Spatter is defined as droplets of molten material that are formed near the welding arc during the welding process, and ejected from the weld puddle. The ejection of molten material has numerous causes. A prominent cause of ejection is the arc failing to start [1] . In this case, the electrode comes in contact with the workpiece, and joule heating triggers disintegration of the wire. The wire piece ejects away from the tip of the electrode in the form of spatter [2] .
Spatter is often a result of parameters that have not been properly optimized [3] [4] . For example, if there is a momentary short circuit during the process or voltage is not tuned properly, spatter occurs when the breakage in current occurs [5] . Spatter is generally known to increase with increase in voltage [6] [7] . Welding power devices have also been improved to be digitally controlled so that more precise control of welding current and voltage waveform can be used to stabilize the arc and reduce the occurrence of spatter [8] [9] .
With increased welding speed, it is essential to control voltage, shorten the arc, and reduce spatter [10] . Spatter severity is dependent on the composition of the electrodes [11] , and the type of ingredients used in the flux [12] . Welding polarity also has an effect on weld spatter and welding arc stability, when direct current electrode positive (DCEP) and direct current electrode negative (DCEN) welding operations are performed [13] .
CO2 gas bubbles that explode during the welding process can also be the cause of excess spatter [14] [15] . Important parameters that need to be controlled in the gas metal arc welding (GMAW) process, are welding current, welding travel speed, arc voltage, type of shielding gas, wire feed rate, and torch travel and work angles [16] [17] .
The goal of this study is to optimize these parameters and eliminate or minimize weld spatter. In the GMAW process an electrical arc is established between a base material and a continuously fed consumable electrode. The electrode is the filler metal, and is protected from atmospheric conditions by a shielding gas that ensures the integrity of the weld is
Spatter is defined as droplets of molten material that are formed near the welding arc during the welding process, and ejected from the weld puddle. The ejection of molten material has numerous causes. A prominent cause of ejection is the arc failing to start [1] . In this case, the electrode comes in contact with the workpiece, and joule heating triggers disintegration of the wire. The wire piece ejects away from the tip of the electrode in the form of spatter [2] .
Spatter is often a result of parameters that have not been properly optimized [3] [4] . For example, if there is a momentary short circuit during the process or voltage is not tuned properly, spatter occurs when the breakage in current occurs [5] . Spatter is generally known to increase with increase in voltage [6] [7] . Welding power devices have also been improved to be digitally controlled so that more precise control of welding current and voltage waveform can be used to stabilize the arc and reduce the occurrence of spatter [8] [9] .
With increased welding speed, it is essential to control voltage, shorten the arc, and reduce spatter [10] . Spatter severity is dependent on the composition of the electrodes [11] , and the type of ingredients used in the flux [12] . Welding polarity also has an effect on weld spatter and welding arc stability, when direct current electrode positive (DCEP) and direct current electrode negative (DCEN) welding operations are performed [13] .
CO2 gas bubbles that explode during the welding process can also be the cause of excess spatter [14] [15] . Important parameters that need to be controlled in the gas metal arc welding (GMAW) process, are welding current, welding travel speed, arc voltage, type of shielding gas, wire feed rate, and torch travel and work angles [16] [17] .
The goal of this study is to optimize these parameters and eliminate or minimize weld spatter. In the GMAW process an electrical arc is established between a base material and a continuously fed consumable electrode. The electrode is the filler metal, and is protected from atmospheric conditions by a shielding gas that ensures the integrity of the weld is
Spatter is defined as droplets of molten material that are formed near the welding arc during the welding process, and ejected from the weld puddle. The ejection of molten material has numerous causes. A prominent cause of ejection is the arc failing to start [1] . In this case, the electrode comes in contact with the workpiece, and joule heating triggers disintegration of the wire. The wire piece ejects away from the tip of the electrode in the form of spatter [2] .
Spatter is often a result of parameters that have not been properly optimized [3] [4] . For example, if there is a momentary short circuit during the process or voltage is not tuned properly, spatter occurs when the breakage in current occurs [5] . Spatter is generally known to increase with increase in voltage [6] [7] . Welding power devices have also been improved to be digitally controlled so that more precise control of welding current and voltage waveform can be used to stabilize the arc and reduce the occurrence of spatter [8] [9] .
With increased welding speed, it is essential to control voltage, shorten the arc, and reduce spatter [10] . Spatter severity is dependent on the composition of the electrodes [11] , and the type of ingredients used in the flux [12] . Welding polarity also has an effect on weld spatter and welding arc stability, when direct current electrode positive (DCEP) and direct current electrode negative (DCEN) welding operations are performed [13] .
CO2 gas bubbles that explode during the welding process can also be the cause of excess spatter [14] [15] . Important parameters that need to be controlled in the gas metal arc welding (GMAW) process, are welding current, welding travel speed, arc voltage, type of shielding gas, wire feed rate, and torch travel and work angles [16] [17] .
