inpursuitofoptimal#weld#parameters# …

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IN PURSUIT OF OPTIMAL WELD PARAMETERS – THE HOW TO –

By Chris Bertoni Director, Quality & Technical Services, 4Front Engineered Solutions Overview There is nothing new about a quest for an Optimal weld. Attempts to improve weld quality are a routine challenge for most folks in the welding industry. This article offers a five step method to finding the optimum or ‘best’ weld. The method detailed below utilizes a statistical tool known as the two level factor approach. This well-‐tested method will assist an investigator in identifying what is to be optimized, choosing inputs for evaluation, running the tests so that statistically significant data is generated, analyzing data, and finally determining the settings for the significant inputs which result in the optimum weld. While there are many methods that can be used to find optimums, it is the author’s belief that the relative intuitiveness and simplicity of the two level factor design approach make it the best choice for most welding optimization projects. The approach is not a panacea. It requires specialized software, it can be costly and, on occasion, even after following all the steps an optimum weld may not be found. The article concludes with an application of the five step approach to a recent real-‐world weld improvement example. What Is An Optimum Weld? Selecting weld parameters is a regular part of welding. In general there are two broad scenarios when weld parameters are explored. The first scenario occurs before a production arc is struck. Prior to production, during the Procedure Qualification Record/Welding Procedure specification (PQR/WPS) qualification of a weld procedure, weld settings are specified, samples are run, and testing performed. If samples fail to meet testing requirements, weld parameters are changed and the tests are continued until a conforming weld is produced. The settings are recorded and the process released for production. At this point adequate weld settings have been found, but it could be wondered if different weld settings would produce a better weld. The same question can be asked of a weld engineer. For instance, is there a better design (less amount of weld, less need for pre or post treatment, different less expensive weld process, etc.) available? While this same five step approach works for weld design as it does for weld procedures, the focus of this article will be on weld procedures. The second scenario when weld parameters are explored occurs when adjustments are continuously required to stabilize a weld process which drifts in to and out of producing a conforming weld -‐ sometimes good welds are made and sometimes marginal welds are made. The general solution here is to call in a weld expert who, based on experience, adjusts the weld settings to produce an improved weld. Should the weld process drift again perhaps a different weld expert will be called upon for assistance. In which case, perhaps, different weld settings will be generated. In each case adequate weld settings will have been found, but a similar question remains as with scenario 1. Would different weld settings produce a more robust weld? Sometimes good enough is all that is needed. But, on occasion, what is desired is not weld settings that produce an acceptable weld but weld settings that produce the best or the optimum weld. Perhaps folks are tired of adjusting the weld settings day in and day out. Or perhaps the weld team is tired of two weld experts expressing their opinions as to who is right when it is required that a weld process be brought back to conformance. Or perhaps a third party is insisting on being provided documentation showing weld improvements following a weld failure in the field. In such cases there is a need to find better weld settings, weld parameters which will achieve more than an adequate weld. What is needed is the optimum weld. An optimum weld parameter can be either a maximum or a minimum. For instance, if the reduction in weld splatter is the goal, then the optimum is found when the inputs are adjusted so as to produce the minimum amount of weld splatter. Conversely, if the strength of the weld is the goal, then the optimum is found when the weld inputs are adjusted so as to produce the maximum strength weld. To find the optimum weld the first step is to state the problem, and the second step is to select weld output(s) which are measurable. For instance the problem/goal could be to reduce a particular weld failure in the field and

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the two outputs to optimize could be depth of penetration and size of the weld. Depending on the goal of the experiments, any number of weld features is candidates for optimization. Examples include depth of penetration, amount of porosity, size of the weld, amount of splatter, weld appearance, the number of weld defects, weld speed, etc. The optimization of the weld output(s) is accomplished by varying weld input settings. Inputs can be broadly classified as controllable or uncontrollable. The controllable inputs are ones which can be varied to determine how much they affect the desired output. Some typical controllable inputs are the volts, filler material type, cover gas, push versus pull, drag angle, surface finish of the parts, operator training, etc. Uncontrollable inputs are inputs that are not varied and can include drafts, cleanliness of parts, ambient temperature, etc. To improve a weld output, therefore, adjustments are made to the weld inputs – in an effort to see if a better combination of inputs can be found which will result in a better weld output. Generally, a change in one of the weld inputs will result in an improvement or deterioration in the weld output/goal of interest. The weld input settings which result in the best output(s) are the optimum weld parameter settings. An optimum weld setting then is one where weld parameters (inputs) are chosen which maximize (depth of penetration, weld travel, etc.) or minimize (number of defects, amount of splatter, etc.) desired output(s). There are usually several inputs which are varied during test runs or trials and one (or more) outputs which are evaluated. See Figure 1.

Figure 1. Example of inputs and outputs for weld testing. Once found, an optimal weld setting is not the only weld setting that will ever be needed. If input parameters drift outside of test limits or if new conditions develop, the input parameters may to be tweaked. How An Optimal Weld Is Found Today Let's begin with an example. Consider the situation where one discovers a nonconforming weld that is covered by a WPS but one where the weld parameters have not been optimized. In this example, we will pretend that this particular weld requires frequent attention as the weld slips in to and out of conformance – sometimes good welds are made and sometimes not so good welds are made. The two most common approaches taken to returning the weld to conformity are shown below in their order of complexity: -‐ Ask the weld expert approach

