Paint Thickness Reduction - Dashboard - Confluence

Paint Thickness Reduction

Prepared by:

CT

Paint Thickness Reduction Project CT 2

DEFINE

Problem Statement

Our existing powder paint system utilized to paint the outside of the tanks on our pole-mounted transformers is found to be in such condition that opportunities to reduce output and its variation are evident.

Our quality specifications followed from ANSI standards, request a mean paint thickness (measured in mils or µm) also referred to as millage of 4, with a lower specification limit (LSL) of 3 mils and an upper specification limit (USL) of 10 mils. The current mean found in our system based on a study of 100 samples is of 5.8 mils.

Business Case

I propose to conduct a Black Belt Six Sigma project in which our current mean paint thickness (millage) would be reduced from 5.8 to a number closer to our target of 4. Based on our production from Jan 09 to Aug 09, we produced a total of 59,539 tanks (Table 1) and consumed a total of 70,800 pounds of powder paint. This tells us that we have used on average 1.2 lbs of powder paint per tank.

Table 1. Tank annual production

A reduction of millage from 5.8 to 4 equates to approximately 30% less paint needed. [((5.8 -4) / 5.8) x 100 = 30%] When implementing the planned savings of 30%, the annual cost of powder paint would be reduced to $142,975 ($204,250 x 70%) for an estimated annual savings of $60,000.


Project Scope

The scope of this project will include the process before and after the powder paint application (Figure 1). This will help us visualize any potential effects caused by these external operations.

Figure 1. Process Layout

This project will begin at the point when tanks exit the Dry-Off oven where they travel on an overhead conveying system hanging from the bottom (upside down) for about 1385 seconds inside the oven kept at 250°F. Once they exit the Dry-Off oven they travel a distance of about 70 feet in ambient temperature for about 600 seconds. This causes a temperature reduction on the tanks of about 150 degrees. These then enter the paint room where the powder paint is applied with the use of 17 spray guns. All these guns are similar in configuration; however, they are strategically located to maximize their output (see Figure 2).

Figure 2. Powder Spray gun locations in relation to tank

(Note: #15 gun was relocated across the booth and labeled as #9)

The amount of time the tanks spend in the Paint Room is about 270 seconds of which 214 of them are actually in powder paint application. They then exit the paint room and after traveling for 300 seconds more at ambient temperature, they enter the Cure Oven where temperature is kept at 350°F and travel for about 2100 seconds. This is the point where the powder paint cures and adheres as a form of protective coat to the metal tank. They are later removed (un-hanged) from the conveyor line at the Un-hang Station. The operator at this last station measures the paint thickness (millage) and records it on a standard log form which will be used to gather all information for this project. This form is found in Appendix A (A-2). This same appendix will show the SIPOC (A-1) form used for this project where the Stakeholders and Metrics are identified.


MEASURE

In order to rely on the numerical measurement output given by the digital gage used to measure the tank paint thickness (millage) at the Un-hang station, a Gage Repeatability and Reproducibility study on Minitab was performed on this gage. For this study, the method used to obtain the readings included 2 operators (one from each shift), 5 random samples and 3 repetitions. The operators were each asked to perform the checks in a random order to ensure bias would not be a factor. From these checks the following results were found and are shown on Figure 3.

Figure 3. Gage R & R results

Based on the data obtained, we can observe that the majority of the Total Variation is found in the Part-to-Part aspect. This is understandable since the parts were randomly selected and there is variation in the process. For the purpose of the Gage acceptance, Repeatability is of concern, since we are testing how reliable the gage is. Based on the value of variability, less than 10 percent being acceptable by industry standards, we can statistically demonstrate that the paint thickness gage used in this study is repeatable with about 9.34% study variation.


Once we have certified that the measurement results given by this gage are acceptable, the Un-hang station’s operator uses a standard log form (A-2) to record the values found. He randomly selects 5 tanks and checks 2 different locations, Tank Thickness and Tank Bottom (Figure 4), for millage values.

After randomly obtaining 100 samples taken from the standard log form (A-2) that would account for a significant sample size the normality of the data was checked to determine the statistical tools to be implemented to measure a more factual improvement. Figures 5 and 6 show the normality test performed.

