An dieser Stelle möchte ich gerne auf einen Artikel hinweisen.

Der Artikel beschreibt die Problemformulierung und Abarbeitung im Team mit strukturierten Methoden.

Exemplarisch wird der Prozess an einem Spritzguss Optimierungsproblem durchgängig erklärt.

Er weist auf Stolpersteine hin, die der Problemlösung/ bzw. im Projekt-Setup auftreten und gibt wertvolle Tipps, wie diese im Team abgearbeitet werden können.

Neben der sauberen beschreibbaren Projektdefinition und Prioritätenzuordnung (Funktion, Budget, Ressourcen, Time-Line) wird auch auf die Diskussion von __nicht__ quantifizierbaren Faktoren mit Hilfe des „Consideo Modelers“ eingegangen.

Nach der Definition geht der Artikel fließend in die Versuchsplanung und Robustheitsprüfung über. Die Vorgehensweise ist stark angelehnt an die aktuellen Konzepte "Quality by Design" , "Design for Six Sigma" und der Diskussion um die Prozessfähigkeitsabsicherung "Design Space Estimation".

Über Feedback freue ich mich!

**1. Introduction **

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This chapter is written to give developers and machine operators a better idea how robust processes can be installed or their processes reviewed and optimized. The clear structure

moves from basic introduction to in-depth application of the methods and tools, thus guiding readers through these processes. The article will mainly be organized like my article “Effective Run-In of an Injection Molding Process.”

Since the publication of the article “Effective Run-In of an Injection Molding Process,” I have noticed that both the start phase of an optimization process and the end phase (“verification / validation”) are the most critical parts. Due to this problem, I decided to extend the upcoming article with the following chapters. Increasingly, “Processes Capability” is a necessary basis for accomplishing design transfer with the customer on a valid foundation. Also “Quality by Design” and “Design Space Estimations” are no longer foreign words within the injection molding business. Especially, the medical and automotive business call for process validation.

The new article will, therefore, be divided into the following chapters:

- Familiarization
- Screening
- Optimization
- Robustness
- Validation
- Summary

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**3. Familiarization **

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This part will help process managers move through the set-up or optimization process. Most of the students who joined, for instance, a “Process Capability Statistics”- or a “Design of Experiments” course have difficulties finding the fulcrum or lever to complete the first steps. Consequently, they often just invest in “trial and error methods” to get their process to work. Also common paradigms like “change one parameter at a time” won’t help to accelerate optimization or enable the improvement team to map the whole process, including interactions or non- linear behaviours. Therefore, this chapter will outline tools to collect the main process factors, identify the disturbance factors and some more special tools to interpret the impact of these on the process. It will start with some “very easy to use” tools like “pair wise comparison”, “the 5 W-method” or “the analytical hierarchical process.” Moreover, I will outline these tools to extract knowledge from the „Problem Solving Discussion” by using the Grid and Fishbone diagrams, and also recommended FMEA methods. Another focus will be to interpret existing data with statistical software.

After this chapter, readers should know how to use a set of tools to extract factors and rank them according to Pareto. // à the chapter will be more than a summary of tools but not a tutorial.

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**4. Screening **

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The challenge for machine operators is to do get the run-in process done as quickly as possible. This means a minimum number of experiments with a maximum ability to describe cause and effect. The operators should be sensitized to the fact that small adjustments in the setup of the machine can have a great impact on the quality of the injection molding parts. Therefore, a well-structured approach and high quality data are needed. To reach these goals, the method of “Design of Experiments” will be introduced. The aim is not to provide readers with mathematical details but introduce them to a summary of process tools which could help them install their processes in a robust way.

In this part, the readers will be told how to move from factors to “Experiment Tables” and guided to give their factors a certain range which defines the operating space in the light of variability. To do this, different experiment design concepts and their consequences will be discussed. At the end of this part, the beneficial use of “Design of Experiments”- methods will be outlined

**5. Optimization **

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In this part, operators learn what it means to connect factors with responses in a model. They will be told how to distinguish good from bad models. It is very common for the first models to widely diverge from good models. Some common causes of bad models will be discussed and also how to handle these causes. In some cases, the objective cannot be described in a linear way. This chapter helps to determine if this is the case and how to enhance the design to describe non-linear behaviour with RSM Designs or transformations.

The crucial process description will be delivered by generated contour plots. These plots will be displayed and discussed, including their statistical quality numbers. After this, I will highlight special tools to calculate potential optimal setups, or, should the situation arise, how to describe that, for instance, it is impossible to reach the pre-defined goals. Also, there will be options to calculate compromises outside the factor settings based on multi-linear regression models and the use of simplex algorithms.

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**6. Robustness**

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The Robustness Chapter will help to check the calculated performance level of the model versus the pre-defined process requirements. Therefore, the use of verifications runs will be explained. Also extrapolation and interpolation will be discussed. In the summary, the four limiting cases of robustness designs will be treated.

**7. Process Capability **

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Finally part five will reflect the results within light of process capability. In this chapter, tools will be presented to calculate “process capability,” “defects per million” or % rejects. For this reason, the concept of the “Monte Carlo algorithm” will be explained. In the next step, I will summarize how the MC algorithm can be used to predict the process quality on basis of the model and the estimation of the factor uncertainty.

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**8. Summary part five **

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After reading the chapter, the readers should have a good understanding how far the combined methods could help them to achieve the pre-defined requirements. In addition, they should also be sensitized to the fact that and extent to which non-structured approaches can be weak and time-consuming. At the end, it is also important for me to sum up the limitations of this method.