Forecasting process parameters on weld nugget hardness of filler added friction stir welded dissimilar aluminium alloys 5052 And 6082 joints

This article deals with the optimization of friction stir welding process parameters with filler ratios on dissimilar Aluminium alloy groups. For this purpose, 6 series Aluminium alloy 6082 and 5 series Aluminium alloy 5052 were taken. Microhardness property was conducted under various rotational speeds, welding speed, plunge depth, Center distance between the holes and filler mixing ratio. The Central Composite Design (CCD), the most commonly used Response Surface Methodology (RSM), is considered to develop the prediction equation. A validation analysis is carried out, and the results were compared with the relative impact of input parameters on weld nugget microhardness. It is observed that the increase in welding speed with plunge depth and filler ratio result in the increase of weld nugget microhardness up to a maximum value. The maximum weld nugget hardness of fabricated joint was obtained with the welding process parameters combination of 1000 rpm rotational speed, 125 mm/min welding speed, 0.15 mm plunge depth, 2 mm centre distance between the holes, and filler ratio of 95% Mg and 5% Cr.

The Effect of Plug Rotation Speed on Micro-Structure of Nugget Zone of Friction Plug Repair Welding Joint for 6082 Aluminum Alloy

This paper carried out the friction plug repair welding of 6082 aluminum alloy keyhole defects by using the method of friction heating between shaft shoulder and base material. In addition, a well-formed friction plug welding joint was obtained at different plug rotation speeds. In order to study the influence mechanism of plug rotation speeds on the microstructure of the weld nugget zone, EBSD technology was used to analyze the grain morphology, grain size and grain boundary characteristics of the weld nugget zone under different rotation speeds of the plug rod. The results show that in the nugget zone, the grain was fine and equated crystals refinement, and there was a preferred orientation. The deformation texture components in the welded nugget zone increased with the plug rotation speed from 1600 to 2000 rpm. However, the grain size first decreased and then increased, while the components in the High-Angle Boundary first increased and then decreased.

PREDICTING NUGGET SIZE OF RESISTANCE SPOT WELDS USING INFRARED THERMAL VIDEOS WITH IMAGE SEGMENTATION AND CONVOLUTIONAL NEURAL NETWORK

Resistance spot welding (RSW) is a widely adopted joining technique in automotive industry. Recent advancement in sensing technology makes it possible to collect thermal videos of the weld nugget during RSW using an infrared camera. The effective and timely analysis of such thermal videos has the potential of enabling in-situ nondestructive evaluation (NDE) of the weld nugget by predicting nugget thickness and diameter. Deep learning (DL) has demonstrated to be effective in analyzing imaging data in many applications. However, the thermal videos in RSW present unique data-level challenges that compromise the effectiveness of most pre-trained DL models. We propose a novel image segmentation method for handling the RSW thermal videos to improve the prediction performance of DL models in RSW. The proposed method transforms raw thermal videos into spatial-temporal instances in four steps: video-wise normalization, removal of uninformative images, watershed segmentation, and spatial-temporal instance construction. The extracted spatial-temporal instances serve as the input data for training a DL-based NDE model. The proposed method is able to extract high-quality data with spatial-temporal correlations in the thermal videos, while being robust to the impact of unknown surface emissivity. Our case studies demonstrate that the proposed method achieves better prediction of nugget thickness and diameter than predicting without the transformation.

Predicting Nugget Size of Resistance Spot Welds Using Infrared Thermal Videos With Image Segmentation and Convolutional Neural Network

Resistance spot welding (RSW) is a widely adopted joining technique in automotive industry. Recent advancement in sensing technology makes it possible to collect thermal videos of the weld nugget during RSW using an infrared camera. The effective and timely analysis of such thermal videos has the potential of enabling in-situ nondestructive evaluation (NDE) of the weld nugget by predicting nugget thickness and diameter. Deep learning (DL) has demonstrated to be effective in analyzing imaging data in many applications. However, the thermal videos in RSW present unique data-level challenges that compromise the effectiveness of most pre-trained DL models. We propose a novel image segmentation method for handling the RSW thermal videos to improve the prediction performance of DL models in RSW. The proposed method transforms raw thermal videos into spatial-temporal instances in four steps: video-wise normalization, removal of uninformative images, watershed segmentation, and spatial-temporal instance construction. The extracted spatial-temporal instances serve as the input data for training a DL-based NDE model. The proposed method is able to extract high-quality data with spatial-temporal correlations in the thermal videos, while being robust to the impact of unknown surface emissivity. Our case studies demonstrate that the proposed method achieves better prediction of nugget thickness and diameter than predicting without the transformation.

