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Traditional teaching programing for the welding of large-scale complex structures is extremely difficult, with low efficiency and poor consistency, and it is very difficult to obtain the random change of joint geometry and achieve excellent welding quality along the entire welding path. In this paper, an integrated method of automatic generation of welding paths and adaptive filling of lap welding based on laser-structured light scanning is proposed. First, the point-cloud data of the welding joint on the entire welding path were acquired by laser scanning a workpiece, and the welding-seam model of the lap joint was accurately reconstructed using a cubic smoothing spline algorithm. The key feature points of the welding path were then extracted by Douglas-Peucker algorithm and the corresponding postures calculated and optimized. Furthermore, a neural network model that correlated the optimized process parameters with the gaps in the lap joints was established. During the welding process, the welding parameters were adaptively adjusted according to the variation of the obtained joint geometry based on the model. The results of the experiments showed that the proposed method has excellent adaptability to complex-curve welding seams with gaps randomly distributed between 0 and 2 mm, achieving satisfactory results in both path accuracy and weld appearance.

As to improve the accuracy, efficiency and flexibility of welding process, intelligent welding is an inevitable trend in the future. To realize the modeling of autonomous welding path planning to the spatial complex weld of large workpiece, we designed a weld path autonomous programming system based on laser structure light, which effectively obtained three-dimensional (3D) information of workpiece by multiple segment scanning, then extracted weld features by grayscale image processing, and reconstructed spatial complex curve seam model by cubic smoothing spline and detected characteristic parameters of lap joints, contributing to subsequent self-generated welding procedures. Besides, experimental results showed that the recognition accuracy of the spatial curve seam by proposed method was within 0.4 mm, meeting the weld tracking requirements.

Software developers with service-oriented technologies usually put a lot of efforts to deploy and manage supporting middleware and tools. Meanwhile PaaS in cloud computing aims at providing efficient support for software developers. In the light of this consideration, we have designed and implemented Service4All, a service cloud platform targeting at improving productivity of service-oriented software developers. In this paper, we describe the key technical issues, architecture design and implementation technology of SAE, a key component in Service4All, in terms of scalability. First, we propose a software appliance-based mechanism for elastic middleware management. Second, we describe a micro-kernel-based AppEngine core for efficient coordination of various components. Third, we present a scalable replication framework to achieve highly scalable application services. Finally, through a real application deployed on Service4All, we demonstrate the effectiveness of our solution.