Computers & Software
“One of the most important tests is the diameter, so we needed first to understand whether those four factors affected diameter,” Quirós explains. By performing multiple regression analysis on these
factors and tyre diameter data, the team discovered the most important factors were angle and weight.
without wasting time and resources.” He selected a two-level factorial design that would let him assess high and low settings for each of the key process input variables with only 16 runs. Before collecting their data, the team performed a
measurement systems analysis with Minitab to ensure they were collecting good data. They also assessed the baseline capability of the ribbon-tread process using Minitab’s Process Capability Sixpack. The results revealed that while the process was stable, the capability of the process to meet certification standards was poor. Now they were ready to produce their 16 experimental tyres, and put them through the certification tests. When Quirós and his team analysed the experimental data
in Minitab, the results showed that all four factors, and the interactions between them, had a significant effect on plunger test results. This was great information, but using it to derive a precise model would be difficult without a tool that could take the experimental results and let the team forecast how different variable settings would affect the tyre’s performance. Fortunately, Minitab’s Response Optimizer tool does just
Fig. 2. Minitab’s contour plot made it easy for Quirós and his team to explore the relationship between their ribbon-tread variables and the diameter of the finished tyres.
that, making it easy for Quirós to fine-tune the process settings to produce the best results. “Using the optimiser helped us attain and surpass our goal for the plunger test,” he says. “We used it to create our predictive model and define our optimal input settings. Then we gathered more data at these settings and used Minitab to perform multiple regression analysis and validate the factor values.”
Results
Based on the results of those analyses, the team redesigned how they used ribbon-tread to make light truck tyres. Before new tires were made, the plant managers would review the last specifications. Previous and proposed values for angle and drum width would be evaluated in a spreadsheet programmed with equations from DOE. If they were acceptable, data about the other two factors in the process would be added. The new model was tried on a tire size that had proven
particularly challenging in the past. The team used the model to determine effective factor settings, then made some tyres with the new process settings. To validate the results, they used the Minitab Assistant’s Capability Analysis tool. They also created a before-and-after control chart, which revealed how dramatically the new model improved the tyre’s performance in the plunger test. “We achieved our results,” Quirós says. “Whenever we
Fig. 3. To validate their results, the project team used the Minitab Assistant’s Capability Analysis tool. The Assistant’s output makes it simple to communicate the results of an analysis to all stakeholders, even those with little statistical experience.
“So in our model, those input values had a constraint, and we were able to generate an equation to set those input values appropriately.” Now Quirós used Minitab’s Design of Experiment tools
to quickly gather the data he needed to develop his model. “Because testing tyres is a destructive process, it’s very expensive to set up experimental runs,” he notes. “But DOE lets us study multiple variables simultaneously using the least number of runs, so we get enough data for reliable results
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changed the level of a parameter, and ran the tests again, the average was very close to what the model predicted. We’re now exceeding the target for the plunger test.” Use of the new model soon was expanded to other tyre
sizes, too. The model was next applied to a different size of light-truck tyre, one that had not passed certification with ribbon tread before. This time, the process was qualified after the first trial. Since then, Quirós and his team have also successfully applied the model to tires used in agriculture. The success of the project has increased production at the
Costa Rica facility, saving hundreds of thousands of dollars and resulting in more than $2 million in opportunity costs. ●
For more information ✔ at
www.engineerlive.com/asia
Eston Martz is with Minitab Inc., State College, PA, USA.
www.minitab.com
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