Manual monitoring and seam tracking through watching weld pool images in real-time, by naked eyes or by industrial TV, are experience-depended, subjective, labor intensive, and sometimes biased. So it is necessary to ...Manual monitoring and seam tracking through watching weld pool images in real-time, by naked eyes or by industrial TV, are experience-depended, subjective, labor intensive, and sometimes biased. So it is necessary to realize the automation of computer-aided seam tracking. A PAW (plasma arc welding) seam tracking system was developed, which senses the molten pool and the seam in one frame by a vision sensor, and then detects the seam deviation to adjust the work piece motion adaptively to the seam position sensed by vision sensor. A novel molten pool area image-processing algorithm based on machine vision was proposed. The algorithm processes each image at the speed of 20 frames/second in real-time to extract three feature variables to get the seam deviation. It is proved experimentally that the algorithm is very fast and effective. Issues related to the algorithm are also discussed.展开更多
In this study,experimental wear losses under different loads and sliding distances of AISI 1020 steel surfaces coated with(wt.%)50FeCrC‐20FeW‐30FeB and 70FeCrC‐30FeB powder mixtures by plasma transfer arc welding w...In this study,experimental wear losses under different loads and sliding distances of AISI 1020 steel surfaces coated with(wt.%)50FeCrC‐20FeW‐30FeB and 70FeCrC‐30FeB powder mixtures by plasma transfer arc welding were determined.The dataset comprised 99 different wear amount measurements obtained experimentally in the laboratory.The linear regression(LR),support vector machine(SVM),and Gaussian process regression(GPR)algorithms are used for predicting wear quantities.A success rate of 0.93 was obtained from the LR algorithm and 0.96 from the SVM and GPR algorithms.展开更多
Wear tests are essential in the design of parts intended to work in environments that subject a part to high wear.Wear tests involve high cost and lengthy experiments,and require special test equipment.The use of mach...Wear tests are essential in the design of parts intended to work in environments that subject a part to high wear.Wear tests involve high cost and lengthy experiments,and require special test equipment.The use of machine learning algorithms for wear loss quantity predictions is a potentially effective means to eliminate the disadvantages of experimental methods such as cost,labor,and time.In this study,wear loss data of AISI 1020 steel coated by using a plasma transfer arc welding(PTAW)method with FeCrC,FeW,and FeB powders mixed in different ratios were obtained experimentally by some of the researchers in our group.The mechanical properties of the coating layers were detected by microhardness measurements and dry sliding wear tests.The wear tests were performed at three different loads(19.62,39.24,and 58.86 N)over a sliding distance of 900 m.In this study,models have been developed by using four different machine learning algorithms(an artificial neural network(ANN),extreme learning machine(ELM),kernel-based extreme learning machine(KELM),and weighted extreme learning machine(WELM))on the data set obtained from the wear test experiments.The R2 value was calculated as 0.9729 in the model designed with WELM,which obtained the best performance among the models evaluated.展开更多
文摘Manual monitoring and seam tracking through watching weld pool images in real-time, by naked eyes or by industrial TV, are experience-depended, subjective, labor intensive, and sometimes biased. So it is necessary to realize the automation of computer-aided seam tracking. A PAW (plasma arc welding) seam tracking system was developed, which senses the molten pool and the seam in one frame by a vision sensor, and then detects the seam deviation to adjust the work piece motion adaptively to the seam position sensed by vision sensor. A novel molten pool area image-processing algorithm based on machine vision was proposed. The algorithm processes each image at the speed of 20 frames/second in real-time to extract three feature variables to get the seam deviation. It is proved experimentally that the algorithm is very fast and effective. Issues related to the algorithm are also discussed.
文摘In this study,experimental wear losses under different loads and sliding distances of AISI 1020 steel surfaces coated with(wt.%)50FeCrC‐20FeW‐30FeB and 70FeCrC‐30FeB powder mixtures by plasma transfer arc welding were determined.The dataset comprised 99 different wear amount measurements obtained experimentally in the laboratory.The linear regression(LR),support vector machine(SVM),and Gaussian process regression(GPR)algorithms are used for predicting wear quantities.A success rate of 0.93 was obtained from the LR algorithm and 0.96 from the SVM and GPR algorithms.
文摘Wear tests are essential in the design of parts intended to work in environments that subject a part to high wear.Wear tests involve high cost and lengthy experiments,and require special test equipment.The use of machine learning algorithms for wear loss quantity predictions is a potentially effective means to eliminate the disadvantages of experimental methods such as cost,labor,and time.In this study,wear loss data of AISI 1020 steel coated by using a plasma transfer arc welding(PTAW)method with FeCrC,FeW,and FeB powders mixed in different ratios were obtained experimentally by some of the researchers in our group.The mechanical properties of the coating layers were detected by microhardness measurements and dry sliding wear tests.The wear tests were performed at three different loads(19.62,39.24,and 58.86 N)over a sliding distance of 900 m.In this study,models have been developed by using four different machine learning algorithms(an artificial neural network(ANN),extreme learning machine(ELM),kernel-based extreme learning machine(KELM),and weighted extreme learning machine(WELM))on the data set obtained from the wear test experiments.The R2 value was calculated as 0.9729 in the model designed with WELM,which obtained the best performance among the models evaluated.