Microstructure and Mechanical Performance of Resistance Spot Welded Martensitic Advanced High Strength Steel

Resistance spot welded 1.2 mm (t)-thick 1400 MPa martensitic steel (MS1400) samples are fabricated and their microstructure, mechanical properties are investigated thoroughly. The mechanical performance and failure modes exhibit a strong dependence on weld-nugget size. The pull-out failure mode for MS1400 steel resistance spot welds does not follow the conventional weld-nugget size recommendation criteria of 4t0.5. Significant softening was observed due to dual phase microstructure of ferrite and martensite in the inter-critical heat affected zone (HAZ) and tempered martensite (TM) structure in sub-critical HAZ. However, the upper-critical HAZ exhibits obvious higher hardness than the nugget zone (NZ). In addition, the mechanical properties show that the cross-tension strength (CTS) is about one quarter of the tension-shear strength (TSS) of MS1400 weld joints, whilst the absorbed energy of cross-tension and tension-shear are almost identical.

An Exploratory Study on Resistance Spot Welding of Titanium Alloy Ti-6Al-4V

Resistance spot welding (RSW) is one of the most effective welding methods for titanium alloys, in particular Ti-6Al-4V. Ti-6Al-4V is one of the most used materials with its good ductility, high strength, weldability, corrosion resistance, and heat resistance. RSW and Ti-6Al-4V materials are often widely used in industrial manufacturing, particularly in automotive and aerospace industries. To understand the phenomenon of resistance spot weld quality, the physical and mechanical properties of Ti-6Al-4V spot weld are essential to be analyzed. In this study, an experiment was conducted using the Taguchi L9 method to find out the optimum level of the weld joint strength. The given optimum level sample was analyzed to study the most significant affecting RSW parameter, the failure mode, the weld nugget microstructure, and hardness values. The high heat input significantly affect the weld nugget temperature to reach and beyond the β-transus temperature. It led to an increase in the weld nugget diameter and the indentation depth. The expulsion appeared in the high heat input and decreased the weld nugget strength. It was caused by the molten material ejection in the fusion zone. The combination of high heat input and rapid air cooling at room temperature generated a martensite microstructure in the fusion zone. It increased the hardness, strength, and brittleness but decreased the ductility.

A novel expulsion control strategy with abnormal condition adaptability for resistance spot welding

Welding expulsion is a common problem in Resistance Spot Welding (RSW) process, which severely impacts weld quality and surrounding facilities. Existing expulsion control strategies are ineffective for complex and changeable welding conditions. This article studied the growth relationship between weld nugget and corona bond under two abnormal conditions: edge proximity (EP) and initial sheet gaps (IG). It is testified that expulsion would occur when the nugget size exceeds the corona bond size under EP and IG conditions. Reducing the welding current before the expulsion time can increase the size difference between the corona bond and the weld nugget, thereby delay and even eliminate the occurrence of expulsion. In this way, a novel online expulsion control strategy, named short-time current regulation (STCR), is proposed through expulsion moment analysis of historical weld data. The effect of the new control strategy is verified with workpieces ranging from low carbon steel to ultra-high strength steel. Experimental results showed that STCR can effectively reduce the amount of expelled metal, decrease the indentation depth and increase the nugget diameter. The method not only works well under one specific abnormal condition, but also adapts to the transition between different welding conditions. This novel expulsion control strategy can help achieve expulsion-free RSW process in mass production without frequent manual offline optimization of welding parameters.

Application of Imaging Techniques to Determine the Post-Yield Behaviour of the Heterogeneous Microstructure of Friction Stir Welds

Background Friction Stir Welding (FSW) causes intense plastic deformation and consequent thermomechanical interactions resulting in a localised heterogeneous microstructure. To understand the weld mechanical behaviour, it is necessary to identify each microstructural sub-region in the weld. Objective Determine the relationship between the local microstructure and mechanical behaviour of the different microstructural regions in a FSW. Methods Scanning electron microscopy (SEM) identified the microstructural sub-regions of an FSW joint. A novel High-Resolution Digital Image Correlation (HR-DIC) methodology enabled the determination of full-field strain response to provide the mechanical behaviour of the FSW sub-regions. X-ray computed tomography (CT) identified the geometry of the FSW and material composition. Results The grain morphology in the FSW varied in the stir zone with a fine grain structure in the weld nugget and larger grains in the thermomechanical affected zone (TMAZ); the grains were larger in the retreating side (RS) compared to the advancing side (AS). Tungsten deposits were found in the weld nugget and attributed to tool wear. The mechanical properties of the weld subregions showed that the material in the stir zone had a greater yield strength than the base material and the RS of the FSW was much more ductile than the weld nugget and the AS side. The tungsten distributions in the stir zone correlated with the local mechanical behaviour. Conclusions A novel methodology is developed that combines microstructural observations with HR-DIC enabling, for the first time, the FSW sub-region mechanical behaviour, to be related to the local grain morphology and inclusions caused by tool wear.