Process stability analysis and weld formation evaluation during disk laser–mag hybrid welding

2020 ◽  
Vol 124 ◽  
pp. 105835 ◽  
Author(s):  
Xiangdong Gao ◽  
Lin Wang ◽  
Ziqin Chen ◽  
Yanxi Zhang ◽  
Deyong You
2021 ◽  
Vol 1986 (1) ◽  
pp. 012035
Author(s):  
Xi’an Fan ◽  
Congyi Wang ◽  
Nanfeng Zhang ◽  
Yanxi Zhang ◽  
Yaowu Song ◽  
...  

Author(s):  
Luis Ramiro Miramontes-Martínez ◽  
Pasiano Rivas-García ◽  
Alonso Albalate-Ramírez ◽  
José Enrique Botello-Álvarez ◽  
Carlos Escamilla-Alvarado ◽  
...  

Author(s):  
Liping Zhao ◽  
Sheng Hu ◽  
Yiyong Yao

Industrial manufacturing processes often show multiple operating modes, where different modes present different regularities, so real-time monitor and analyzing the quality state stability is an important way to ensure product quality. This paper proposes a state-driven fluctuation space model for quality stability analysis for multimode manufacturing process. First, the whole process is divided into many sub-processes and the multimode formation mechanism is analyzed to form the stability analysis framework. Then each single-mode quality state fluctuation space model is built based on multi-kernel support vector data description method to determine the max effective fluctuation border of the process state. For the current process state, the deep neural network (DNN) is adopted to extract process state features automatically and recognize the mode type. Thus appropriate quality stable fluctuation space model is selected to monitor and analyze the process stability state. Finally, a case study is performed to evaluate the feasibility of proposed stability analysis method, and the result reveals that the method shows good effect for analyzing the process stability in manufacturing process.


Author(s):  
Zied Sahraoui ◽  
Kamel Mehdi ◽  
Moez Ben-Jaber

The development of the manufacturing-based industries is principally due to the improvement of various machining operations. Experimental studies are important in researches, and their results are also considered useful by the manufacturing industries with their aim to increase quality and productivity. Turning is one of the principal machining processes, and it has been studied since the 20th century in order to prevent machining problems. Chatter or self-excited vibrations represent an important problem and generate the most negative effects on the machined workpiece. To study this cutting process problem, various models were developed to predict stable and unstable cutting conditions. Stability analysis using lobes diagrams became useful to classify stable and unstable conditions. The purpose of this study is to analyze a turning process stability using an analytical model, with three degrees of freedoms, supported and validated with experimental tests results during roughing operations conducted on AU4G1 thin-walled tubular workpieces. The effects of the tubular workpiece thickness, the feed rate and the tool rake angle on the machining process stability will be presented. In addition, the effect of an additional structural damping, mounted inside the tubular workpiece, on the machining process stability will be also studied. It is found that the machining stability process is affected by the tubular workpiece thickness, the feed rate and the tool rake angle. The additional structural damping increases the stability of the machining process and reduces considerably the workpiece vibrations amplitudes. The experimental results highlight that the dynamic behavior of turning process is governed by large radial deformations of the thin-walled workpieces. The influence of this behavior on the stability of the machining process is assumed to be preponderant.


2014 ◽  
Vol 26 (4) ◽  
pp. 042005 ◽  
Author(s):  
Oliver Seffer ◽  
Rabi Lahdo ◽  
André Springer ◽  
Stefan Kaierle

2016 ◽  
Vol 28 (1) ◽  
pp. 012004 ◽  
Author(s):  
Qinglong Pan ◽  
Masami Mizutani ◽  
Yousuke Kawahito ◽  
Seiji Katayama

2021 ◽  
Vol 1986 (1) ◽  
pp. 012031
Author(s):  
Guangwen Ye ◽  
Nanfeng Zhang ◽  
Qianwen Liu ◽  
Xiangdong Gao

2011 ◽  
Vol 48 (10) ◽  
pp. 101405
Author(s):  
刘双宇 Liu Shuangyu ◽  
张宏 Zhang Hong ◽  
刘凤德 Liu Fengde ◽  
石岩 Shi Yan ◽  
徐春鹰 Xu Chunying ◽  
...  

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