The goal of this study is to optimize these parameters and eliminate or minimize weld spatter. In the GMAW process an electrical arc is established between a base material and a continuously fed consumable electrode. The electrode is the filler metal, and is protected from atmospheric conditions by a shielding gas that ensures the integrity of the weld is
Spatter is defined as droplets of molten material that are formed near the welding arc during the welding process, and ejected from the weld puddle. The ejection of molten material has numerous causes. A prominent cause of ejection is the arc failing to start [1] . In this case, the electrode comes in contact with the workpiece, and joule heating triggers disintegration of the wire. The wire piece ejects away from the tip of the electrode in the form of spatter [2] .
Spatter is often a result of parameters that have not been properly optimized [3] [4] . For example, if there is a momentary short circuit during the process or voltage is not tuned properly, spatter occurs when the breakage in current occurs [5] . Spatter is generally known to increase with increase in voltage [6] [7] . Welding power devices have also been improved to be digitally controlled so that more precise control of welding current and voltage waveform can be used to stabilize the arc and reduce the occurrence of spatter [8] [9] .
With increased welding speed, it is essential to control voltage, shorten the arc, and reduce spatter [10] . Spatter severity is dependent on the composition of the electrodes [11] , and the type of ingredients used in the flux [12] . Welding polarity also has an effect on weld spatter and welding arc stability, when direct current electrode positive (DCEP) and direct current electrode negative (DCEN) welding operations are performed [13] .
CO2 gas bubbles that explode during the welding process can also be the cause of excess spatter [14] [15] . Important parameters that need to be controlled in the gas metal arc welding (GMAW) process, are welding current, welding travel speed, arc voltage, type of shielding gas, wire feed rate, and torch travel and work angles [16] [17] .
The goal of this study is to optimize these parameters and eliminate or minimize weld spatter. In the GMAW process an electrical arc is established between a base material and a continuously fed consumable electrode. The electrode is the filler metal, and is protected from atmospheric conditions by a shielding gas that ensures the integrity of the weld is 48th SME North American Manufacturing Research Conference, NAMRC 48 (Cancelled due to maintained. The arc formed between the consumable wire and workpiece heats the workpiece metal resulting in melting and joining. The process input parameters must be adjusted to minimize noise deviations in output, while the robustness and parameter fluctuations are maximized [18] [19] . Some parameters are programmable including, voltage, and wire feed rate, weld arm traveling speed, whereas others like surface condition, welding process type and wire stick out are not programmable.
One reason improperly set machines can produce excess spatter is that the welding process uses either a globular transfer or a short circuit transfer. A globular transfer is when the weld metal travels across the arc in globs or large droplets. A short circuit transfer is when the wire comes in contact with metal which creates a short circuit prior to detaching from the electrode. Both interrupt the molten weld puddle production and cause some of the puddle to leave the weld puddle in the form of spatter. A way to reduce spatter is to use a spray transfer. Spray transfer uses a transfer method similar to the spray paint process. It deposits metal into the weld puddle from an arc length. In spray transfer the wire electrode does not come into contact with the weld puddle. Another way to reduce spatter is to use pulse gas metal arc (MIG) welding, which generally produces less spatter than standard constant voltage MIG welding [20] [21] . Both constant voltage and pulse MIG welding processes were used for testing.
Investigation into some of these methods to reduce weld spatter can be done through experimentation, which can be optimized using orthogonal arrays based on a technique developed by Dr. Genichi Taguchi. Use of orthogonal arrays reduces the number of experiments and serves as an excellent tool for screening a large set of parameters and identifying the most significant parameters that affects the process performance [22] . Furthermore, Taguchi technique involves reducing the variation of a process through a robust design of experiments (DOE). The objective of the technique is to produce high-quality products at a lower cost to the manufacturer [23] .
A cell phone camera can be used to take high speed video of each experiment. The videos can then be used to analyze the amount and pattern of spatter emerging from the weld [24] . The videos can also be used to analyze the weld puddle to see how much it is affected by the wire electrode, if at all. Still frame captures of each video can also be used to quantify spatter by counting the number of particles coming off the weld [25] .
ImageJ is an open sources image processing program that can be used to count those particles. These spatter particles can be quantified by converting the images to black and white and filtering bit sizes and thresholds within the picture to prevent the weld arc flash from skewing the data. Particle counts will be consistent so long as the still frame images are taken at the same intervals.
