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Statistische Tolerierung

Statistical toleration

Increase production reliability - reduce rejects.

One of the central tasks of preventive reliability engineering is the reduction of production waste. This idea is based on the realization that incompetent manufacturing processes lead to large variations in product characteristics and quality features.

High initial variations – for example in the capacitance of capacitors – often also cause large fluctuations in service life. If the rejects are not detected by end-of-line testing, this results in high initial field failure rates. By using statistical tolerancing methods, scrap rates can be significantly reduced – often combined with a noticeable reduction in costs.

Principles of statistical tolerancing

A key principle is to take the actual machine and process capabilities into account when defining tolerances. This ensures that manufacturing processes are optimally utilized from a quality perspective without overtaxing them.

A second principle is the system approach: the definition of tolerances for individual parts or features is based on the entire chain of effects – right up to the quality feature that the customer perceives. Only this holistic view enables overall optimization. If it is neglected, developers tend to specify tolerances that are too tight for their area (“gold-plated components”) in order to supposedly ensure system quality – but this usually results in unnecessary costs.

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In combination with other methods

Statistical tolerancing is closely linked to Robust Design and Design of Experiments (DoE). While DoE identifies the relevant influencing variables, statistical tolerancing ensures that the design parameters derived from this are implemented economically and reliably. In combination with Robust Design, this creates an efficient approach to preventive quality and cost optimization.
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