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-‐ Traditional or One Input at a Time test approach Ask the weld expert When a nonconforming weld is discovered, often the solution begins with finding the weld 'expert' (that's the welder or weld engineer with the most gray hair or, the person with the least hair, as is in my case) and asking them what to do. From experience the weld expert will, in short order, find a new weld setting which will produce conforming welds. Knobs will be turned, tips examined, words will be mumbled and possibly new settings will be recorded. Victory will be declared and production will resume. That is, until a marginal weld is discovered once again. When this happens, the weld process will once again come under the scrutiny and attention of a weld expert and the readjustment process will be repeated. Most weld problems are addressed with a high degree of success by the weld expert. However, depending upon the frequency and severity of the consequences of a nonconforming weld, additional, more formal testing may need to be done. Traditional, or One Input at a Time When a nonconforming weld is found and either the weld expert is unable to achieve lasting success or the circumstances require that tests be run, the approach chosen most often is the traditional or one input at a time approach. The traditional approach normally begins with a meeting where the problem is defined, the outputs (what we wish to optimize -‐ maximize or minimize) are chosen and, the input variables are selected. Next a range is established for the input variables – a high and a low. Details of each of these steps are provided later in the article. A single input is selected from all of the inputs as the first input to vary, call it the trial input, and the other inputs are called the held constant inputs. For the first test the held constant inputs are set to their best guess individual optimum high or low values. Then a test is run with the trial input at let’s say its low value followed by a second test with all other inputs held constant and unchanged while the trial input is now set at its high value. Often the first two tests are run again to confirm the results. The results are recorded and the trial input value which resulted in the better output response (either when it was set at the high value or when it was set at the low value) is locked in. This trial input now becomes part of the held constant inputs and a new trial input variable is selected from the formally held constant inputs. Two additional tests are now conducted, often to be repeated to be sure of the results, one with the new trial input set to a low value and another test with the trial input set to a high value. The results from the second set of tests are analyzed and the value for the second trial input which resulted in the better output value is selected, moved to the held constant inputs, a new trial input selected and the process continued until all inputs have had their turn. An simple two input example is offered below. For this example it is assumed that the goal is to improve weld penetration and that the team choose the outputs and controllable inputs as below.

Output variable -‐ depth of penetration, rated after visual inspection of a cross section Inputs (controllable) – volts and cover gas setting Inputs (uncontrollable) -‐ everything else: drafts, cleanliness of parts, fixturing, cover gas, etc. High and low settings – see Table 1:

Input

Volts (DC) Gas (CFH)

Setting Low 26 35

High 33 50

Table 1. Controllable inputs and their high and low settings. (Note while the following results are borrowed from a real example they have been fudged a bit to be useful as an example.)

The test is begun by picking gas flow at high (50) as the held constant input and voltage, as the trial input, varying between low (26) and high (33). The tests will be run twice to increase the confidence in the results. Table 2 below shows the results from the first test. It is noted that runs 3 and 4 yield the best depth of penetration – the average of two runs at 4.1. As the quest is to maximize the depth of penetration, with the gas set at high (50), a better output is obtained when the voltage is at a high (33) than when voltage is set at low (26). The last trial is run with the volts now held constant at high (33) and the gas flow set to low (35). As the results from runs 5 and 6 are lower than the results from 3 and 4 we declare that an optimum weld setting for depth of penetration has been found which is Volts set at high (33) and Gas Flow set at high (50).

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Input setting

Order Run Volts (VDC) Gas Flow (CFH) Result Ave Result Trial / Test Ru

n 1 1 low 26 high 50 1.7 2.0

1 2 low 26 high 50 2.2 1 3 high 33 high 50 3.8

4.1 1 4 high 33 high 50 4.3 2 5 high 33 low 35 0.9

1.1 2 6 high 33 low 35 1.2

Table 2. Traditional testing results identifying the settings for runs 3 and 4 as producing the optimum weld.

Something New Within the field of statistics a specialized body of knowledge exists known as Design of Experiments (DOE). Individuals within the DOE group assist (or equip) others by providing the tools and information needed to run tests and make statistically significant decisions as to the influence a particular input has on an output. The main tool used by the practitioners of DOE is specialized software. Within such software packages can be found many options as to the design of the test. Included are tests such as Box-‐Behnken, Plackett-‐Burman, two level factor design, and Taguchi. It is the author’s experience that the most appropriate DOE test for finding an optimum weld setting is the two level factor design and as such it is the only test which will be explored. Two level factor design The two prime reasons that the two level factor design is recommended as the test design approach are that it is more efficient and better able to find an optimum than is the one input at a time approach. Also it is not as complicated as other Design of Experiment approaches. It is the middle ground so to speak. As shown later the two level factor design test is accomplished with fewer runs (cheaper) and that even with fewer runs it is more likely to find an optimum (more successful) than is the one input factor at a time approach. However, to take full advantage of this approach to finding an optimum a person with access to the DOE software and knowledgeable in the subject area of DOE will need to become part of the team. To emphasize the advantages of the two level factor design over the traditional approach the example begun above will be continued. The start of this test approach is similar to the traditional or one input at a time approach in that it normally begins with a meeting where the problem is defined, the outputs are chosen, the inputs are selected, and a test range is established for the input variables – a high and a low. The similarities end here. The next step in the two level factor design is to list all possible combinations between the inputs and then to randomize the order of the test. Randomization is done to mitigate the influence of some uncontrollable inputs such as machines warming up and drifting during the test or an operator becoming tired as the test progresses, etc. For example, Table 3 lists the four possible combinations of volts and gas flow.

Volts (VDC) Gas Flow (CFH)

low 26 low 35

high 33 low 35

low 26 high 50

high 33 high 50

Table 3. The four possible combinations of high and low for the two inputs chosen. The results from the randomized run are entered in Table 4 below. These are the same numbers as were used in the first trial run from Table 2, the traditional, one input at a time approach.