The audio coming out of the welding process can also be analyzed to optimize the welding machine parameters. The welding spatter loss is proportional to the arc sound energy produced [26] . A poorly optimized machine will sound choppy and uneven, whereas a well optimized machine will sound like a constant hiss or a buzz [27] . Weight of the test specimen before spatter test
Weight of the test specimen after spatter test 
Test specimen material: The test pieces used were cut from ¼" thick ASTM A572 Grade 50 structural steel that is most commonly used in the industry, where these tests were conducted. As such, ASTM A572 Grade 50 became the obvious choice for selection of this material to be used for spatter reduction tests. The chemical composition of the test specimen material is given in Table 1 . Weld wire material: The weld wire used was Lincoln ED032863 0.045" (1.1 mm) SuperArc L-59 MIG Wire. This wire has an AWS classification of ER70S-6, which conforms to the AWS A5.18/A5.18M:2017 specification for carbon steel electrodes and rods for gas shielded arc welding standard by the American Welding Society. The chemical composition of the weld wire is given in Table 2 . The weld wire's salient features include an engineered alloy system which enhances silicon island management and facilitates minimal spatter. It is coated with copper for long contact tip life, increased electrical conduction, and offers fast travel speeds. MicroGuard Ultra coating provides superior Iqbal Shareef and Christopher Martin/ Procedia Manufacturing 00 (2019) 000- 000 3 feeding and arc stability. Type ER70S-6 is a wire with higher levels of deoxidizers and is suitable for welding of steels with moderate amounts of scale and/or rust. For MIG welding, the wire manufacturers recommendation is to use + CO2 or Argon + CO2 or Argon + 2% Oxygen as shielding gases. In this study 98% Argon and 2% CO2 mix was used for shielding.
The goal of the experimental work was to conduct a series of well-designed experiments incorporating key process parameters that affect the weld spatter. This was in line with the overall goal to reduce weld spatter without sacrificing the quality and cost aspects of production. To identify the most significant parameters that influence production of weld spatter, a well-diversified team of experts with knowledge, experience, and economic stake in weld spatter reduction was formed. The team consisted of robotic cell operators, sections managers, and subject matter experts both from industry as well as from academia. With the team in place the first brainstorming session was held only to collect as many ideas as possible. During the second brainstorming session all ideas collected and the parameters listed were shared with the team and the team was asked to identify the most significant parameters affecting weld spatter in each members' view. The ideas generated in the 2 nd brainstorming session were prioritized in the order of importance assigned by the team members and the team was asked to finalize the number of parameters to be tested. A final brainstorming session was held that resulted in 10 input parameters for testing. These included: i) Gas Mixture, ii) Grounding Wire Length, iii) Electrical Wire Stick-out, iv) Surface Condition, v) Weld Process Type, vi) Weld Direction, vii) Wire Feed Speed, viii) Arm Travel Speed, ix) Voltage, and x) Torch Travel Angle. Once the 10 most significant parameters to be tested were identified, each parameter was further examined from a practical feasibility point of view and the number of levels needed to investigate each parameter was selected through team consensus, keeping in mind the best approach to study weld spatter. Even after the selection of input parameters and the corresponding number of levels to be tested for each parameter was finalized, there were three additional revisions made before the very final design that was actually implemented. The ten input parameters and the corresponding levels of investigation are listed in Table 3 . 
Having arrived at ten different input variables with each designated to be tested a specific level, the investigation team carefully examined various test options. After initial review of the experiment the option of testing one variable at a time while keeping the rest constant was ruled out in the interest of optimizing time and resources needed to conduction the experiment. Consequently, alternative design of experiments was explored. With four parameters at 4-levels, four additional parameters at 2-levels, and last two parameters at 1-level listed in Table 3 , the most feasible and appropriate design suitable for determining the effect of 10 parameters on weld spatter was determined to be Taguchi's L16 orthogonal array. Table 4 lists Taguchi's L16 orthogonal array that was run in randomized order to eliminate any biases that may arise in the experiments. Unlike in the MIG constant voltage (CV) weld process, where the voltage is held constant, in Pulse welding process when the trim is decreased from 1 to 0.85 the weld arc length reduces and in doing so reduces the welding voltage. Similarly, when the trim is increased from 1 to 1.15, the weld arc length increases and therefore increases welding voltage. Accordingly, in Table 4 two numbers in the column for Voltage/Trim are for Pulse welding process and just one number for the CV process. 
The test samples were made from ¼" thick ASTM A572 Grade 50 steel. Two plates of dimensions 10"x3"x1/4" and 10"x2"x1/4" were tag welded at the ends to form a "T" junction as shown in the schematic in Figure 1 . A total of 80 such test samples were made for weld spatter testing to cover 64 test pieces for DOE, and the rest for use in preliminary trial experiments. Fig. 1 . Weld spatter test sample geometry
Initially three spatter collection trays were designed and fabricated from a 1/16" sheet metal. A ¼" diameter steel rod bent in the shape of a shallow U, and welded at the ends of the trays, served as handles for easy placement and handling of spatter collected in the trays. During preliminary trial experiments spatter was collected by placing three trays directly under the welding fixture as shown in Figure 2 . This method of using three trays for spatter collection was discarded for two reasons: a) it did not catch the weld spatter in its entirety, and b) the process was too cumbersome. The improved method of collecting spatter consisted of designing, and fabricating an enclosure in the shape of a five sided cone with a narrow opening at the bottom, and a wide opening at the top. The enclosure was specifically designed to fit the envelope of the test robots, while at the same time not interfering with robot operations. The primary aim was to prevent weld spatter from escaping the catchment area. The enclosure extended all the way from the bottom of the floor to the base of the robot arm. A small spatter collection tray was also designed to fit the narrow opening of the enclosure at the bottom. The spatter collection enclosure with the small tray at the bottom is shown in Figure 3 . All components were designed, welded, and fabricated at the industrial test facility. Anti-spatter was sprayed on trays and pans in order to prevent spatter from sticking to the sides. This also facilitated easier collection and weighing during the experiments. 
