Discussion on key factors of pressure vessel welding intelligence
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摘要: 以绍兴地区的压力容器制造企业为例，调查研究了企业的焊接现状。结果表明，在当前压力容器焊接制造行业仍以传统制造模式为主，焊接过程基本都需要焊工目视检测，焊接智能化程度严重不足。利用主成分分析(PCA)方法对压力容器焊接因素进行分析，得到两个相互独立的主成分指标，主要对应焊工因素和焊接硬件设备因素，结合企业的实际调查情况例证了实现焊接智能化升级的关键因子，对实现压力容器焊接智能化的途径进了探讨，可为相关产业升级提供决策依据。Abstract: Taking pressure vessel manufacturing enterprises in Shaoxing as an example,the welding status of enterprises was investigated and studied. The results showed that traditional manufacturing mode was still the main mode in the current pressure vessel welding manufacturing industry,where manual visual monitoring of welding process was needed basically and the degree of welding intelligence was seriously insufficient. Welding factors of pressure vessels were analyzed by principal component analysis(PCA) and two independent principal component indexes were obtained which mainly corresponded to welder factor and welding hardware equipment factor. Key factors of pressure vessel welding intelligence upgrading were illustrated by combining with the actual investigation of enterprises and then approaches to realize pressure vessel welding intelligence were discussed,which could provide a decision basis for the related industrial upgrading.
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[1] 王国法, 杜毅博. 德国工业4.0与中国煤机装备智能制造的发展[J]. 煤炭科学技术, 2019, 47(3): 1-9. [2] 周济. 智能制造—"中国制造2025"的主攻方向[J]. 中国机械工程, 2015(17): 5-16. [3] 邓勇军,陈华斌,林涛,等. 基于OpenCV的移动焊接机器人视觉系统自主标定方法[J]. 焊接学报, 2012, 33(7): 45-48. [4] Wu D,Chen H B,Chen S B.Monitoring of weld joint penetration during variable polarity plasma arc welding based on the keyhole characteristics and PSO—ANFIS [J]. Journal of Materials Processing Technology, 2016(239):113-124.
[5] 陈善本, 吕娜. 焊接智能化与智能化焊接机器人技术研究进展[J]. 电焊机, 2013(5):72-80. [6] 张俊杰.工业机器人在船厂焊接中的电控技术应用[J].制造业自动化, 2019, 41(6): 146-149. [7] 李江, 时国玉, 刘金龙. 数字化与网络化技术在高速列车焊接车间的实践应用[J]. 电焊机, 2018, 48(3): 43-47. [8] 李鹤喜,韩新乐,方灶军. 一种基于CNN深度学习的焊接机器人视觉模型[J]. 焊接学报, 2019,40(2): 154-160. [9] 路浩, 吕安松. 焊接机器人及其在高速列车智能制造中的应用[J]. 焊接技术, 2015, 44(11): 53-56. [10] 郭广飞, 王勇, 韩涛, 等. 双椭球热源参数调整在预测在役焊接熔池尺寸上的应用[J]. 压力容器, 2013, 30(1): 15-19. [11] Singh A, Datta S, Mahapatra S S, et al. Optimization of bead geometry of submerged arc weld using fuzzy based desirability function approach [J]. Journal of Intelligent Manufacturing, 2013, 24(1): 35-44.
[12] Sharma S K, Maheshwari S, Rathee S. Multi-objective optimization of bead geometry for submerged arc welding of pipeline steel using RSM-fuzzy approach [J]. Journal for Manufacturing Science & Production, 2016, 16(3): 141-151.
[13] 朱齐丹, 王彦柯, 朱伟, 等. 基于结构光的焊点智能识别算法设计[J]. 焊接学报, 2019, 40(7): 82-87. [14] 孙博文, 朱志明, 郭吉昌, 等. 基于激光结构光的视觉传感器的图像处理技术研究应用及展望[J]. 焊接, 2018 (9): 10-14. [15] 段铁群, 石广远, 于丹, 等. 重型压力容器马鞍形焊缝自动焊机插补算法与仿真[J]. 焊接学报, 2010, 31(1): 63-66. [16] 徐祥久, 张宇, 黄超, 等. SA-335 P91钢全位置窄间隙热丝TIG焊接工艺及接头性能研究[J]. 压力容器, 2019(8): 12-15.
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