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Input setting

Order Volts setting Gas Flow Result

Trial / Test

run

1 high 33 low 35 0.9

2 low 26 high 50 1.7

3 high 33 high 50 3.8

4 low 26 low 35 4.9

Table 4. Randomized run order results for the two level factor design trials. Notice two things from Table 4 above. First, a better setting for depth of penetration was found, 4.9 versus 4.1. This occurred from a combination of inputs, Volts low and Gas Flow low which was not even tested in the traditional approach. Second, note that the optimum was found in 4 trials versus 6 trials. The question can be asked: Is there as much confidence in the results from the two level factor approach as with the traditional approach? In the traditional approach to improve confidence in the results two runs were made for each trial while in the two level factor approach only one run was made for each trial. Would this not imply that the traditional approach provides an answer with more certainty -‐ more confidence? The answer is no. With the two level factor approach note that each and every input is tested twice -‐ at a high level and a low level -‐ providing the same confidence in the result as with the traditional approach. The reason that the traditional approach found a non-‐optimal solution is that in the traditional approach factor interactions are ignored. Hence, should the voltage effect on the depth of penetration change as the gas flow changed the traditional approach would miss this interaction. As the two level factor tests all inputs at high and low settings, interactions are captured. The interactions for this example can be graphed and viewed as shown in Figure 2 below. Parallel lines indicate no interaction while non-‐parallel lines indicate an interaction between the inputs.

Figure 2. Graph of the Volts and Gas Flow interaction data. As parallel lines do not result, data supports that there is an interaction between Volts and Gas Flow.

Later in this article it will be shown that when using the two level factor approach the most significant inputs and their interactions can be found using simple arithmetic. To summarize, the benefits and disadvantages of the three approaches used to find an optimum weld setting are: Ask the weld expert The benefits of this approach are that the results are immediate and, if the weld expert is an internal person, the cost is often considered to be zero. The disadvantage is that one does not know if an optimum weld was found as the solution is based on the experience of the individual weld expert. Two experts may resolve the problem with a

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different fix, and on occasion each may feel the other’s approach is non-‐optimum, sending mixed signals to the team. Traditional or One Input at a Time One benefit of this approach is that it is intuitively pleasing. It makes logical sense to many technical folks. In addition, it is an approach many have used successfully to find a better weld result. Also, no outside expert is required as a team member. The disadvantages are that it is wasteful of test resources as shown above. To achieve the same confidence as the two level factor design the traditional approach took 6 runs while with the two level factor design only 4 runs were required. The traditional approach only tests for main factors and is blind to interaction of factors such as the gas flow / volt setting from Figure 2. As shown above, this approach can miss the optimum. As run order is managed e.g. pick one trial input and vary it then pick another trial input, the benefits of randomization are lost. Finally the inputs chosen may not be statistically significant. Two level factor design The three main benefits of the two level factor design over the traditional approach to finding an optimum are that a better optimum may be discovered due to considering input interactions and the randomization of the test, less runs will be needed, and the results will be statistically significant. A disadvantage of the approach is that a person familiar with the subject area of design of experiments is needed as a member on the test design team. This outsider’s primary responsibility is to develop the test plan, evaluate the data, and present the conclusions. Furthermore, time will need to be devoted to educating this expert as to the processes involved, the input factors, and the problem. This adds cost and lengthens the project time line. More About The Two Level Factor Approach -‐ A Golf Example The five step approach to find an optimum is presented below. A golf example will be used to introduce and formalize the five step approach and provide the final comments on the superiority of the two level factor testing approach over the ask the expert or the traditional, one factor at a time testing approaches.

1. State the problem and chose the outputs 2. Choose the inputs and determine their levels for testing 3. Select a test/experiment approach 4. Conduct the test and collect data 5. Analyze the data to determine the optimum

1. State the problem and chose the outputs The golfer in this simplified example wishes to optimize the accuracy of their drive. To measure the accuracy of the drive the golfer decides to create a bull’s eye and reward drives where balls that lie closer to the flag receive a higher score (see Figure 3). In this case the output optimum is achieved by maximizing the score.

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Figure 3. Golf target example. A higher score (output) is

recorded when the drive lands closer to the flag 2. Choose the inputs and determine their levels for testing There are, of course, a great many inputs which could be varied to see which are significant to maximizing the score. Inputs for consideration would include the direction the wind is blowing, the wind speed, the manufacturer or style of driver, the amount of rest had before the test, how overcast or bright the sky is, height of the grass, dryness of grass, whether the head stays down during the swing, etc., etc. For the sake of this example our model will consider just two controllable inputs, is the golfer’s arm remain straight, and what is the length of the golfer’s cleats. All other inputs will be considered uncontrollable. These two inputs were chosen to demonstrate that the two level factor design can accommodate both categorical and attribute data, e.g. non numeric data (such as does the glass feel wet, etc.) as well as numerical input information. The levels will be set as below: Is the golfer’s arm straight? -‐ The golfer’s arm is straight -‐ The golfer’s arm is not straight Is the length of the golfer’s cleats important? -‐ The golfer’s cleats are 1/4” long -‐ The golfer’s cleats are 3/4” long 3. Select a test/experiment approach The test approach to be used is the two level factor approach. As mentioned earlier, there are two reasons for this, one, is that it is superior to the traditional or one input at a time design approach (a better optimum may be discovered, less runs will be required, and the results will be statistically significant) and two is that it is the author's opinion that this is the most appropriate test for welding. In a classic full two level factor approach our test would look something like Table 5 below:

Test Run Input 1 Input 2 Results (zone)

1 Arm not Straight 1/4" Cleats

2 Arm Straight 1/4" Cleats

3 Arm not Straight 3/4" Cleats

4 Arm Straight 3/4" Cleats

Table 5. Listing of all possible combinations of inputs

Note that we need to run minimum of (4) tests to cover all the different combinations or runs. (Or two times (4) or three times (4) if we wish to run replicates to improve the certainty of our results.) Now we all know that varying just the two chosen variables may or may not make any difference on how close the golfer gets to the hole. In fact the variables chosen for the test may not have any predictive value at all. Our test results from this test may prove

