Carlos Alberto Hernández ZavalaAssembling Technique Based on the Statistical Feed-Forward Control Model for Low Precision Manufacturing Processes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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ISBN: | 978-3-8440-2000-7 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Reihe: | Schriftenreihe des Instituts für Produktionsmesstechnik Herausgeber: Prof. Dr.-Ing. Rainer Tutsch Braunschweig | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Band: | 8 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Schlagwörter: | Assembling Technique; High Variation Processes; Statistical Feed-Forward Control Model; Statistical Dynamic Specifications Method; Dynamic Tolerances | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Publikationsart: | Dissertation | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Sprache: | Englisch | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Seiten: | 264 Seiten | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abbildungen: | 135 Abbildungen | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Gewicht: | 393 g | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Format: | 21 x 14,8 cm | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Bindung: | Paperback | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Preis: | 49,80 € | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Erscheinungsdatum: | Juli 2013 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Kaufen: | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
DOI: | 10.2370/9783844020007 (Online-Gesamtdokument) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Zusammenfassung: | The assembling of components coming from manufacturing processes characterized by high dimensional variation has been traditionally approached from two different perspectives: selective and adaptive assembling. Most recent techniques, however, usually adopt a combination of these approaches. A disadvantage of selective techniques is the need of inspecting and classifying all the component items that have to be assembled. Adaptive assembling techniques usually do not use all the information available and only take into account the last inspected sample to estimate the necessary adjustments. None of these approaches consider the nature of the variation as a superposition of different variation forms that evolve independently over time.
This thesis embraces the challenge of developing an assembling technique to deal with the problem of producing assemblies that have low dimensional variation by means of mating components that have high dimensional variation. The main objectives of the proposed model are the reduction of the resulting variation, the reduction of the scrap levels and the improvement of the process capability indices. The innovative approach proposes the dynamic management of specifications, target and tolerance, using the Statistical Dynamic Specifications Method (SDSM) and the assembling of complementing groups using the Statistical Feed-Forward Control Model (SFFCM). While SDSM can be seen as a statistical tool that helps determine the right specification adjustments using reduced samples taken from groups of items produced consecutively during a short-time interval; SFFCM can be seen as a monitoring tool that helps counter the effect of an eventual “detectable” long-term component present in the variation. With the help of proprietary software, the production of lots of one thousand assemblies made of two components coming from non-capable processes (c p<1.33) characterized for producing items that present high dimensional variation was simulated. Large sets of experiments were designed and simulated to quantify the individual and combined influence of different factors in the assembling process. For the analyzed conditions, in comparison to a fully randomized assembling, simulation results revealed an average reduction by 89% of the mean shift, an average reduction by 14% of the standard deviation, an average improvement of the actual capability index of the assembling process by 16%, an average improvement of the potential capability index of the assembling process by 101% and an average reduction of the assemblies out of tolerance by 100%. In conclusion, the proposed SFFCM-based assembling technique, a combination of the adaptive and the selective approach with emphasis in the inspection optimization, effectively helped achieve the mayor objectives of this thesis: reduce the process variation, reduce the scrap level and improve the process capability indices. In few words, it was possible to end up with low variation assemblies made of high variation components. Seen from a different perspective, a capable process (c p>1.33) was obtained by means of combining two non-capable subprocesses. |