Figure 5. Tank Paint Thickness Normality test


Figure 6. Tank Bottom Paint Thickness Normality test

The results of the normality test demonstrate a P-value of <0.5. Therefore, I can conclude that the data is not normal and that it would be more appropriate to track Median improvements as opposed to Mean improvements. The following descriptive statistics demonstrate a tank thickness median if 5.4 and tank bottom median of 5.6 mils.


To track current performance I used the I-MR chart, where it can be observed that the system is found to be unstable. See Figures 7 and 8 below.

Figures 7 & 8 above demonstrate that several points in the data recorded are found to be 3 or more standard deviations away from the mean (noted by the RED dots). The variability on the system is what needs to be controlled. For a more detailed description, the process capability using a Weibull distribution due to the non-normality of the data is used.


Observe Figures 9 and 10 below.

Figure 9. Tank Paint Thickness Process Capability

Figure 10. Tank Bottom Paint Thickness Process Capability


ANALYZE There are several inputs in this paint process that could potentially affect its performance. I used a cause-and-effect diagram (Ishikawa Fishbone) to organize these factors (Figure 11). Analyzing the effects and magnitude of impact that each factor has on Paint Thickness (Millage) will help determine which factors will provide the most beneficial outcome in paint usage.

With the help of expert paint operators and maintenance technicians, a Matrix form (Figure 12) was developed in order to rate the factors and their potential impact on Paint Thickness (Millage). Based on these ratings, a priority order is set and used to determine the vital few from the trivial many. More focus is applied to the top factors.


Figure 12. Cause and Effect Matrix of Paint Thickness (Millage)

Powder Paint System

The paint system consists of 17 identical powder paint guns (refer to Figure 2 for locations and arrangement). Each paint gun has two measureable inputs that are manually set and controlled by the painter in charge. These inputs are Air and Powder pressure that can vary from 0 to 80 PSI. A careful 5-day study on the system’s settings and performance led to the following observations.

Five trials were used to observe the impact of key paint guns (determined by arrangement location in relation to the tank). These key guns are situated at prime locations based on how the tanks hang from our overhead conveying system and our targeted area of focus. Table 2 below describes the settings optimization used for each trial. The initial settings found on the second and third column of the table, represent the standard settings to which the guns were found on our current state. Our “before” data was gathered using these settings on the paint guns as they represented the common practice then. These are the settings found in our current quality control plan and that they were once designated to be the optimal values required to achieve our target of 4 mils. However, as previously shown, the median paint thickness values then were 5.4 mils for Tank and 5.6 for Bottom.


Table 2. Paint gun settings optimization

During each trial, the response paint thickness values were recorded by measuring 10 random points at the Tank and Bottom painted areas using the paint thickness gage certified earlier. A box-plot for each of the trials is shown below (Figures 13 and 14) and from these we can appreciate the variation. It is assumed that all other factors are left constant and that the only changes made are on the selected paint gun settings.

In trial #1 (dated 9-1-09) from the above figures, we can see how the settings have dropped our mean target to a number closer to 3, even though this is closer to our target of 4 mils, there is just too much significant variation present. Variation like this is what I propose to reduce with this project. In order to minimize cost at experimenting with different settings I opted to perform a Design of Experiment with the samples that I had.


Design of Experiments

The data gathered from the 5 different trials (each trial optimizing a particular set of paint guns), would serve as the main ingredient for a Design of Experiments in order to optimize our response values.Figure 15 shows an example of the Response Optimization performed to arrive at the optimal values. This same tool was used for the rest of the paint guns selected.

Figure 15. Response Optimization for paint gun settings

The results obtained that would take us the closest to our target are shown on the last column of the same Table 2 (shaded purple).


Considering the next 3 factors (moisture, powder texture and nozzles), outside vendors were invited to come and audit to determine best practices. No changes were made to any of these. It was simply reinforced to maintain our current condition via preventive maintenance.

