IOP Conference Series: Materials Science and Engineering, 2015
In the present work, the effects of gas metal arc welding-cold metal transfer (GMAW-CMT) paramete... more In the present work, the effects of gas metal arc welding-cold metal transfer (GMAW-CMT) parameters on surface roughness are experimentally assessed. The purpose of this study is to develop a better understanding of the effects of welding speed, material thickness and contact tip to work distance on the surface roughness. Experiments are conducted using single pass gas metal arc welding-cold metal transfer (GMAW-CMT) welding technique to join the material. The material used in this experiment was AA6351 aluminum alloy with the thickness of 5mm and 6mm. A Mahr Marsuft XR 20 machine was used to measure the average roughness (Ra) of AA6351 joints. The main and interaction effect analysis was carried out to identify process parameters that affect the surface roughness. The results show that all the input process parameters affect the surface roughness of AA6351 joints. Additionally, the average roughness (Ra) results also show a decreasing trend with increased of welding speed. It is proven that gas metal arc welding-cold metal transfer (GMAW-CMT)welding process has been successful in term of providing weld joint of good surface quality for AA6351 based on the low value surface roughness condition obtained in this setup. The outcome of this experimental shall be valuable for future fabrication process in order to obtained high good quality weld.
Knowledge on occupational safety and health (OSH) management system is very important to any indu... more Knowledge on occupational safety and health (OSH) management system is very important to any industrial organization in order for them to make sure that the safety and health elements in their organization are well maintained and according to the law. It is very important for the industrial management and workers to know and understand the correct OSH knowledge and concept. Because of that it is very important for them to attend sufficient and relevant OSH training in order for them to attain the OSH knowledge. These trainings must be effective and have sufficient impact. For that purpose a new method is used in this research. The Kano model and SERVQUAL have been integrated into the house of quality (HOQ) for the purpose of developing an OSH training course that would satisfy not only the requirement and needs of the industry but also unexpected factors towards the trainee who attends the course. By using this method we can see that the level of understanding for the training participant using this new model is higher compared to the level of understanding for the participant from the conventional OSH training program that has been conducted by the training provider. With the increment in the level of understanding, the level of effectiveness for doing OSH-related job for the training participant would also be different. We can prove this by using Kirkpatrick’s Evaluation Model, whereby we would evaluate the trainee from the conventional OSH training program against the newly developed OSH training program. The evaluation would be based on their level of understanding and the level of effectiveness of doing OSH-related jobs in their respective workplace.
The use of High Strength Steels (HSS) for automotive parts improves car performance in terms of s... more The use of High Strength Steels (HSS) for automotive parts improves car performance in terms of structural strength and weight reduction. However it poses major challenges to manufacturing since HSS is prone to springback. Springback causes deviation in part geometry from its intended design thus giving problem to its subsequent assembly process. In this paper, three models for predicting springback were evaluated. First model is based on the Multiple Regression (MR) technique. Second model utilized Hill Orthotropic constitutive material model and the last model employed a neural network predictive model. All the models were evaluated by using tool surface and stamped part historical data that are obtained from three selected springback prone automotive BIW parts representing three different levels of springback severity namely high, medium and small. The results on the low springback part show that the neural network model outperforms the other approaches.
ABSTRACT The use of High Strength Steels (HSS) for automotive parts improves car performance in t... more ABSTRACT The use of High Strength Steels (HSS) for automotive parts improves car performance in terms of structural strength and weight reduction. However it poses major challenges to manufacturing since HSS is prone to springback. Springback causes deviation in part geometry from its intended design thus giving problem to its subsequent assembly process. In this paper, three models for predicting springback were evaluated. First model is based on the Multiple Regression (MR) technique. Second model utilized Hill Orthotropic constitutive material model and the last model employed a neural network predictive model. All the models were evaluated by using tool surface and stamped part historical data that are obtained from three selected springback prone automotive BIW parts representing three different levels of springback severity namely high, medium and small. The results on the low springback part show that the neural network model outperforms the other approaches.