Welding Machine: Lincoln Electric Power Wave 455M welding machines were used during sample preparation and test parameter validation. The Power Wave® platform is designed around three principles of process control, which allows for optimized performance and reduced waste: Implement -Optimized arc performance with Waveform Control Technology®, Control -Process standardization with Power Wave® Manager and Weld Sequencer® Software, and Verify -Productivity and quality monitoring with CheckPoint® Production Monitoring.
Welding Robot: A FANUC ARC Mate 100iC Industrial Robot was used in this study. The FANUC ARC Mate Series that are built specifically for Arc welding includes a variety of models with payloads of up to 20 kg. It has an arm reach of up to 2.0m, is suitable for a wide range of arc welding, laser welding, soldering and cutting applications. The robot used had a variety of customized tools to increase its versatility, and to assist in making the weld processes more efficient and profitable.
Robot Programs: In this study, FANUC's specialized robot software products were used that included dedicated functions, simple to use interfaces, and exclusive features to simplify and standardize robot programming. These features enabled the robot to run more effectively during the experimentation, and also allowed program adjustments to arc start and end parameters. A sample of one of the programs manipulations is given below. Manipulating the highlighted program schedule creates adjustments to the arm speed, voltage, and wire feed speed. In this example "{36}" in the program listed below represents the adjustments which are detailed in the program scheduled settings given at the end of the program ImageJ, an open source Java image processing program with tremendous plugin options for extended functionality, was used for vision parameter data collection. ImageJ can read many image formats including TIFF, GIF, JPEG, etc., and display, edit, analyse, process and print images. In this work, ImageJ was used to calculate area and pixel value statistics of user-defined selections, density or grey scale calibration, measurement of distances, and angles to create density histograms and line profile plots.
Audacity is an open source user friendly multi-track audio editor and recorder that can be used in various operating systems. Audacity can be used to record live audio, and convert records into digital recordings, edit sound file formats, cut, copy, splice change the speed or pitch of a recording. During the experiments we used this program to monitor weld sound and convert these to data points for spatter analysis.
Each of the 16 experiments listed in Table 4 were repeated 4 times and the average results used for final analysis. Two special jigs were designed and fabricated, one to hold the test pieces for tack welding and one to hold the tack welded test pieces to conduct the designed experiments in the welding robot cell. The weld cell fixture contained two stops and a magnet to secure the part.
In determining how to collect data, importance was placed on identifying a data collection plan, identifying data sources, and finally the data itself. Key elements of this study included frequency of occurrence, and resultant weight of excess spatter. To that end, multiple responses were measured to ensure full capture of the phenomena with comparative data streams leading to more definitive findings. Measurement focused on weight of the test piece before and after weld, weight of the spatter collected, video recording of the weld process via multiple cameras, and audio recordings of each weld test.
Various methods were employed for collection and analysis of weld spatter data, to express the loss due to spatter in terms of cost. Several materials and tools were used, first to establish a baseline, and subsequently to test and analyse the responses Even before the start of experiments, the current state of weld spatter generation had to be quantified and a baseline established. The process of determining the baseline included quantifying spatter in production. Consequently, a highvolume robot was identified running normal settings and three trays shown in Figure 1 were placed at the base of the welding fixture. These remained for an entire shift after which they were removed, and the trays were emptied, and spatter accumulated in the trays was collected. Subsequently the spatter was transferred into a Ziploc bag and weighed. The costs associated with weld spatter were also explored and summarized. A summary of the standard settings are listed in Table 5 below, and the weight in pounds of spatter collected on each day from the collection trays is shown in Table 6 . The total spatter collected each day is the sum of the amount of spatter collected from the trays and the spatter collected off the floor surrounding the trays because the trays could not cover all the areas where spatter could fall each time. The annual costs of weld spatter were broken down into annual costs per robot cell and annual costs from all robotic welders. The annual costs from each robotic cell include cleaning, scraping, and weld wire loss. The annual costs per robotic cell are shown in Table 7 . The annual costs from all robotic welders include quality issues at the paint line, tool/fixture repair hours, and consumables. The annual cost for all robotic weld cells is also shown in Table 7 . 
A total of 6 test responses were measured during each test listed in Table 4 . The responses measured were: i) Input weight of the weld wire, ii) Weight of the spatter collected, iii) Percent loss of weld wire during welding, iv) Number of weld spatter particles, v) Intensity of sound during welding, and vi) Weld quality Input weight of the weld wire ( ): The actual weight of the weld wire consumed during each spatter collection test was expressed in pounds, and it is calculated as follows. The weight of the input wire consumed in a test (Ww lb) is a function of the diameter of the weld wire consumed in each test (dw inch), wire feed rate used in the test (fw inch/min), the time of weld test (tw min), and the density of the weld wire (rw lb/in 3 ). The volume of the weld wire consumed in a test (Vw in 3 ) was obtained by multiplying the cross-sectional area of the weld wire with wire feed rate (fw in/min), and weld time (tw). Then the weight of the weld wire was obtained by multiplying the volume with the density of the weld wire. The diameter of the wire was measured using a micrometre from which the cross-sectional area was calculated. The feed rate of the weld wire was obtained from the welding machine.