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to be completely random. If our testing suggests this, the tests can be rerun the test with a different mix of variables. Let's assume that for this particular golfer the variables chosen will be significant. The next thing is to randomize the run order. A random number would be assigned to each trial. Perhaps the order would be test run 3 followed by test run 1 followed by test run 2 followed by test run 4. Randomization is a critical test element. It helps even out the playing field when there are uncontrollable variables (and this is always the case). Uncontrolled variables include wind direction, dryness of the grass, style of driver, etc. When inputs cannot be controlled it is best to average out their effects – and often randomization is the best method. 4. Conduct the test and collect data Data will be created as needed to demonstrate the two level factor design approach. The results from the trial are recorded below. The results have been entered back in to the original table and are shown in Table 6.

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Table 6. Golf example #1.

5. Analyze the data to determine the optimum After examining Table 6 what might be concluded is:

-‐ The drive is better (remember that closest to the pin in zone #5) while wearing shoes with 3/4”cleats landing in zone #5 every time.

-‐ And that whether the golfer's arm is straight or not does not makes a difference. With the arm held straight, the ball lands in zone #5 one time and another time the ball lands in zone #1. Also, with the arm held not straight, the ball lands in zone #5 one time and another time the ball lands in zone #1.

A little later it will be shown how to convert this table in to a number format and calculate which variables result in the best drive better using simple arithmetic. For the time being our powers of observation will be the guide. Rather than the results above from golf example #1 perhaps different test outputs resulted, for example, the results as in Table 7 below:

Table 7. Golf example #2. For Golf example #2 what would be concluded? It might be concluded that:

-‐ The drive is better (remember that closest to the pin is zone #5) when the golfer's arm is straight. Here the ball lands in zone #1 every time.

-‐ Also, whether the cleats are 1/4” or 3/4” makes no difference. With 1/4” cleats, the ball lands in zone #5 one time and another time the ball lands in zone #1. Also, with 3/4” cleats, the ball lands in zone #5 one time and another time the ball lands in zone #1.

Time for arithmetic. The next step is to add some numbers so that the output results can be quantified and not dependent upon an intuitive review. The same data tables as above can be converted to meet the need. The variables will be defined as below. It does not make a difference which input is the ‘+1’ or the ‘-‐1‘. If the +1’s and -‐1‘s were chosen differently it would make NO difference.

Arm not Straight = ‘-‐1‘ 1/4” Cleats = ‘-‐1‘ Arm Straight = ‘+1’ 3/4” Cleats = ‘+1’

And Zone #1 = 1 and Zone #2 = 2, etc. The results of this math conversion is to convert golf example #1 (Table 6) and Golf example #2 (Table 7) in to Tables 8 and 9.


1 Arm not Straight 1/4" Cleats #1

2 Arm Straight 1/4" Cleats #1








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Table 8. Golf example #1 converted to a math format

Table 9. Golf example #2 converted to a math format

Next add two columns and one row to Tables 8 and 9 for the ‘math’ results, see Tables 10 and 11. The last two columns are formed when the Result column is multiplied by Input 1 or Input 2. For instance, for row 1 of Golf example #1, Input 1 x Result yields -‐1 as the Input of -‐1 times the result of 1 which equals -‐1. On the same row for Input 2 the result is obtained by multiplying -‐1 times 1 which equals -‐1. And so it goes for the other rows until the table is completed.

Table 10. Golf example #1 Table 11. Golf example #2

Input Result columns added Input Results columns added Once a table is expanded, all that needs to be done is to pick the input with the largest number (absolute value) which is 8 in both examples. Based on the observations above for the first example it was observed that Input 2 (Cleats/No Cleats) was the more important factor than Input 1 (Arm Straight/Arm not Straight). Now by simply observing the summations it is readily apparent that Input 2 (Cleats/No Cleats) has the largest value and hence is the most significant. The same reasoning goes for the second example. Earlier it was observed that Input 1 (Arm Straight/Arm Not Straight) was a more important factor than Input 2 (Cleats/No Cleats). With the completed table it is a straight exercise to identify that Input 1 (Arm Straight/Arm Not Straight) with a summation value of 8 is a more important input that Input 2 (Cleats/No Cleats) with a summation value of 0. So far there is relatively nothing surprising here. Following the traditional approach of varying one input at a time may have achieved the same level of understanding. As stated above, one advantage of the two level factor Design of Experiment approach over the traditional approach is that interactions between the variables can be checked. The step begins with the addition of one more column to the table to capture the interaction effect. To complete the last column (the interaction column) multiply both Input columns (1 or -‐1) with the Results column and enter the answer in the last column. The rows are summed as before. Remembering that:

Test Run

Input 1 Input 2 Results (zone)

1 -‐1 -‐1 1

2 1 -‐1 1

3 -‐1 1 5

4 1 1 5

Test Run

Input 1 Input 2 Results (zone)

1 -‐1 -‐1 5

2 1 -‐1 1

3 -‐1 1 5

4 1 1 1

Test Run

Input 1

Input 2

Results (zone)

Input 1 x Result

Input 2 x Result

1 -‐1 -‐1 1 -‐1 -‐1

2 1 -‐1 1 1 -‐1

3 -‐1 1 5 -‐5 5

4 1 1 5 5 5 Total 0 8

Test Run

Input 1

Input 2

Results (zone)

Input 1 x Result

Input 2 x Result

1 -‐1 -‐1 5 -‐5 -‐5

2 1 -‐1 1 1 -‐1

3 -‐1 1 5 -‐5 5

4 1 1 1 1 1 Total -‐8 0

This Times this

Equals this

-‐1 -‐1 1

1 -‐1 -‐1

-‐1 1 -‐1

1 1 1

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In the case, Golf example #1 (Table 10) becomes Table 12.

