Simulation of Weld Depth in A-TIG Welding with Unified Arc-electrode model

Ｉｔｋｎｏｗｎｔｈａｔｓｍａｌｌａｍｏｕｎｔｓｏｆｆｌｕｘｏｎｔｈｅｓｕｒｆａｃｅｏｆｓｔａｉｎｌｅｓｓｓｔｅｅｌｃａｎｉｎｃｒｅａｓｅｔｈｅｄｅｐｔｈｏｆｗｅｌｄｐｅｎｅ ｔｒａｔｉｏｎｉｎＴＩＧｗｅｌｄｉｎｇｂｙａｆａｃｔｏｒｏｆｔｈｒｅｅ[1] .Ｔｈｉｓｐｒｏｃｅｓｓｉｓｒｅｆｅｒｒｅｄｔｏａｓ”ＡＴＩＧ”ｏｒＴＩＧｗｅｌｄｉｎｇａｃｔｉ ｖａｔｅｄｂｙｆｌｕｘ .Ｔｈｅｒｅｈａｖｅｂｅｅｎｔｈｒｅｅｐｕｂｌｉｓｈｅｄｐｈｙｓ ｉｃａｌｍｅｃｈａｎｉｓｍｓｔｈａｔａｒｅｐｏｓｓｉｂｌｅｃｏｎｔｒｉ…     

Recent development of high efficiency arc welding systems in Japan

Automationhasgivenapowerfulmeansto solvemanyissuesinproductionindustries,e.g.up efficiency,costreduction,manpowerproblems,en vironmentproblems,etc.Automatizationofthe weldingprocesseshavebeenremarkablypro gressedduringaboutthelasthalfcenturyandit wasbasedonthetechnologicaldevelopmentsof manyconstituentssuchas,sensorsandtheirappli cationsystemsforseamtrackingandadaptivecon trol,weldingrobot,weldingmaterial,weldingpow ersource,etc.Particularlyinthelastdecade, throughtheprogressofthecompute…     

Influence of Flux and Sulfur on YAG Laser Welding

1　IntroductionYAGlaserweldingprocesscanbeconductedbyarobotusinganopticalfibertotransferthelaserenergyontoaworkpieceduetoitssmallwavelength (1.0 6mm ) .ThustheweldingautomationlevelcanbehighlyincreasedcomparedwithCO2 laserwelding .Therefore ,recentlyyears ,YAGlaserweldingprocesshasbeenquicklydevelopedandappliedformanufac turingofautomobileandchemicalvesselsetc .Gener ally ,thepowerofaCWYAGlaserisnotsohigh (sofarupto 10kW )duetoitslowefficiencyofenergyconversion .Whenweldingwithamiddleorl…     

YAG激光与脉冲MIG复合焊

采用设计制造的复合焊接机头 ,研究了YAG激光 脉冲MIG电弧复合焊接铝合金时各种规范参数对焊缝成型的影响规律及激光与电弧的复合作用 .结果表明 ,在比较宽的参数范围内YAG激光 脉冲MIG复合焊接铝合金具有焊缝成型美观 ,无气孔等优点 ,熔深与激光单独焊相比增加 4倍 ,与脉冲MIG焊接相比增加 1倍以上 ,焊速显著提高 ,是一种理想的焊接工艺 .     

Volatility forecasts using stochastic volatility models with nonlinear leverage effects

The leverage effect—the correlation between an asset's return and its volatility—has played a key role in forecasting and understanding volatility and risk. While it is a long standing consensus that leverage effects exist and improve forecasts, empirical evidence puzzlingly does not show that this effect exists for many individual stocks, mischaracterizing risk, and therefore leading to poor predictive performance. We examine this puzzle, with the goal to improve density forecasts, by relaxing the assumption of linearity of the leverage effect. Nonlinear generalizations of the leverage effect are proposed within the Bayesian stochastic volatility framework in order to capture flexible leverage structures. Efficient Bayesian sequential computation is developed and implemented to estimate this effect in a practical, on-line manner. Examining 615 stocks that comprise the S&P500 and Nikkei 225, we find that our proposed nonlinear leverage effect model improves predictive performances for 89% of all stocks compared to the conventional stochastic volatility model.