During each test, five video cameras including a GoPro camera was placed in the cell at a strategically located predetermined position to record the entire welding operation. The robot was programmed in such a way that the starting of the robot was the same for each experiment and was within the observation range of the GoPro camera. This ensured that the cameras were synchronized to the process to see spatter development throughout the cycle from start to end. The exact amount of time taken in each test was calculated by importing videos from the GoPro camera paced in the weld cell into a video editing software, and finding the frame in which the weld started and the frame in which the weld ended. The difference in time stamps of the weld start and weld end picture frames was calculated to determine the exact time of the weld in each test. ℎ ℎ . " = ( " , " , " , " ) (1)
Actual weight of the spatter ejected during the welding process (Ws) was the second response. Before each test the spatter collection tray was weighed using a scale correct to a gram, and was placed at the narrow opening of the enclosure at the bottom as shown in Figure 3 . After the welding experiment was complete, the tray was weighed again. The difference in weight of the spatter collection tray before and after the weld, was recorded as the actual weight of the spatter collected in pounds.
Percent loss of weld wire during welding ( % ): Each test specimen was weighed before (Wtsb) and after each experiment (Wtsa). The scale used is shown in Figure 4 . The difference in weight of the sample before and after the test was the exact weight of metal deposited (Wmd). Knowing input weight of the weld wire (Ww), and the weight of the metal deposited in each test (Wmd), the weight of the wire lost in the form of spatter and vaporization (Wwl)is calculated. Once knowing the weight of the wire lost (Wwl) during each test, the percent loss in weight of the wire is calculated.
"2% = S T F U T VW T F X 100 (5) Fig. 4 . Scale for measuring test specimen weight
A GoPro camera and four stationary video cameras were installed in the robotic cell shown in Figure 5 . These cameras were used to monitor welding robots and manual welding processes. During each test videos were recorded and transferred to a video editing software for analysis. Each video from the welding cell was analyzed and screenshots for each part were taken. While welding was performed on both sides of the 10 inch long inverted-T section test specimen shown in Figure 1 , data from only one side of each test piece was processed due to the robotic welding arm covering some of the spatter on the other side, leaving only one side of each test piece to be analyzed. Using the weld on the back side of each test piece to capture spatter optically, screenshots were taken at regular intervals from the videos. Those images from each piece were ran through a particle counting software called ImageJ. The ImageJ is an open source image processing program that was used to count the spatter particles coming off of the weld. These spatter particles were quantified by converting the images to black and white and filtering bit sizes and thresholds within the picture to prevent the weld arc flash from skewing data. Particle count was found to be consistent as long as the still frame images were taken at the same intervals. A screenshot of the ImageJ software navigation tool bar along with still frame of the spatter image imported in ImageJ is shown in Figure 6 . After each image is imported into ImageJ, the image can then be inverted and the dark scale threshold changed in order to get the best focus on the weld spatter itself. The same threshold was used for every image to keep the spatter quantification process consistent. An example of an image that has been inverted and focused by changing the threshold is shown in Figure 7 . As can be seen from Figure 7 , the arc flash from the weld causes reflections off of the robot arm, but ImageJ software allows for this kind of interference to be cropped out. Figure 8 shows the cropped image that is devoid of any extraneous reflections and shows only the region that covers weld spatter. Then this cleaned image is used by ImageJ to count the spatter particles detected. The pixel size that is detected by ImageJ can be changed to prevent the arc flash from skewing the data. By filtering the size, the arc flash is not counted as a particle while all the other smaller particles are. To test the accuracy of the ImageJ software known number of triangles, square, rectangles and circles, of different sizes were drawn with few hundred dark areas. Each time ImageJ provided exactly the same number of the images that were placed in the calibration still image. Having established the accuracy of the ImageJ software, each test listed in Table 4 was repeated four times. During each experiment, four screen shots at regular intervals were taken and the number of particles in each screenshot determined. The average of the four screenshots was taken to reduce variability in measurement. Then taking the average of each of the four repeat experiments, an overall average (Nsp) of each test listed in Table 4 was determined. 
The GoPro camera installed in the robotic welding cell recorded both audio and video information during each test. The audio data was imported from the GoPro camera into the open source audio software called Audacity. In Audacity the stereo data was converted to mono at a sample rate of 48,000 Hz. From each weld five samples each of two second duration were exported into text files. Each test specimen shown in Figure 1 had two welds. Each two second data packet contained 96,000 data points. The text file contained the normalized decibel waveform data, and a decibel scale range from -1 to 1. A value of zero is quietest while -1 or 1 indicates the loudest relative sounds. The exported text file is inserted into Excel where the relative minimum and maximum values were determined. The maximum values are averaged. The larger the average of maximum values, the louder the weld is. A louder weld is an indication of spatter being produced.