Table 12. Golf example #1 (Table 10) with an interaction column

So, given the results from Golf example #1 the conclusions remain the same. Input 2 (Cleats/No Cleats) is the significant variable and the interaction effect is nonexistent. It is now time for Golf example #3. The results are shown below in Table 13. What could be concluded based on an observation of the data below?

Table 13. Results for Golf example #3 Probably not much. Converting Table #13 to a math table without the interaction column results in Table 14.

Table 14. Golf example #3 converted to a math format Table #14 suggests that neither Variable 1 (Arm Straight/Arm Not Straight) nor Variable 2 (Cleats/No Cleats) make a difference in how far the result of the drive. The suggestion is that the variables are insignificant. But, if the math table is expanded and the interactions included the Table 14 becomes Table 15.

Table 15. Golf example #3 interaction column added Table 15 shows that the significant factor here is the interaction and not the individual Inputs. What is seen an interaction between Input 1 and Input 2. Specifically if Input 1 is a ‘+’ and Input 2 is a ‘-‐‘ OR Input 1 is a ‘-‐‘ and Input 2 is a ‘+’ our drive is close to the pin. On the other hand if both are ‘-‐‘ s OR both ‘+’s our drive is far from the

Test Run

Input 1

Input 2

Results (zone)

Input 1 x Result

Input 2 x Result

Interaction Input 1 & 2

1 -‐1 -‐1 1 1 -‐1 -‐1

2 1 -‐1 1 -‐1 -‐1 1

3 -‐1 1 5 5 5 5

4 1 1 5 -‐5 5 -‐5

Total 0 8 0






Test Run

Input 1

Input 2

Results (zone)

Input 1 x Result

Input 2 x Result

1 -‐1 -‐1 1 -‐1 -‐1

2 1 -‐1 5 5 -‐5

3 -‐1 1 5 -‐5 5

4 1 1 1 1 1

Total 0 0

Test Run

Input 1

Input 2

Results (zone)

Input 1 x Result

Input 2 x Result


1 -‐1 -‐1 1 -‐1 -‐1 1

2 1 -‐1 5 5 -‐5 -‐5

3 -‐1 1 5 -‐5 5 -‐5

4 1 1 1 1 1 1

Total 0 0 -‐8

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pin. So if the golfer's arm is straight (1) and the cleats are 1/4" (-‐1) OR the golfer's arm is not straight and the cleats are 3/4" this results in a drive which is closest to the pin. There are two additional results that could have occurred. One is that no input or interaction is significant and the other is that there are no clear significant inputs or interactions. In Golf example #4, Table 16, each test run results in the golf ball landing in zone 2. As the summation totals are all zero, this suggests that none of the selected inputs or their interaction is significant, meaning the inputs chosen have no effect on the results. At this point additional inputs should be considered and tests with these new inputs would need to be run to see if any of them have an effect on the output.

Test Run

Input 1

Input 2

Results (zone)

Input 1 x Result

Input 2 x Result


1 -‐1 -‐1 2 -‐2 -‐2 2

2 1 -‐1 2 2 -‐2 -‐2

3 -‐1 1 2 -‐2 2 -‐2

4 1 1 2 2 2 2

Total 0 0 0

Table 16. Golf example #4 As the summation totals for input 1, input 2, and the interaction are zero, the

data suggests neither input nor interaction is significant.

In Golf example #5, Table 17, each run results in the golf ball landing in zones 3, 4 or 5. The closeness of these outputs makes finding a significant input more difficult than if the golf ball landed in zones 1 -‐ 5. The summation totals do not suggest a clear significant input. Perhaps Input 1 is significant then again perhaps it is not. To find out if input 1 is significant a statistical analysis of the data known as an analysis of variance (ANOVA) should be performed. The ANOVA test helps determine the probability of an input being significant versus the probability that the result is due to chance (hence the input is insignificant). Anytime a two level factor design is run, an ANOVA table should be generated to check the statistical significance for the inputs and interactions. This check will normally be done as part of the software provided by the design of experiment subject matter expert. The ANOVA test for significance uses the data already developed from the tables. The ANOVA calculation will not be covered in this article but it comes standard with Design of Experiment software.

Test Run

Input 1

Input 2

Results (zone)

Input 1 x Result

Input 2 x Result


1 -‐1 -‐1 3 -‐3 -‐3 3

2 1 -‐1 4 4 -‐4 -‐4

3 -‐1 1 3 -‐3 3 -‐3

4 1 1 5 5 5 5

Total 3 1 1

Table 17. Golf example #5 As the summation totals are close, ANOVA testing is required to determine

statistical significance of inputs and interaction.

In summary the five step method was presented using several golf examples. The heart of the five step method is the two level factor design approach. Details of how the two level factor design approach "discovers" the significant factor(s) and their interactions were shown using data tables. Not covered was the use of an analysis of variance (ANOVA) when the summation does not clearly identify a superior input. Real-‐World Example

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The five step approach will be further developed by covering a recent real world example. This project was begun in response to a weld failure in the field (see Figure 4). A visual analysis of the welds confirmed that the failed welds lacked fusion between the tube and the angle. Some process specifics follow:

-‐ 3/16” x 2” flare bevel joint (GMAW) -‐ 1.25” OD x 0.688” ID x 2” long tube (A36) -‐ 3” x 3” 1/4” angle (A36) -‐ 90/10 (Co2/Ar) gas mixture -‐ 0.052” E70C-‐6M electrode