The last factor that is going to be covered under this project is hook grounding. In order for powder paint to stick to our tank metal, an electrical charge must be present. This effect would mainly concentrate on the variation of paint thickness throughout different areas of the tank caused by poor grounding.

To study this effect, 3 different hooks were utilized. The goal was to determine if too many coats of paint on the hooks (from being used multiple times before removing any paint) had any impact on the Paint thickness variance of the tanks.

Hook #1 was one with 0 mils of paint (paint recently removed) Hook #2 had 21.7 mils of paint (approximately 3 – 4 coats) Hook #3 had 39.8 mills of paint (approximately 6 – 7 coats) .

A test for equal variances between the tanks hanged from hooks 1 and 2 taking 11 sample readings from each, indicated that we must fail to Reject the null hypothesis of equal variances (P > .5). Knowing this, a 2-Sample T-test can be performed to check for differences in mean.

Figure 16. Test for equal variances hooks 1 & 2

The resulting P-Value for the mean test was 0.162 suggesting that there is no significant mean difference between hooks 1 & 2. In production terms, this can be translated to having little to no effect on the tank paint thickness by using a hook without any coat of paint than to use one that has 3-4 coats of paint.

The third hook was also analyzed to see the effect when measured against the first one. This time, a Test for equal variances proved that there was a significant variance between them (P < .5). Instead of using a 2-Sample T-test, an individual value plot graph is shown below.


Figure 18 demonstrates the significant variance when using a hook that has about 6 – 7 coats of paint (approximately 39.7 mils) versus one that has which paint has been recently removed.

IMPROVE

As an improvement, I recommend implementing standard practices to improve on the following 2 factors: Paint Gun Settings and Hook Grounding

By adjusting the paint guns to the previously determined settings, and using a significant size random sample of 100 values, a Minitab Box-plot analysis of the Before vs. After stage shows the following improvement. The shift in median for each, Tank and Bottom Paint Thickness (millage) is evident as well as the reduction of standard deviation.


Another form to observe these improvements is by taking these same values and plotting them on X bar – S chart and compare Before vs. After Stages (Figure 21). These values were grouped by 5 since each time each shift takes 5 samples at a time.

Figure 21. X bar-S chart of Before and After Stages

Based on the new values of Paint Thickness (millage) and considering the reduction in median of 5.4 to 4.2 for Tank and 5.6 to 4.7 in Bottom, we can conclude an average of 20% reduction. This amounts to $40,000 in savings ($204,250 x 20%).


CONTROL

In order to assess control of the system, it was required to monitor it and observe any deviations from the established acceptable settings determined earlier. All forms were changed to reflect this change and to instruct operators how system is to be run. As part of the training, a basic visual management tool aspect could not be overlooked. The picture below (Picture 1) demonstrates such visual implemented. In it we visually specify (by the blue marks) where the settings of the paint guns are to be kept. This will ensure reduced variation between the operators and serves as a quick reference of status in case machinery faults out or some other uncontrollable events occur at any given time. It can confirm standardization throughout shifts and will immediately recognize out-of-standard situation.

Picture1. Paint gun controls

To control the Hook Grounding factor, a schedule for paint removal of the hooks was established in order to maintain the least amount of paint (millage) on them. Each shift will be responsible for taking care of a certain amount of hooks per day.

It is expected to have a better control of the paint thickness by implementing both of these changes. This will also help reduce the cost to produce a tank for our pole-mounted transformers.


APPENDIX A A-1

SAPOC

A2

Standard Log Form

Stakeholders (Identify names for each functional area identified) Suppliers Process Owners Customers Raw materials Buyer Plant Manager Dept.

Supervisor Lead Person Operators: Painter Maintenance Tech

Next step Drop Station

Of data & information Paint Thickness Log -Unhang Operator

Downstream Casing -Dept. Supervisor

Of human resources HR Manager

Consumer Crating -Dept. Supervisor

Of financial resources Plant Controller Regulatory ANSI

Key Metrics Measurable Inputs, xs Process Metrics, xs and Ys,

ys Measurable Outputs, Ys, ys

Paint Lbs / unit Paint Millage

Documents

Paint Thickness Reduction - Dashboard - Confluence