance was calculated to be 31.6% maximum, and the steel variations ranged from 19-37% within the Flow Groups (Figure 3).

And now that the two main sources are separated and quantified, you can determine the solutions for each root cause. Melt rotation technologies could easily be utilized in this mold to solve the shear imbalances. However, this technology would not fix the steel imbalances in the mold. Therefore you would need to determine the source of steel imbalances and address them independently. You can begin to diagnose the steel conditions by comparing the heavy cavity to the light cavity within each Flow Group (not across the entire mold) and look for differences in gate size, gate land geometry, venting, wall thickness variations, runner sizes, etc…

HINT: One way to help determine the root cause of steel imbalances is to look for trends. If you see a trend in the data, this is telling you that there is a common root cause to most of the variation.

After studying the data from this mold, it was noticed that the cavities in quadrants A & D were always heavier than the cavities in quadrants B & C. By looking at the Flow Group schematic of the runner, you will see that the cavities in quadrants A & D are all on the left side of the sprue. This tells you that something is different from one side of the mold versus the other side.

Now you can begin to measure, verify, and eliminate each possible steel imbalance source that would cause one half of the mold to fill before the other half until the true root cause of the variation was found. Through a process of elimination, the main source of steel variation in this mold was ultimately found to be within the diameter of the primary runners. A variation of 0.005” – 0.007” (0.127 – 0.178 mm) was measured between the right primary runner versus the left primary runner. The larger primary runner was on the left side of the sprue. And according to our pressure drop equation (refer back to Figure 1), a larger radius means a lower pressure drop which means an easier flow path for the plastic.

This analysis correlates exactly with the data seen from the advanced mold balance analysis. The entire process described within this case study took less than 30 minutes to complete, including the identification of the primary runner variation. Using the traditional mold balance analysis, we simply would not have had enough information to provide the insight to easily identify the root cause of the steel imbalances within this mold.

QUIZ: Now it is time to see if you understand the concepts we discussed over the past two months. From Figure 4 below, see if you can answer the following questions:

How many Flow Groups are in this mold?
Which cavities belong to each Flow Group?
Which cavities would you compare when evaluating steel imbalances in this mold?
Which cavities would you compare when evaluating the shear imbalance in this mold?

BONUS QUESTION: What is wrong with this short shot sample?

To find out the answers, please contact Beaumont Technologies, Inc. at info@beaumontinc.com (Subject Line: Tooling & Product News) or call 814-899-6390 ext. 130.

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