Figure 4. Tube Angle 1. State the problem and choose the outputs A team was selected and chartered with the responsibility of investigating the nonconformance. The core team members included: the Director of Quality (AWS certified weld inspector), the Manager of Quality, the Director of Facilities (AWS certified junior weld inspector), the Maintenance Lead (AWS certified weld inspector), and the Research and Development Technician (a subject matter expert). Plant welders are position ‘qualified’ to AWS D1.1 (American Weld Society Structural Weld Code). The joint design, base materials, and filler material (electrode) in this case are all prequalified per AWS D1.1. However, there was no documented weld procedure for this particular weld. Hence, there was ample opportunity for weld variability caused by welder to welder technique differences and weld machine set up. It was agreed that documenting and controlling welder set-‐up parameters and weld procedures would minimize the likelihood of this nonconformance from occurring again. During the first meeting the problem statement became: Improve fusion between the tube and the angle. The goal of this design of experiment was to identify and understand the critical parameters and associated interactions for the tube to angle weld. During this step the team created a list of outputs that were indicators of what was to be optimized. In this example Weld Appearance, Weld Size, and Depth of Penetration were chosen as outputs which were to be optimized. Agreement was also reached as to how the outputs would be measured.

1. Weld Appearance – Visual on a scale of 1 – 5 (porosity, inclusions, voids, etc.) 2. Weld Size – Weld gage 3. Depth of Penetration – Visual after cross sectioning and polishing on a scale of 1 -‐ 5

For the first output the welds were to be examined and graded in situ. For outputs two and three the adjusting sleeve assembly would be cross sectioned, polished and etched, and then graded. See Figure 5 below for examples of nonconforming and conforming cross sectioned, etched and polished welds.

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Figure 5. Nonconforming Weld Conforming Weld 2. Choose the inputs and determine their levels for testing

Figure 6. Meeting room Once the outputs were selected, the team focused on inputs which would affect the outputs. These were the inputs which were to be varied during testing in hopes of finding an optimum output. An optimum in this case is where each of the three outputs is maximized. Figure 6 shows the white board used during discussions. At these meetings the inputs and their levels for testing was determined. A fishbone diagram was generated to list all reasonable inputs (see Figure 7).

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Figure 7. Fishbone diagram of all reasonable inputs as judged by the team

Then the inputs were categorized as to those to be varied, those to hold constant, and those to be ignored (nuisance). Low and High settings were agreed to. The low and high settings were chosen by team subject matter experts based on their experience and supplier provided literature -‐ e.g. the weld wire provider. The Control inputs were:

1. Gas flow -‐ 35 -‐ 50 CFH 2. Voltage -‐ 26 -‐ 33 VDC 3. Wire speed -‐ 258 -‐ 500 IPM 4. Push versus Pull 5. Weave versus no weave

The Held-‐Constant inputs were:

1. Drafts / wind -‐ Fan in the area will be turned on 2. Type of gas

-‐ Use the plant standard 90/10 (Ar/CO2) gas mixture. It is piped from central storage tanks 3. Wire diameter -‐ Use the plant standard 0.052” 4. Type of weld machine -‐ Use the existing Miller LN-‐7 with a Lincoln CV-‐300 power supply 5. Polarity -‐ Use the plant standard DCEP 6. Wire material

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-‐ Use the plant standard E70C 7. Clip versus no clip -‐ Clip the electrode after each weld 8. Ground clamp position -‐ Continue with the ground as it is positioned 9. Operator -‐ The operator will be the Maintenance Lead -‐ an AWS Certified Weld Inspector.

10. Part materials -‐ The parts being joined are ASTM A36

11. Weld position -‐ As there will be no change to the fixturing, the weld will be performed in the flat position

The Nuisance inputs were: 1. Cleanliness of incoming power 2. Contamination of parts -‐ scale, rust, oil, water, paint 3. Tip to work distance 4. Work angle

Now that the input variables were chosen and their high and low settings determined it was time to select the test design. 3. Select a test/experiment approach At this point in the hunt for an optimum a design of experiment subject matter expert will take the lead. The two factor design approach covered in the Golf examples is officially known as a 'full' two level factor design. This is a powerful design, but as the number of inputs increase the number of interactions multiply and the number of tests required become quite large. For instance if there were four inputs say A, B, C and D, the two factor interactions are AB, AC, AD, BC, BD, and CD while the three factor interactions are ABC, ABD, and BCD and the four factor interaction is ABCD. Rather than just two inputs creating one interaction, as the level of inputs grow there become three and more interactions which in the real world are unlikely. Basically in the 'full' two level factor design after about three inputs a fractional versus 'full' two level factor design is chosen. This fractional design approach eliminates the need for running tests which provide information about complex input interactions that are relatively useless. This allows for a reduction in the number of tests needed. It was determined by subject matter experts that third order and higher interactions were difficult to imagine, e.g. for instance it was felt that there was little likelihood that Gas flow, Voltage and Wire speed would interact together resulting in a significant change in an output. The test model chosen was a 25-‐1 fractional factorial with four center points to screen the effects of the chosen factors on the response variables. This meant that twenty samples were needed to be run. This is a good design choice for finding first and second order interactions. A randomized run sheet was prepared by the software1 as shown in Table 18.

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Table 18. Randomized run order for the trial runs.

4. Conduct the test and collect data The run was supervised by members of the team. During the test, all samples were identified with the run number. Results from the run are shown in Figure 8.

Figure 8. Samples prior to visual inspection

After the run, the samples were rated by four experienced individuals – three AWS experienced Weld Inspectors and one subject matter expert. These individuals were told to rate the visual appearance of the samples on a scale of 1 – 5 with 1 being the poorest weld and 5 being the best weld. As the adjusting sleeve to upright angle weld produced two welds per sample, each inspector provided two ratings for each sample. After examination of the data, it was decided that the differences were negligible and the two ratings were averaged. Measurement of the weld size proved to be a difficult task using standard weld gages. However, the weld size was averaged and recorded for all the samples.

