Components search vs Paired comparison technique

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    It is a commmon knowledge that Paired comparison is used when component-swapping is not possible.
    I am little confused as to how I should investigate for variables like: Dia., Length etc. – without disassembly?
    This is typical of press fitted components (eg.: Crank shaft assembly – where I need to study the effect of Connecting rod Bore dia., Crank pin dia. etc.)
    Please help me with suggestions


    Michael Schlueter

    Hi Sasun,
    I do understand that you want to evaluate the impact of dimensions on the crank shafts function. The difficulty is that you have to break your component afterwards to obtain the input data (dimensions) – which may be impossible to do at all. Is that correct?
    If so I would consider the following possibilities.

    Do paired comparision, i.e. evaluate the function, select 6-13 best-of-best and worst-of-worst components (same amount for both groups, so total number is > 18 .. 39). Break the components, measure the parameters (hope they are not affected by this disassembly). Check for significant parameters, e.g. by applying Tuckey test (which basically is a reordering of results by your parameter values). Cf. Bothe’s book “World Class Quality”.
    Basically proceed as in #1, but replace measurement of dimensions by detection of dimensions, best within the assembled component. Frequently it is difficult to obtain precise measurement results, but detection can be done easily, whether or not a parameter has a certain value or is within a certain range. E.g. measuring cracks in glas can be very difficult to do, while detecting cracks via sound is pretty easy (pling vs. plong sound). – Utilize physical effects (noise, sound, force, electrical resitance, resonance, low temperature behavior, x-rays, weight to name a few), color marking etc. for detection purposes.
    Best, reverse your approach: do DOE, or more precisely, perform Taguchi`s Parameter Design: change dimensions intentionally and build different dedicated crank shafts. Use orthogonal arrays (or full factorial if you prefer those) to assign the changes in dimension (design parameters) and create a balanced experimental plan (a recipe for different crank shafts, so to speak). Evaluate your components performance under various conditions of use (noise parameters, mimicking variability) for each design variant. Calculate main effects in stability and mean. Optimize for stability, first. Optimize for on-target performance, second. Verify optimal settings (i.e. decisions on dimensions). – You do not need to disassemble later on. As an aside, you’ll improve your components performance by design: your design will survive various sources of variability – that’s robustness.
    Hope this helps you along. I invite you to contact me, if I can help you in more detail ([email protected]).
    Michael Schlueter
    (A western member of the Quality Engineering Society, Tokyo)


    Chip Hewette

    I suggest that a practical review of the engine crankshaft tolerances be performed first.  It is not always possible to have an operating engine with all combinations of part tolerances.  Putting component tolerances in any orthogonal array can be a disaster, in that the engine would self-destruct if certain part dimensions combine.
    It makes more sense to me to study the dimensional relationships than to study the part dimensions.  If a given range of rod journal to crank journal clearance is acceptable for noise, performance, and durability, part dimensions and tolerances can be calculated to achieve the range.
    By studying the functional relationships, you could also study the effect of having one or more “bad” clearances on a multi-cylinder engine.
    If disassembly is not possible, you may need to consider bench tests of the components before investing in the engine build.

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