Weld quality (Wq): After each experiment, the test samples were allowed to reach the room temperature before evaluating the quality of the weld. The quality of each weld was determined from visual inspection, and rating of the quality on a scale of 1 to 5, where a test specimen of best quality was rated as 1, while a test sample of unacceptable quality was rated as 5. Based on the prior experience and input from subject matter experts in DOE of the weld quality, a rating of 1 or 2 was given to only those that were deemed to be acceptable.
An example of 5 rated welds would include heavy undercuts and porosity as shown in Figure 9a . Test specimens rated 4 had minor undercuts that were still visible to the naked eye as seen in Figure 9b . Test specimens rated 3 are the ones that do not have enough heat to melt the weld wire and produce a very rounded weld bead. A quality rating of 3 is shown in Figure 9c . Test specimens rated 2 are the ones with weld beads that are slightly convex and in need of a little bit more voltage to get the weld to a level one quality. A quality rating of 2 is shown in Figure 9d .
Test specimen rated 1 would be a six millimeter flat to slightly concave weld bead with no undercut or porosity. A quality rating of 1 is shown in Figure 9e . Table 4 was repeated four times, and the average of the four values was taken as the mean response. Six output responses described in Section 4 were measured and the measured responses are given in Table 8 . From the examination of the estimated model coefficient for Signal to Noise ratios in Table 9a , it is clear that the data is symmetric about the mid-point with both positive and negative T-values spread almost evenly on both sides of the zero. The coefficient of determination R 2 is the percentage of variation in the response that is explained by the model. It is a statistical measure of how close the data are to the fitted regression line, and a R 2 value of 93.4% is relatively good for the real data obtained from industry. Examination of F-values in Table 9b shows that the F-value of 6.4 for the Weld Process is the largest in the table, and is nearly twice that of the next largest value of 3.38, which is for voltage. The next largest being 2.54 and 1.73 for stick out and torch travel angle respectively. The ANOVA Table 9b for S/N ratios shows that the weld process, voltage, stick out, and torch travel angle are cause for most of the variation.
From Table 10a for estimated Model Coefficients for Means, it is clear that the T-values are again spread equally around both sides of zero, and the R 2 values of 84.5% is again relatively good, given the fact that it is the real life data obtained from industry. In Table 10b the F-value of 3.31 for weld process is more than double the next largest value of 1.6 for stick out, and more than three times for weld direction.
The response table for signal to noise ratio is shown in Table 11a , and the corresponding response tables for means and standard deviations are shown in Tables 11b and 11c. It is clear from the main effects plot of S/N ratios in Figure  10a , that the voltage and WfsAts are almost linear. The S/N ratio increases with an increase in voltage and decreases with an increase in WfsAts. The response Table 11a for S/N ranks voltage as 1, torch travel angle as 2, weld process as 3, and WfsAs as 4.
The main effects plot for Means in Figure 10b shows a huge difference between weld processes, and insignificant difference between surface conditions. Although, torch travel angle is somewhat significant, it is not clear why the response fluctuates with linear increase in torch travel angle. One reason may be due to the sensitivity of the response to changes in torch travel angle, or the data may be corrupted resulting in a nonlinear change in response due to linear change in angle. As such, this may require further investigation. The corresponding response Table 11b ranks voltage as 1, weld process as 2, WfsAts as 3, and torch travel angle as 4.
The main effects plot for standard deviations given in Figure 10c , and the corresponding response table 11c for standard deviations shows very much the same trends as that of the main effects plot of Means. Figure 10 . Settings were based on parameters with the main effects that resulted in a smaller signal to noise ratio due to our desire to reduce spatter. Two verification tests were developed, and 6 variations of parameters close to the mean were used for optimization testing in search of the sweet spot. A torch travel angle of 15, Wire feed and Arm travel speed of 425, voltage between 26 and 28 volts, average stick out of 0.75, and a clean surface condition were used. There was huge noise variation due to weld process so variations of both CV and Pulse were tested. The results of these tests are detailed in Table 12a . Better quality was observed for tests 3 to 8 with large variations in particle count and nominal variations in noise and weight loss. The only significant variant was weld process so further comparison was done to understand which weld process presented more optimal results. Table 12b shows the comparison of pulse and constant voltage welding at similar wire feed speeds, and voltages. Further tests were done to refine the results and establish final recommendations. The results from the recommendation verification experiments are shown in Table 13a . Table 13b shows a comparison of schedule 12 (current settings) to the three recommended settings which resulted in the lowest particle count and highest quality. The test pieces with the best-looking welds from each set of four experiments were cut into thirds with a band saw. The middle section of each piece was grinded, polished and etched on both sides to view the weld penetration. The penetration shows as triangular darker areas at the welds. Photos for each weld can be seen in Figures 11a to 11f . Table in 13a 6. Discussion
After running the L16 Orthogonal Array, the Minitab analysis showed what parameters impact spatter the most. The most impactful parameter was voltage. At the highest voltage, there was very little spatter produced while the weld quality was reduced, which led to less aggressive voltages being recommended. The second most impactful parameter was torch travel angle. Torch travel angle was a four-level parameter and the torch travel angle of 15 degrees produced the best results. The third most influential parameter was weld process. Pulse welding produced less spatter. The ranking of the remaining parameters was wire feed speed, stick out, weld direction, and surface condition respectively.