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After the visual weld appearance inspection and the measurement of the weld size were completed, all samples were cross sectioned, polished and etched with nitric acid based solution. These samples were then inspected by the same four inspectors. The depth of weld penetration was evaluated by the four inspectors and the samples were placed in to one of five groups. The samples were reviewed by the group and jointly evaluated. As before, a rating of 1 – 5 was used where 1 indicated that the group felt that the sample exhibited very poor depth of weld penetration while 5 meant that the sample exhibited excellent depth of weld penetration. Sample results are shown in Figure 9.

Cross Sectioned Samples Good Depth of Penetration Poor Depth of Penetration

Figure 9.

The various responses are shown in Table 19 below.

Analyze the data to

determine the optimum The data was entered into a design of experiment software package.1 Several analyses were conducted. In each case the five input variables were contrasted with each of the three outputs: Weld Appearance, Weld Size, and Depth of Penetration. For reference, the five input factors (previously identified) were:

Low ('-‐'s) High ('+'s) A Gas flow (Cubic Ft/hr.) 35 50 B Voltage (Volts DC) 26 33

C Wire speed (inches/min) 258 500 Two weld techniques:

D Weave versus no weave Weave No weave E Push versus Pull Push Pull

Std order

Run order

LC RW CB JC Average

Appearance Rating

Average Size

Penetration Rating Right

Penetration Rating Left

8 1 1 2.5 2 2 1.9 0.2 5.0 5.0

19 2 1 4 3 2 2.5 0.3 2.0 1.0

15 3 1 2 1 1 1.3 0.1 4.0 4.0

3 4 1 1 1 1 1.0 0.1 2.0 2.0

17 5 1 2 1 1 1.3 0.2 1.0 1.0

1 6 2 2 2 2 2.0 0.3 5.0 5.0

12 7 5 5 5 4 4.8 0.3 3.0 3.0

7 8 1 1 1 1 1.0 0.3 1.0 1.0

10 9 4 4 5 1.5 3.6 0.2 3.0 2.0

9 10 4 4 5 3 4.0 0.3 3.0 2.0

2 11 3 3 3 4 3.3 0.3 5.0 5.0

20 12 1 3 2 1 1.8 0.3 1.0 1.0

18 13 4.5 3 4 2 3.4 0.2 3.0 3.0

6 14 4 4 4.5 4 4.1 0.3 4.0 4.0

5 15 2 4 3 3 3.0 0.2 5.0 5.0

14 16 1 2 1.5 1 1.4 0.3 3.0 3.0

11 17 4.5 5 4.5 2.5 4.1 0.3 2.0 2.0

16 18 1 2 2 1 1.5 0.1 1.0 1.0

13 19 4.5 5 4 3 4.1 0.2 4.0 4.0

4 20 1 1 1 1 1.0 0.3 1.0 1.0

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Inputs/Variables versus the Depth of Weld Penetration – Right In Table 20 the primary inputs, results, and interactions (those appropriate for the fractional factorial design chosen) have been calculated and summed.

Depth of Penetration

(Right)Run A B C D E A B C D E AB AC AD AE BC BD BE CD CE DE1 1 1 1 -‐1 -‐1 5 5 5 5 -‐5 -‐5 5 5 -‐5 -‐5 5 -‐5 -‐5 -‐5 -‐5 52 0 0 0 -‐1 1 2 0 0 0 -‐2 2 0 0 0 0 0 0 0 0 0 -‐23 -‐1 1 1 1 -‐1 4 -‐4 4 4 4 -‐4 -‐4 -‐4 -‐4 4 4 4 -‐4 4 -‐4 -‐44 -‐1 1 -‐1 -‐1 -‐1 2 -‐2 2 -‐2 -‐2 -‐2 -‐2 2 2 2 -‐2 -‐2 -‐2 2 2 25 0 0 0 -‐1 -‐1 1 0 0 0 -‐1 -‐1 0 0 0 0 0 0 0 0 0 16 -‐1 -‐1 -‐1 -‐1 1 5 -‐5 -‐5 -‐5 -‐5 5 5 5 5 -‐5 5 5 -‐5 5 -‐5 -‐57 1 1 -‐1 1 -‐1 3 3 3 -‐3 3 -‐3 3 -‐3 3 -‐3 -‐3 3 -‐3 -‐3 3 -‐38 -‐1 1 1 -‐1 1 1 -‐1 1 1 -‐1 1 -‐1 -‐1 1 -‐1 1 -‐1 1 -‐1 1 -‐19 1 -‐1 -‐1 1 1 3 3 -‐3 -‐3 3 3 -‐3 -‐3 3 3 3 -‐3 -‐3 -‐3 -‐3 310 -‐1 -‐1 -‐1 1 -‐1 3 -‐3 -‐3 -‐3 3 -‐3 3 3 -‐3 3 3 -‐3 3 -‐3 3 -‐311 1 -‐1 -‐1 -‐1 -‐1 5 5 -‐5 -‐5 -‐5 -‐5 -‐5 -‐5 -‐5 -‐5 5 5 5 5 5 512 0 0 0 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 113 0 0 0 1 -‐1 3 0 0 0 3 -‐3 0 0 0 0 0 0 0 0 0 -‐314 1 -‐1 1 -‐1 1 4 4 -‐4 4 -‐4 4 -‐4 4 -‐4 4 -‐4 4 -‐4 -‐4 4 -‐415 -‐1 -‐1 1 -‐1 -‐1 5 -‐5 -‐5 5 -‐5 -‐5 5 -‐5 5 5 -‐5 5 5 -‐5 -‐5 516 1 -‐1 1 1 -‐1 3 3 -‐3 3 3 -‐3 -‐3 3 3 -‐3 -‐3 -‐3 3 3 -‐3 -‐317 -‐1 1 -‐1 1 1 2 -‐2 2 -‐2 2 2 -‐2 2 -‐2 -‐2 -‐2 2 2 -‐2 -‐2 218 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 119 -‐1 -‐1 1 1 1 4 -‐4 -‐4 4 4 4 4 -‐4 -‐4 -‐4 -‐4 -‐4 -‐4 4 4 420 1 1 -‐1 -‐1 1 1 1 1 -‐1 -‐1 1 1 -‐1 -‐1 1 -‐1 -‐1 1 1 -‐1 -‐1

