# Quality Engineering(QE) 6

I talked that we should try to evaluate dynamic characteristics for Quality Engineering(QE) as much as possible. We can analyze that the input and output are linear, but what if it is non-linear? How do you think about it? Looking at the QE reference book, it is stated that in molding, if the mold shape is ideal, it is evaluated whether it has been “transferability” well to the molded product. I tried about two years ago to see if the molding conditions can really be set with "transferability".

Please refer to the material → QE6

p.1:  A small piece called "dumbbell" is used when conducting a tensile strength test on polymers. We consider to set the injection molding conditions of this dumbbell by using QE . First, the objective function is "Molding dimension is the intended dimension", and the generic function to be evaluated is "Molding dimension is proportional to mold dimension (transferability)". Ideally, "die size = molded product size". But this will be a problem later ...

p.2: Today's main task "How should we do if the output is non-linear or transferable with respect to the input signal?" If the output m is non-linear with respect to the input signal M, as in the upper left, we rearrange the output m so that the smaller one is on the left and the larger one on the right. (see the lower right Fig.) This rearranged value is the input signal m. In the case that output is same to this input signal mk, we get result a straight line N0 with a slope of one. This is ideal condition. I haven't talked about error factors yet, but when we set parameters that we can't control as error factors and experiment, it becomes like N1 or N2 line. We experiment the conditions for N1 and N2 to approach N0 by orthogonal table experiments. This method is called the "standardized SN ratio method". In this method, we do not treat sensitivity. We should see how to reduce the variation.

p.3: Transferability is the same idea as p.2. we set the signal factor M to the ideal mold size. Plot the smaller-sized one on the left and the larger one on the right. The vertical axis is the dimension y of the corresponding position on the molded product. The error factor N1 is the second shot after the start of molding, and N2 is the 1000th shot after the start of molding. Since the mold was not warm enough at the start of molding, it was assumed that the dimensions would be small and if molding 1000 shots, the resin would enter the mold too much and burrs would appear. we set target of the molding conditions to achieve the ideal dimensions even after the start of molding or after long-run molding. Please set this error factor freely.

p.4: We evaluation molding parameters and error factors N1 and N2 in an orthogonal table, measure the sample dimensions, and then plot.

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