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Gas metal arc welding (GMAW) process is one of the most widespread welding processes used in industries for their excellent quality, reliability, productivity, and cost-effectiveness. To develop an automatic GMAW system, vision capability in the system is a necessary component supplying real-time information about weld pool and seam tracking. In this research work, an automatic seam tracking system is presented, where the automatic tracking of welding path and torch positioning are performed by a newly developed image acquisition system. The system aims to add a vision capability to the GMAW system. A CCD camera is configured with a welding torch to acquire real-time images. The acquired images are processed through newly developed software for real-time detection of welding seam location and characteristics. The software encapsulates the acquired image input facility, image filtering technique, strategy to measure the seam gap, strategy to position torch at the starting point of welding, user interface for automatic guide, and the strategy to correct the torch movements. The seam recognition accuracy was verified during several welding experiments on linear weld seam. Real-time measurements of the seam gap and the seam tracking have achieved a high accuracy

In recent decades, aerial robots especially small UAVs and drones have witnessed tremendous improvements in terms of their structure, working methodology, flying features and navigation control. UAVs are highly utilized in a wide range of services such as photography, path planning, search and rescue, inspection of power lines and civil constructions, etc. This manuscript reports a wide overview and comprehensive survey of recent developments in commercially available UAV’s and gives a brief note on the progress and research covered in last 10 years. The research presents a roadmap to understand the successive development of advanced drones/ UAVs in terms of their geometric structure, flying mechanism, sensing and vision ability, aviation quality, path planning, intelligent behaviour and adoptability. A literature survey is conducted systematically on 254 retrieved articles published in the last 10 years and scaled down to 96 relevant articles. In these shortlisted articles, path planning, neural network, artificial intelligence, inspection, surveillance, tracking and identification, etc. are the most relevant methodologies or applications presented. The current research is concerned about the growth and impact of UAVs/drones in the society and also inspires the newbies to carry research in this field and propose new methods to select or equip the flying robot for a specific application in various fields. This article also assists researchers in understanding and evaluating their research work in the context of existing solutions. It also helps newcomers and pilots/practitioners to quickly gain an overview of the existing vast literature in the related fields.

This paper proposes a multiple order-up-to policy based inventory replenishment scheme to mitigate the bullwhip effect in a multi-stage supply chain scenario, where various transportation modes are available between the supply chain (SC) participants. The proposed policy is similar to the fixed order-up-to policy approach where replenishment decision "how much to order" is made periodically on the basis of the pre-decided order-up-to inventory level. In the proposed policy, optimal multiple order-up-to levels are assigned to each SC participants, which provides decision making reference point for deciding the transportation related order quantity. Subsequently, a mathematical model is established to define optimal multiple order-up-to levels for each SC participants that aims to maximize overall profit from the SC network. In parallel, the model ensures the control over supply chain pipeline inventory, high satisfaction of customer demand and enables timely utilization of available transportation modes. Findings from the various numerical datasets including stochastic customer demand and lead times validate thatthe proposed optimal multiple order-up-to policy based inventory replenishment scheme can be a viable alternative for mitigating the bullwhip effect and well-coordinated SC. Moreover, determining the multiple order-up-to levels is a NP hard combinatorial optimization problem. It is found that the implementation of new emerging optimization algorithm named Bacterial Foraging Algorithm (BFA) has presented superior optimization performances. The robustness and applicability of the BFA algorithm are further validated statistically by employing the percentage heuristic gap and two-way ANOVA analysis.