-‐1 -‐13 3 -‐4 -‐10 3 -‐1 -‐5 -‐5 3 7 -‐9 -‐1 -‐5 0

Factors Result

Summation of the Result columns Table 20. Real World example -‐ five primary input results and ten two input interactions

Using simple arithmetic it is apparent that the three most significant inputs are B (Voltage) at 13, E (Push versus Pull) at 10 and the interaction of BE at 9. Additional design of experiment tools (half normal, normal plots, Pareto effect of the inputs versus a standard ‘t’ value, and a Bonferroni value) can be used to reach a similar conclusion. A statistical analysis of variation, Table 21 (ANOVA), confirms that the three factors B, E and BE are statistically significant. The design of experiments ANOVA table calculates a 'p' value which is the probability that the factor's effect on the output is due to chance. For the model with the three inputs of B, E, and BE, the p value is 0.0048. This means that there is a less than 1% chance that changes in the output Depth of Penetration -‐ Right caused by varying B, E and BE is due to chance. Hence the three factors are significant and can be used to predict a change in weld penetration. The low R^2 (correlation coefficient) was not considered troubling as per the fishbone diagram one can note the multitude of ‘difficult to control’ factors – tip to work distance, ground clamp position, etc.

Source Sum of Squares df

Mean Square F Value p Value

Model 20.62 3 6.87 6.75 0.0048 B -‐ Voltage 10.56 1 10.56 10.38 0.0062 E -‐ Push-‐pull 5.03 1 5.06 4.97 0.0426 BE 5.03 1 5.06 4.97 0.0426 Curvature 6.93 2 3.46 3.40 0.0625 Residual 14.25 14 1.02 Correlation Total 41.80 19

Table 21. Analysis of variance (ANOVA) demonstrating that inputs B, E and BE are statistically significant as based on their p value being less than 0.05 (5%).

A final consideration is provided by the statistical software. It is used to insure that the sound fundamentals of normality and randomness have not been violated. This is done through residual tests. Plots generated from the software are shown below (Table 22) demonstrating that the residual tests have all been met, e.g. the tests for: normality, independence versus run, and actual versus predicted.

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Table 22. Residual plots for Factors/Variables versus the Depth of Weld Penetration – Right (goal

of the experiment) So, what is the optimum setting to achieve the highest depth of penetration? An interaction plot (Table 23) generated by the software offers a visual presentation of the data.

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Table 23. Interaction of Inputs Push versus Pull and Voltage at 26 and 33 versus the output of Right -‐ Depth of Penetration.

The small green triangles and small red squares in the chart above are the averages of the data at a particular point, e.g. high or low. The lines extending above and below the averages are a measure of the spread (dispersion or variance) of the data. When these spread lines overlap, the results are considered insignificant. In other words the effect of push or pull when the voltage is low on the depth of penetration is insignificant – either pushing or pulling will result in essentially the same output. However, when the voltage is set to the high level, pushing results in a statistically significant better depth of penetration. From Table 24, the interaction plots versus visual it can be shown that the best weld appearance is obtained when the wire feed is low and there is no weave. Low wire feed and no weave appears to offer the most attractive welds.

Table 24. Other interaction plots generated by the software -‐ visual output versus inputs.

Result of Study: When depth of penetration was the output, it was identified that welder voltage and push/pull were statistically significant inputs while gas flow, wire feed, and weave/no weave were not significant. Setting voltage low and welding in a do not weave pattern produced the optimum depth of penetration.

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When visual weld appearance was the output, it was identified that wire feed and weave/no weave were statistically significant inputs while gas flow, voltage and push/pull were not significant. Setting wire feed low and welding in a do not weave pattern produced the most attractive welds. Therefore, the optimum weld for depth of penetration and weld appearance was achieved by setting the voltage low, pushing, setting the wire feed low, and welding in a do not weave pattern. The other input of gas flow is not significant over its test levels and can be set to either high or low. Note that this means that the gas flow can be set to low resulting in a cost savings without compromising either the depth of penetration or the weld appearance. Summary It was shown that finding an optimum weld is a common task in the field of welding, whether it is to create a WPS or PQR, or to stabilize a weld process, or to respond to a failure in the field. Three approaches were explored, the weld expert, the traditional or one input at a time trials, and the two level factor approach. Each testing approach was explored and advantages and disadvantages of each were listed. The advantages of the two input factor approach over the traditional approach were listed as: -‐ Requires less runs for the same confidence level; more efficient -‐ Produces statistically significant results -‐ Tests the entire model space; better able to discover an optimum A five step test approach utilizing a two level factor design of experiment approach was demonstrated in several Golf examples. These showed how significant inputs and possible interactions can be identified using simple arithmetic. Finally the two level factor design and five step approach was further developed with a real world weld example using a design of experiments statistical software package. In weld optimization efforts the traditional one factor at a time approach should not be used. If one is serious in finding a weld optimum, a design of experiment expert familiar with the two level factor design should be add to the test team. To contact Chris Bertoni, phone: 972-‐323-‐6716 or email: [email protected] Notes 1. Design of Experiments software Stat-‐Ease, Design Expert® 8.0.7.1

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