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Taguchi experimental design and artificial neural network solution of stud arc welding process
Joint Authors
al-Qazzaz, Ismail I.
Hamzah, Riyad M. A.
Abd al-Sahib, Nabil Kazim
Source
Issue
Vol. 16, Issue 2 (30 Jun. 2010), pp.4771-4794, 24 p.
Publisher
University of Baghdad College of Engineering
Publication Date
2010-06-30
Country of Publication
Iraq
No. of Pages
24
Main Subjects
Information Technology and Computer Science
Topics
Abstract AR
تصميم التجارب هو طريقة هيكلية و تنظيم لحساب العلاقات بين عوامل تؤثر في إجراء معين و مخرجات هذا الأجراء، لإجراء تصميم معين بأفضل صياغة من الممكن استخدام طريقة التجربة و الخطأ و لكنه ليست بالطريقة الفعالة.
طرق الأفضلية المنظمة هي أمثل دائما، و قد تم اختيار 225 نموذج من هذا اللحام فيها.
مع التصميم بمعاونة الحاسوب للجانب العملي لتجارب أجراء لحام البرغي اعتمادا على الشبكات العقدية للذكاء الصناعي باستعمال البرنامج الجاهز (Matlab V6.5)، صممت الشبكات العقدية لخلق دقة أكثر بين العوامل و مخرجات الإجراء لتصميم تجارب تاكووجي.
الشبكات العقدية المقترحة هي متعددة الطبقات أمامية موجهة وظفت في ثماني عقد مدخلات (عوامل السيطرة للإجراء)، 16 عقدة مخيفة و عقدتين خارجيتين (متغيرات الاستجابة).
قاعدة التعليم اعتماد على آلية (Levenberg – Marquardt) للتعلم.
باستعمال ماكنة لحام البراغي (DABOTEK) في هذه الدراسة للحام القوس البرغي قياس قطر 6 ملم.
معادن الصفيحة K14358 و K52355 نسبة إلى (USN) و معادن البرغي هي 54NiCrMoS6 4OCrMnMoS8-6 نسبة إلى (DIN).
عوامل سيطرة ثمانية هي (زمن اللحام، سمك الصفيحة، طلاء الصفيحة، تيار اللحام، تصميم البرغي، معدن البرغي، التسخين المسبق و حالة السطح) درست في مصفوفة تصميم تجارب تاكوجي L16 المختلطة عوملت لحساب حالة الحل الأفضل، خطوات التطوير لتحسين طريقة تاكوجي هي خطوة تحليل بيانات التجربة.
كان الانخفاض في الانحراف المعياري (30.006 %) تقريبا و كان الانخفاض في المدى كان (29.39 %) تقريبا.
من ناحية أخرى كانت الزيادة في متوسط مقاومة الشد (30.84%) تقريبا.
العوامل الأكثر تأثيرا على الإجراء هي زمن اللحام و يليه نوعية معدن الصفيحة ثم نوعية معدن البرغي.
Abstract EN
Stud arc welding has become one of the most important unit operations in the mechanical industries.
The need to reduce the time from product discovery to market introduction is inevitable.
Reducing of standard deviation of tensile strength with desirable tensile strength joint as a performance character was use to illustrate the design procedure.
The effects of (welding time, welding current, stud material, stud design, sheet material, and sheet thickness, sheet cleaning and preheating) were studied.
Design of Experiment (DOE) is a structured and organized method to determine relationships between factors affecting a process and output of the process itself.
In order to design the best formulation it is of course possible to use a trial and error approach but this is not an effective way.
Systematic optimization techniques are always preferable.
Tensile strength quality is one of the key factors in achieving good stud welding process performance.
225 samples of stud welding were tested.
Computer aided design of experiment for the stud welding process based on the neural network artificial intelligence by Matlab V6.5 software was also explain.
The ANN was designed to create precise relation between process parameters and response.
The proposed ANN was a supervised multi-layer feed forward one hidden layer with 8 input (control process parameters), 16 hidden and 2 output (response variables) neurons.
The learning rule was based on the Levenberg-Marquardt learning algorithm.
The work of stud welding was performed at the engineering college laboratory, Baghdad University by using the DABOTEKSTUD welding machine, for 6 mm diameter stud.
The sheet materials are (K14358 and K52355) according to (USN standards, and stud materials are (54NiCrMoS6 and 4OCrMnMoS8-6) according to (DIN standards).
The eight control parameters (welding time, sheet thickness, sheet coating, welding current, stud design, stud material, preheat sheet and surface condition) were studied in the mixed L16 experiments Taguchi experimental orthogonal array, to determine the optimum solution conditions.
The optimum condition was reached for the stud welding process tensile strength, where the researcher develops a special fixture for this purpose.
The analysis of results contains testing sample under optimum condition, chemical composition of usage materials and micro structure of optimal condition sample.
According to that : ● Practicality : the influence parameters that affect the stud welding process are welding time, which have a major effect on stud welding process, followed by sheet material and stud material.
● The reduction in standard deviation was approximately (30.06 per cent) and for the range was as approximately (29.39per cent).
In the other side the increase in the tensile strength mean was as approximately (30.84 per cent).
The influence parameters that affect the tensile strength stud welding process are: the factor welding time has a major effect on stud welding process, followed by factor C (sheet coating) and factor F (stud material).
American Psychological Association (APA)
Abd al-Sahib, Nabil Kazim& Hamzah, Riyad M. A.& al-Qazzaz, Ismail I.. 2010. Taguchi experimental design and artificial neural network solution of stud arc welding process. Journal of Engineering،Vol. 16, no. 2, pp.4771-4794.
https://search.emarefa.net/detail/BIM-288429
Modern Language Association (MLA)
Abd al-Sahib, Nabil Kazim…[et al.]. Taguchi experimental design and artificial neural network solution of stud arc welding process. Journal of Engineering Vol. 16, no. 2 (Jun. 2010), pp.4771-4794.
https://search.emarefa.net/detail/BIM-288429
American Medical Association (AMA)
Abd al-Sahib, Nabil Kazim& Hamzah, Riyad M. A.& al-Qazzaz, Ismail I.. Taguchi experimental design and artificial neural network solution of stud arc welding process. Journal of Engineering. 2010. Vol. 16, no. 2, pp.4771-4794.
https://search.emarefa.net/detail/BIM-288429
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 4794
Record ID
BIM-288429