A method for optimizing monitoring by using Artificial Neural Network (ANN) technique was proposed based on instability of arc voltage signal and welding current signal of solid wire electrode (GMAW). This technique is not only for effective process modeling, but also to illustrate the correlation between the input and output parameters responses. The algorithms of monitoring were developed in time domain by carrying out the Moving Average (M.A) and Root Mean Square (RMS) based on the welding experiment parameters such as travel speed, thickness of specimen, feeding speed, and wire electrode diameter to detect and estimate with a satisfactory sample size. Experiment data was divided into three subsets: train (70%), validation (15%), and test (15%). Error back-propagation of Levenberg-Marquardt algorithm was used to train for this algorithm. The proposed algorithms on this paper were used to estimate the variety the Contact Tip to Work Distance (CTWD) through Mean Square Error (MSE). Based on the results, the algorithms have shown that be able to detect changes in CTWD automatically and real time with takes 0.147 seconds (MSE 0.0087).

The aim of this study is to improve cladding process productivity by high production rate with low dilution process by specifying technique as hot-wire GMAW process. The base metal of carbon steel A516 Gr70 was cladded by austenitic stainless steel 309LSi for creating a buttering layer and stainless steel 308LSi for hot-wire filler for topping a cladding layer in a one-pass run. The studied parameters this experiment consist of the feeding ratio of hot wire feeding speed per GMAW wire feeding speed and travel speed. Welding phenomenon during welding was observed by CCD camera with specifying the optical device to see the appropriate condition. The result showed the hot-wire GMAW cladding process could reduce cycle time 3.5 times compare with conventional FCAW cladding process. Moreover, dilution of this process could decrease lower than 15% with acceptable FN 3 on the top of weld surface. Therefore, single pass cladding process achieved by using this method with low dilution by still keep microstructure capability.

This paper proposes a new evaluation method for welder skill in Gas Metal Arc Welding (GMAW) process in term of studying the natural hand-movement that affect the signal processing. Weld quality of GMAW generally depends on welder skill to maintain the uniform of hand movement. Therefore, the welder skill is considered as the critical point to maintain the weld quality. Hence, welding current and voltage signal could be an alternative way for monitoring and assessing the skill of welder based on the signal variation of the welding process. This research defines in two stages, first is the physical-simulation using robot welding Fanuc Arc Mate 100iB and monitoring the signal using Cyclogram technique. Second is comparing the Cyclogram characteristic of robot welding and manual welder. By using the data acquired, the characteristic of Cyclogram was analyzed by varying Torch angle change (W1) and Torch-height change (W2) to investigate the signal processing. Furthermore, the data of current and voltage were generated as a quantitative method to determine the size of Cyclogram. The results show that the method capable of differentiating the two beginner welders compare to the robot welding performance based area of Cyclogram characteristic. Finally, the Cyclogram could be a novel tool for monitoring and evaluating the welder skill with high sensitivity to detect hand motion.

No abstract

No abstract