Input of weld wire deposit rate: The calculated weight of the wire deposited into the weld was found to have, by nature, a very low variability response due to all the inputs being known constants. The time taken was calculated by importing the video from the camera placed in the weld cell into a video editing software. By finding the frame where the weld started and ended, the time difference was able to be found for each weld. The time taken was then multiplied by the weight per second to find the theoretical weight of the wire that was put into the weld.
Percent loss of weld wire during welding process: The percent loss was based on the difference between the calculated weight and the actual weight of the weld. The percent loss correlated pretty well to most of the other responses, but did not correlate directly with weld quality. A lower percent loss did not necessarily lead to better weld quality. If the voltage was a little too high for the wire feed speed to where it melts too much of the metal but not to the point where it is vaporizing the metal, the result led to undercut on the top plate.
Spatter collection: The spatter collected from the tray placed in the welding robot varied between certain trials. While the maximum weight of spatter collected from the tray after a trial was 0.0184 lb., the minimum weight was only 0.0002 lb. and the average was 0.00438 lbs. The spatter produced in the designed experiment trials was not as significant as the 0.01387 lb. per piece average of the baseline. The primary reason lies in the nature of the T-joint used for the experiments which was an ideal reach for the robot arm and the distance from the nozzle to the work piece remained constant. Despite this, the experiment loss variation and multiplication effect validate that a comparison between baseline and experiment is valid. It can also be extrapolated that more complex production welds will present more spatter and more opportunity for spatter reduction in a real production environment.
Optical evaluation: There is a great deal of variability with the nature of spatter collection itself, but by taking multiple images of the same weld and averaging the particle count, variability was decreased. It was decreased again by taking the average of all the trials that contain parts ran at the same parameters. The results showed some correlation between other responses that were measured. Also, spatter particle count correlated with what was expected. For example, running parts at low wire feed speeds with high voltages resulted in low spatter counts because the wire itself was vaporizing and not being ejected as spatter. This was the case in other experiments in this investigation, although the lower spatter count experiments did not always result in high quality welds. As the best parameter sets were selected, the goal was kept on reducing spatter count while also maintaining weld quality.
Audio evaluation: The audio data had variability across different experiments. The variability proved there was something to be learned from the audio data. The average value of the maximums showed the relative loudness of the 2 second weld clip. The louder welds produced more spatter. The loudest experiment was 12. Trial 12 produced the most spatter according to optical analysis. There was a high level of confidence that the average of the maximums correlated to the spatter generation.
Weld Quality: The quality in welds varied considerably between test pieces. The toe-to-toe lengths in comparison to the profile of the welds were mostly acceptable and porosity was not found on many pieces. The pieces that had porosity were ones having an oily surface condition. Also, no piece had any cracks along the welds. However, several of the test pieces had severe undercut along much of their lengths. Any sets of parameters that produced undercut were not considered for the team's recommendations as it was considered a huge quality issue.
Weld Spatter Baseline: The weld spatter baseline provided a good baseline for understanding weld spatter loss and resultant costs on weld robots. This cost is different from manual weld operations due to the extensive use of antispatter spray in manual vs robotic weld operations which costs an additional $11,781 annually. This cost was not factored in this study. Costs factored include time spent scraping and cleaning the robot cells. These times are based on operator surveys and are deemed as necessary to maintain the standards of the facility. Spatter reduction will have positive impact on the overall cost.
Optimization testing: Experiments 1 and 2 were repeat experiments meant to verify the results from the screening experiments as being accurate and repeatable. Those two experiments were run at the same settings as experiments 5 and 6 from the L16 Designed Experiment. After analysing and comparing the responses for the identical experiments, they were found to be very similar, verifying the responses for their repeatability. The other six experiments, 3 to 6 in Table  12a , were conducted to optimize parameter settings. It was found that increasing the voltage by small increments and utilizing pulsed MIG spray transfer welding, spatter generation was reduced, and weld quality was maintained. Switching to pulse did impact the voltage, and the voltage did not remain constant. A trim was set at a certain value that corresponds to arc length. The trim relationship to arc length and voltage differed from machine to machine. A trim value of 1 was the recommended voltage for a given wire feed speed by the welding machine's original equipment manufacturer. The goal was to get the voltage and trim as high as possible without creating undercut on the welded piece. The results from the optimization tests conducted were analysed to determine recommended settings for our industry partner.
