KALİTE MÜHENDİSLİĞİ PROBLEMLERİ İÇİN ÖNERİLEN BİR YANIT YÜZEYİ TABANLI SAĞLAM TASARIM MODELİ

Sağlam tasarım modellerinin amacı değişkenliği mümkün olduğu kadar azaltmaktır. İstenen hedef değer ile işlem ortalaması arasındaki fark olarak tanımlanan işlem yanlılığı, kalite mühendisliği problemleri için önemli bir husustur. Ek olarak, farklı değişkenlik ölçümlerinin seçimi bir yanıt değişkeni için en uygun çalışma koşullarını da değiştirebilir. Bu nedenle, bu makalenin üç amaçlıdır. Birincisi, yanıt modelinin bir başka görünümü işlem yanlılığını en aza indirirken girdi değişkenleri için en iyi sağlam tasarım çözümlerini belirlemek amacıyla üç farklı değişkenlik ölçüsüyle önerilmiştir. İkincisi, kısıtlamaların doğrusallaştırılması, etkin bir optimizasyon yöntemi olarak sıralı ikinci dereceden programlama yöntemi kullanılarak gerçekleştirilir. Üçüncüsü, girdi değişkenleri için en uygun ayarları elde etmek için literatürden bir baskı işlemi süreci araştırılmıştır. Son olarak, önerilen modelin sonuçları geleneksel modellere göre yaklaşık % 16 daha fazla varyansın azaldığını gösterir.

Anahtar Kelimeler:

: sağlam tasarım, ikili tepki modeli, yanıt yüzey tasarımı, farklı değişkenlik ölçüleri, sıralı ikinci dereceden programlama

A PROPOSED RESPONSE SURFACE-BASED ROBUST DESIGN MODEL FOR QUALITY ENGINEERING PROBLEMS

The aim of robust design models is to reduce the variability reduction as small as possible. The process bias defined as a difference between the desired target value and the process mean is an important concern for quality engineering problems. In addition, the selection of different variability measures may also change optimal operating conditions for a response variable. Therefore, this paper is three-fold. One, another view of dual response model is proposed with the three different variability measures in order to determine optimum robust design solutions for input variables while minimizing the process bias. Two, the linearization of constraints is performed using the sequential quadratic programming method as an effective optimization method. Three, a printing process from the literature is conducted to obtain the best optimal settings for input variables. Finally, the results of the proposed model show approximately % 16 more variance reduction than traditional models.

Keywords:

robust design, dual response model, response surface design, variability measures, sequential quadratic programming,

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