**8. Conclusion/summary**

60 Some Critical Issues for Injection Molding

 The yellow line represents target value for the responses as specified in the Optimizer. The red lines are the specification limits for each response as specified in Optimizer. The faded green region represents the probability of a prediction for a random distribution of factor settings in the given ranges (low-optimum-high), the design space. The black T-bars represent the space in which one factor can be varied while freezing

The histograms of Fig. 37 represent the response targets based on the regression model at an optimal factor setting including the factor uncertainty variation calculated by the MCS.

Assuming the predictive model is of good quality, the responses and its degree of achievement can be evaluated against priorities of the projects' definitions. Within the most experimental setups, responses targets are optimistically defined and with a high degree of safety. Sometimes not all of these targets can be fully achieved. Therefore, and as described in the optimization, responses can be weighted again according to their priorities and their targets, in order to find a sufficient compromise. This compromise could mend not only the factors and responses that need to be adjusted but could also mean that conditions that act as disturbances need to be compensated. Potential disturbance factors are, for instance: Relative air humidity, temperature, water content in raw materials, using different machines

Also predictive models are only as good as their data. Even if factors are sufficiently enough arranged to describe the target functions, there are still a lot of things which could impair the prediction model such as bad pruning of the model terms, bad distributions or deviating

experiments, missing factors, bad measurement *(calibrated)* equipment.

the other factors and still keeping the calculated response fulfillment.

Fig. 37. Predictive design space histogram 5.

**7. Success with restrictions** 

or bad machine calibration.

Chart legend of Modde-predicted response profile

After reading the chapter, the readers should now have a good understanding how far the combined methods could help them to achieve the predefined requirements. In addition, they should also be sensitized to the fact that non-structured approaches are weak and timeconsuming. It is also important to understand that while the design of experiments "DoE" does not necessarily lead to good results or capable processes, they can help to describe and document the potential of a process. Even if the targets could not be achieved, it is still possible to derive useful, cost-effective and robust knowledge with this structured approach in order to identify and assess possible disturbance factors or possible process constraints. This can provide beneficial clues for fine-tuning the factors and conditions in order to ensure and optimize process capability and success.

By following this good "DoE" practice recommendation, the iterative difficulties in finding the fulcrum or lever at the beginning of an optimization process first hand can now be reduced--- if not eliminated. And by following this consistently structured approach, the right things can be done in the right way with the right tools. Thus, Pareto's law can be intelligently leveraged, and finally, the optimization team can operate in the most efficient and effective fashion.

### **9. References**

AB, U. (2009). Software & Help File Modde 9.0. ISBN-10 91-973730-4-4, Sweden.

From Wikipedia, t. f. (n.d.). *Wikipedia*. Retrieved 09 15, 2011, from:

http://en.wikipedia.org


**Part 3** 

**Powder Injection Molding** 

Rauwendaal, C. (n.d.). *SPC Statistical Process Control in Injection Molding end Extrusion.* München 2008, ISBN 978-3-446-40785-5: HanserVerlag.

Vester, F. (2002). *Die Kunst vernetzt zu denken.* dtv, ISBN 3-423-33077-5: München, Germany.

**Part 3** 

**Powder Injection Molding** 

62 Some Critical Issues for Injection Molding

Rauwendaal, C. (n.d.). *SPC Statistical Process Control in Injection Molding end Extrusion.*

Vester, F. (2002). *Die Kunst vernetzt zu denken.* dtv, ISBN 3-423-33077-5: München, Germany.

München 2008, ISBN 978-3-446-40785-5: HanserVerlag.

**3** 

 *Slovenia* 

**Powder Injection Molding** 

 **of Metal and Ceramic Parts** 

Joamín González-Gutiérrez, Gustavo Beulke Stringari and Igor Emri

Powder injection molding (PIM) is a technology for manufacturing complex, precision, netshape components from either metal or ceramic powder. The potential of PIM lies in its ability to combine the design flexibility of plastic injection molding and the nearly unlimited choice of material offered by powder metallurgy, making it possible to combine multiple parts into a single one (Hausnerová, 2011). Furthermore, PIM overcomes the dimensional and productivity limits of isostatic pressing and slip casting, the defects and tolerance limitations of investment casting, the mechanical strength of die-cast parts, and the shape

Due to the demand of high performance materials and the miniaturization of complex components in various fields, PIM market has exceeded the \$ 1 billion mark in 2007, becoming approximately six times larger than 15 years before (German, 2008). This impressive growth rate is not expected to slow down in the next few years, as a recent report from Global Industry Analysts announced that together, world metal and ceramic PIM market is forecast to reach \$ 3.7 billion by the year 2017 (Global Industry Analysts [GIA], 2011). Metal powder injection molding (MIM) is still considered the largest segment of this market, accounting for more than 70% of global output. Although PIM is globally widespread, Europe and Asia-Pacific account for a major share of MIM segment, while USA

In Europe, the MIM production is dominated by automotive applications and the so called consumer market (which includes watches and eyeglasses), while the North American production is mainly applied to the medical/healthcare field. On the other hand, the Asian production, considered the largest one, is dominated by consumer electronics and information technology applications. The consumer electronics market is, indeed, one of the drivers behind MIM, whose growth is largely taking place in Asia, specifically in Taiwan, Malaysia, Thailand, China, Singapore and South Korea. Another growth factor is the expansion of medical component production also in Asia, as a larger population gains

A recent increase in MIM sales has generated a need for new equipment, with a simultaneous investment in research and development. Typically, leader companies invest an average of 10.5% of sales in the combination of capital expansion and research. Besides

is still the largest market for Ceramic Injection Molding (CIM) (GIA, 2011).

limitation of traditional powder compacts (Tandon, 2008).

access to improved health care (German, 2008).

**1. Introduction** 

*Center for Experimental Mechanics, University of Ljubljana, Ljubljana,* 