More experiments were conducted to verify the three recommended settings at different wire feed speeds. Although the optimization experiments ran pulsed MIG with a trim of 1.1 had no quality problems, it was noticed that when trying to set three recommended parameter settings on a different day, the voltages generated were a little higher than desired. It was determined that a trim value of 1.05 would be the best. Although spatter is not minimized as much as possible with this, quality cannot be sacrificed as a result. Essentially a trim value of 1.05 could be transferred to all machines without having to fret about compromised weld quality. Using a trim greater than 1.0 still leads to spatter reduction. Leveraging pulsed MIG welding versus the traditional constant voltage welding is the cause of the improvement. The verification trials included tests in both constant voltage and pulse. The reasoning behind the duplication of similar wire feed speeds and voltages was to verify that pulsed MIG did indeed drastically impact the spatter generation. The wire feed speeds of 425 and 450 inches per minute did not appear to be the best settings to be disseminated across all possible welds. However, the wire feed speed of 475 inches per minute was recommended for implementation. It was further determined that whenever a weld design is not completed enough to create a situation requiring the welding robot to reach difficult to reach places, pulse welding is a better fit than constant voltage. When welds require extreme angles or stick out, constant voltage is the better selection. The reason for constant voltage being better in these challenging welding locations is because the trim value only regulates the arc length. When arc length has consistent variation, the pulsed current jumps to extremely high voltages leading to undercut.
Upon inspecting the etched parts from the optimization tests, it was determined that all of the weld penetrations for the test pieces were sufficient; the plates were melded together properly for a strong weld. This was clearly evident by the extent to which the weld penetration extended in the weld joint area on both sides of each test piece shown in Figures 11.1 to 11.6. While the penetrations varied slightly between pieces and sides, all of them displayed high quality.
The cost savings for the project were based off the 3 best responses from the experiments. The three responses are percent loss of weld wire, audio out of the average of the relative maximums, and spatter count from optical analysis. The responses were given weights to find the weighted average of the percent improvement from the current most used schedule. The weight for percent loss of weld wire was 30%. The weight for audio output was 10%. The weight for particle count was 60%. Weights were assigned based on the accuracy and consistency of the response throughout all experiments. Once weights were assigned to the response, a weighting factor was also assigned to each recommendation. The weighting factors on the recommendations were assigned based on DOE results at a given wire feed speed. The 425, 450 and 475 inches per minute of wire feed speed were given 45%, 30%, and 25% weights respectively. The 475 inches per minute schedule corresponds to the test industry sites current schedule 12. Table 14 shows the computation of the percent reduction from each process improvement as well as overall percentage savings and cost savings form each recommendation. Columns 1 and 2 of Table 14 lists the three recommendations for implementation and the corresponding weighting factor for each recommendation. Columns 3 to 5 lists the percentage savings from loss of weld wire, reduction in noise and number of spatter particles A full display of these percent improvements and results of the trials conducted are shown in Table 13b . 
In conclusion, the L16 Taguchi Orthogonal Array was useful in identifying parameters influential to spatter production in the welding process. The test also gave the optimal set of parameters where virtually no spatter was being produced. The issue with utilizing the mean value table to determine the optimal setting was that quality would have been violated. A weld with a wire feed speed of 325 inches per minute and a voltage of 30 volts had massive undercut in the designed experiment, which were the parameters the mean value table indicated to leverage.
The ImageJ and Audacity software provided insight into the spatter generation for the designed experiments. The ImageJ software was used for the optical analysis. The optical analysis served as the best response for spatter quantification. It was the most effective because the response was not affected by any outside noise. The average of 10 images was a sufficient sample size to discern the difference between averages and experiments. Audacity was used for audio analysis. Leveraging audio data was a challenge due to the size of the samples. However, after analysis, Audacity could differentiate different L16 experiments. If the team had access to computers with greater processing power, more information could have been derived from the audio data.
The primary conclusion is that maximizing voltage, without causing undercut, minimizes spatter. With the given constraints of certain parameters (weld wire, gas mixture), the project revolved around finding a sweet spot where quality was not compromised but rather improved along with the primary goal of reduction in spatter. It is recommended to increase voltage to match drastically increased wire feed speeds that stemmed from previous designed experiments. Pulsed MIG welding was found to produce less spatter by 9.02% or approximately 9%. Pulse welding is recommended to be used when applicable. The determination of 9% is explained in the justification of cost savings in Table 14 . The 9% translates to an annual cost savings of $6,490 for the industrial facility, not including the significant impact it has on the overall quality of the welding operations resulting in a robust product. In addition, an uncalculated resultant cost savings was the energy consumption difference between pulsed MIG and traditional constant voltage. Pulse used a low background voltage and high peak current so that average current was less than using constant voltage. The energy consumption is a subject of ongoing future work that is not reported in this manuscript. An interesting conclusion related to the cleanliness of the parts was that parts contaminated with an oily substance do not suffer from additional spatter generation.
Furthermore, it was found that practically it was impossible to eliminate weld spatter entirely while still maintaining weld quality with the constraints of high wire feed speeds, constant gas mixture, and constant wire type. Nevertheless, optimization of the salient weld parameters has been completed, and the recommended schedules that were implemented in the industrial production are shown in Table  13b , along with the current highest used schedule 12 listed in the first column of table 13b.