IOP Conference Series: Materials Science and Engineering, 2015
In the present work, the effects of gas metal arc welding-cold metal transfer (GMAW-CMT) paramete... more In the present work, the effects of gas metal arc welding-cold metal transfer (GMAW-CMT) parameters on surface roughness are experimentally assessed. The purpose of this study is to develop a better understanding of the effects of welding speed, material thickness and contact tip to work distance on the surface roughness. Experiments are conducted using single pass gas metal arc welding-cold metal transfer (GMAW-CMT) welding technique to join the material. The material used in this experiment was AA6351 aluminum alloy with the thickness of 5mm and 6mm. A Mahr Marsuft XR 20 machine was used to measure the average roughness (Ra) of AA6351 joints. The main and interaction effect analysis was carried out to identify process parameters that affect the surface roughness. The results show that all the input process parameters affect the surface roughness of AA6351 joints. Additionally, the average roughness (Ra) results also show a decreasing trend with increased of welding speed. It is proven that gas metal arc welding-cold metal transfer (GMAW-CMT)welding process has been successful in term of providing weld joint of good surface quality for AA6351 based on the low value surface roughness condition obtained in this setup. The outcome of this experimental shall be valuable for future fabrication process in order to obtained high good quality weld.
Knowledge on occupational safety and health (OSH) management system is very important to any indu... more Knowledge on occupational safety and health (OSH) management system is very important to any industrial organization in order for them to make sure that the safety and health elements in their organization are well maintained and according to the law. It is very important for the industrial management and workers to know and understand the correct OSH knowledge and concept. Because of that it is very important for them to attend sufficient and relevant OSH training in order for them to attain the OSH knowledge. These trainings must be effective and have sufficient impact. For that purpose a new method is used in this research. The Kano model and SERVQUAL have been integrated into the house of quality (HOQ) for the purpose of developing an OSH training course that would satisfy not only the requirement and needs of the industry but also unexpected factors towards the trainee who attends the course. By using this method we can see that the level of understanding for the training participant using this new model is higher compared to the level of understanding for the participant from the conventional OSH training program that has been conducted by the training provider. With the increment in the level of understanding, the level of effectiveness for doing OSH-related job for the training participant would also be different. We can prove this by using Kirkpatrick’s Evaluation Model, whereby we would evaluate the trainee from the conventional OSH training program against the newly developed OSH training program. The evaluation would be based on their level of understanding and the level of effectiveness of doing OSH-related jobs in their respective workplace.
The use of High Strength Steels (HSS) for automotive parts improves car performance in terms of s... more The use of High Strength Steels (HSS) for automotive parts improves car performance in terms of structural strength and weight reduction. However it poses major challenges to manufacturing since HSS is prone to springback. Springback causes deviation in part geometry from its intended design thus giving problem to its subsequent assembly process. In this paper, three models for predicting springback were evaluated. First model is based on the Multiple Regression (MR) technique. Second model utilized Hill Orthotropic constitutive material model and the last model employed a neural network predictive model. All the models were evaluated by using tool surface and stamped part historical data that are obtained from three selected springback prone automotive BIW parts representing three different levels of springback severity namely high, medium and small. The results on the low springback part show that the neural network model outperforms the other approaches.
ABSTRACT The use of High Strength Steels (HSS) for automotive parts improves car performance in t... more ABSTRACT The use of High Strength Steels (HSS) for automotive parts improves car performance in terms of structural strength and weight reduction. However it poses major challenges to manufacturing since HSS is prone to springback. Springback causes deviation in part geometry from its intended design thus giving problem to its subsequent assembly process. In this paper, three models for predicting springback were evaluated. First model is based on the Multiple Regression (MR) technique. Second model utilized Hill Orthotropic constitutive material model and the last model employed a neural network predictive model. All the models were evaluated by using tool surface and stamped part historical data that are obtained from three selected springback prone automotive BIW parts representing three different levels of springback severity namely high, medium and small. The results on the low springback part show that the neural network model outperforms the other approaches.
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Papers by Khairul Zaim