Process Engineering as Good as Gold

Metalor Technologies improves the quality of its silver powder

Published: Monday, January 3, 2011 - 16:12

(Minitab: State College, PA) -- Swiss-based Metalor Technologies, a global leader in precious metals and advanced materials, is a supplier to electronics companies and manufacturers of medical and electrical equipment.

Metalor’s skill in creating reliable technology has earned the company a preferred-vendor status and a global reputation for excellence. In fact, well beyond the benefits to its own interests, Metalor’s expertise has fostered the creation of new market segments for many of its partners.

In its quest for innovative solutions, Metalor relies on Minitab Statistical Software for help in achieving its process engineering goals.

The challenge

Among Metalor’s products is a high-purity silver powder that is used in fabricating a variety of microelectronic products that range from solar-cell metalization on silicon wafers to membrane touch-switches on flexible plastic. Two properties of the powder—density and surface area—are critical to its quality and performance in Metalor’s customers’ processes. However, these two properties are difficult to predict or control in production. Using the design of experiment (DOE) tools in Minitab, as well as some DOE best practices of its own, Metalor set out to determine how density and surface area were affected by three key process inputs: reaction temperature, ammonium level, and stir rate (see figure 1). The ultimate goal was to improve the quality of their silver powder.

Figure 1: This main-effects plot for density clearly shows that two of the three factors Metalor tested had a stronger effect on the powder’s density.

How Minitab helped

Once Metalor identified the three key factors in its process, it analyzed them to determine their effect on the silver powder products. A full factorial experiment in Minitab—i.e., using only one high and one low setting for each input—let Metalor efficiently evaluate the effect of each input, as well as the interaction effects between these inputs, on the two output variables of interest. Minitab’s power and sample-size calculations indicated that Metalor needed to replicate the full factorial to achieve the statistical power needed to detect the effects important to the process.

The analysis resulted in two equations that were used to generate an overlaid contour plot showing both responses as a function of the process conditions (see figure 2). The plot helped Metalor adjust its process to meet customer specifications for both the density and surface area of its powder. Once the new process settings were implemented, Minitab’s control charts clearly showed the sustained benefits of the improved process (see figure 3).

Figure 2: Minitab’s DOE modeling functionality provided an overlaid contour plot that was used to find the optimum process conditions needed to meet the specifications of both responses.

Figure 3: Minitab control charts demonstrate the dramatic effect of implementing the optimal process conditions determined by the experiment.

Results

Silver is expensive, so reduced experimentation was necessary to control costs. Nonetheless, Minitab helped Metalor find the solution that decreased variation in its process by 50 percent and improved the quality of its silver powder. The solution was implemented, and the process monitored over time. Minitab control charts illustrated the significant decrease in process variation that led to a higher-quality powder that met customer specifications. In addition, these improvements reduced rejected batches by 75 percent and made Metalor’s silver powder production more efficient and cost-effective.

About The Author

Minitab LLC

Minitab Inc. develops statistical analysis and process improvement software for academic and commercial users. Minitab Statistical Software, a data analysis tool for businesses, has been used to implement virtually every major Six Sigma quality improvement initiative, and to teach statistics in more than 4,000 colleges and universities worldwide. Quality Companion is used to plan and execute Six Sigma projects. Use the subscription-based online learning course, Quality Trainer, to learn or refresh your knowledge and to access quality statistics expertise anytime. Interactive lessons based on real problems make concepts easy to retain.