TAGUCHI EXPERIMENTAL DESIGN AND ARTIFICIAL NEURAL NETWORK SOLUTION OF STUD ARC WELDING PROCESS
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 chara...
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| Format: | Article |
| Language: | English |
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University of Baghdad
2010-06-01
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| Series: | Journal of Engineering |
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| Online Access: | https://joe.uobaghdad.edu.iq/index.php/main/article/view/2600 |
| _version_ | 1797301121530724352 |
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| author | Nabeel K. Abid Al-sahib Riyadh M.A Hamza Ismail I. Al-kazaz |
| author_facet | Nabeel K. Abid Al-sahib Riyadh M.A Hamza Ismail I. Al-kazaz |
| author_sort | Nabeel K. Abid Al-sahib |
| collection | DOAJ |
| description | 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, 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 was 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).
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| first_indexed | 2024-03-07T23:17:51Z |
| format | Article |
| id | doaj.art-541c587670b142ad871e24b137c912a1 |
| institution | Directory Open Access Journal |
| issn | 1726-4073 2520-3339 |
| language | English |
| last_indexed | 2024-03-07T23:17:51Z |
| publishDate | 2010-06-01 |
| publisher | University of Baghdad |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj.art-541c587670b142ad871e24b137c912a12024-02-21T09:48:56ZengUniversity of BaghdadJournal of Engineering1726-40732520-33392010-06-01160210.31026/j.eng.2010.02.05TAGUCHI EXPERIMENTAL DESIGN AND ARTIFICIAL NEURAL NETWORK SOLUTION OF STUD ARC WELDING PROCESSNabeel K. Abid Al-sahibRiyadh M.A HamzaIsmail I. Al-kazazStud 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, 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 was 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). https://joe.uobaghdad.edu.iq/index.php/main/article/view/2600Taguchi experimental Design, Stud Welding Optimization, Artificial Neural Network, Stud Welding. |
| spellingShingle | Nabeel K. Abid Al-sahib Riyadh M.A Hamza Ismail I. Al-kazaz TAGUCHI EXPERIMENTAL DESIGN AND ARTIFICIAL NEURAL NETWORK SOLUTION OF STUD ARC WELDING PROCESS Journal of Engineering Taguchi experimental Design, Stud Welding Optimization, Artificial Neural Network, Stud Welding. |
| title | TAGUCHI EXPERIMENTAL DESIGN AND ARTIFICIAL NEURAL NETWORK SOLUTION OF STUD ARC WELDING PROCESS |
| title_full | TAGUCHI EXPERIMENTAL DESIGN AND ARTIFICIAL NEURAL NETWORK SOLUTION OF STUD ARC WELDING PROCESS |
| title_fullStr | TAGUCHI EXPERIMENTAL DESIGN AND ARTIFICIAL NEURAL NETWORK SOLUTION OF STUD ARC WELDING PROCESS |
| title_full_unstemmed | TAGUCHI EXPERIMENTAL DESIGN AND ARTIFICIAL NEURAL NETWORK SOLUTION OF STUD ARC WELDING PROCESS |
| title_short | TAGUCHI EXPERIMENTAL DESIGN AND ARTIFICIAL NEURAL NETWORK SOLUTION OF STUD ARC WELDING PROCESS |
| title_sort | taguchi experimental design and artificial neural network solution of stud arc welding process |
| topic | Taguchi experimental Design, Stud Welding Optimization, Artificial Neural Network, Stud Welding. |
| url | https://joe.uobaghdad.edu.iq/index.php/main/article/view/2600 |
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