Spot Welding Robot Technology

2016 ◽  
Vol 85 (7) ◽  
pp. 646-651
Author(s):  
Teruki ITO
Author(s):  
Johan Segeborn ◽  
Johan S. Carlson ◽  
Kristina Wa¨rmefjord ◽  
Rikard So¨derberg

Spot welding is the predominant joining method in car body assembly. Spot welding sequences have a significant influence on the dimensional variation of resulting assemblies and ultimately on overall product quality. It also has a significant influence on welding robot cycle time and thus ultimately on manufacturing cost. In this work we evaluate the performance of Genetic Algorithms, GAs, on multi-criteria optimization of welding sequence with respect to dimensional assembly variation and welding robot cycle time. Reference assemblies are fully modelled in 3D including detailed fixtures, welding robots and weld guns. Dimensional variation is obtained using variation simulation and part measurement data. Cycle time is obtained using automatic robot path planning. GAs are not guaranteed to find the global optimum. Besides exhaustive calculations, there is no way to determine how close to the actual optimum a GA trial has reached. Furthermore, sequence fitness evaluations constitute the absolute majority of optimization computation running time and do thus need to be kept to a minimum. Therefore, for two industrial reference assemblies we investigate the number of fitness evaluations that is required to find a sequence that is optimal or a near-optimal with respect to the fitness function. The fitness function in this work is a single criterion based on a weighted and normalized combination of dimensional variation and cycle time. Both reference assemblies involves 7 spot welds which entails 7!=5040 possible welding sequences. For both reference assemblies, dimensional variation and cycle time is exhaustively calculated for all 5040 possible sequences, determining the optimal sequence, with respect to the fitness function, for a fact. Then a GA that utilizes Random Key Encoding is applied on both cases and the performance is recorded. It is found that in searching through about 1% of the possible sequences, optimum is reached in about half of the trials and 80–90% of the trials reach the ten best sequences. Furthermore the optimum of the single criterion fitness function entails dimensional variation and cycle time fairly close to their respective optimum. In conclusion, this work indicates that genetic algorithms are highly effective in optimizing welding sequence with respect to dimensional variation and cycle time.


2020 ◽  
Vol 19 (04) ◽  
pp. 855-867
Author(s):  
Xiangru Wang ◽  
Wei Song ◽  
Tao Xue ◽  
He Tian

With the development of electronics, mechanical automation, computer and other related disciplines, and the improvement of product efficiency and quality in modern industry, welding robots are born and play an increasingly important role in industrial production lines. 6R welding robot is most commonly used in industrial production lines, so the research on 6R welding robot has practical application values. In this paper, MS165 Yaskawa robot is selected as the target robot. SolidWorks software is used to establish the three-dimensional model of Yaskawa robot, which is imported into Adams. Dynamics analyses of the rigid–flexible coupling system of 6R spot welding robot are studied by powerful dynamic simulation functions of Adams. The maximum stress position of the spot-welding robot working under load is also studied, and the maximum stress curve is obtained.


2011 ◽  
Vol 2-3 ◽  
pp. 366-371 ◽  
Author(s):  
Li Cheng ◽  
Yu Wang Liu ◽  
Hong Guang Wang ◽  
Yong Chang ◽  
Yi Feng Song ◽  
...  

A novel 165Kg spot welding robot is introduced, which is independently developed by China. The Newton-Euler approach is used to derive the dynamic equation. Then, entity model of the robot is built up with Solidworks software. The motion of the robot is planned by the position, velocity and acceleration of all the joint. The first three joints, which bear most of the payload and the own weight of the robot, are set with maximum acceleration during the accelerating/ decelerating process. According to the planned trajectory, dynamic simulation is carried out using Solidworks. The driving torques of each joint are obtained. From the dynamic analysis, we find the position yielding the maximum driving torque while the robot is moving with the maximum payload and the maximum speed. The moment of inertia is a predominant influence in the causation of big actuating torque.


1995 ◽  
Vol 13 (6) ◽  
pp. 756-759
Author(s):  
Atsushi Watanabe ◽  
Ryo Nihei ◽  
Takeshi Okada ◽  
Hiroshi Uchida
Keyword(s):  

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