On Sensor-Controlled Robotized One-off Manufacturing

A semi-automatic task oriented system structure has been developed and tested on an arc welding application. In normal industrial robot programming, the path is created and the process is based upon the decided path. Here a process-oriented method is proposed instead. It is natural to focus on the process, since the path is in reality a result of process needs. Another benefit of choosing process focus, is that it automatically leads us into task oriented thoughts, which in turn can be split in sub-tasks, one for each part of the process with similar process-characteristics. By carefully choosing and encapsulating the information needed to execute a sub-task, this component can be re-used whenever the actual subtask occurs. By using virtual sensors and generic interfaces to robots and sensors, applications built upon the system design do not change between simulation and actual shop floor runs. The system allows a mix of real- and simulated components during simulation and run-time

Robotizing the conventional and Hot-Forging Wire Arc Additive Manufacturing processes for producing 3D parts with complex geometries

Wire Arc Additive Manufacturing (WAAM) is an Additive Manufacturing (AM) process which has high deposition rates at reduced costs, being suitable to produce large size compo-nents. Hot-Forging WAAM (HF-WAAM) is a WAAM variant which uses an oscillating hammer to forge the material as it is deposited, improving mechanical properties and the microstruc-ture of the produced parts. This study aimed to use and validate the WAAM and HF-WAAM to robotize the pro-duction of compact metallic and complex geometry parts. Thus, a welding torch capable of performing forging was redesign, developed and assembled in a 6 degree-of-freedom (6-DoF) manipulator robot. 316LSi stainless steel parts were produced using WAAM and HF-WAAM processes. During their production, the vibration signal of the robot was acquired and then processed and compared. The AM robotic system demonstrated to be suitable to build these parts, since the tool tip speed and tool tip to substrate distance are controlled, and the tool path optimized. It was also observed that vibration did not negatively affect the built parts quality.O Wire Arc Additive Manufacturing (WAAM) é um processo de Manufatura Aditiva (MA) que apresenta elevadas taxas de deposição a custos reduzidos sendo adequado para produzir peças de grandes dimensões. O Hot-Forging WAAM (HF-WAAM) é uma variante do WAAM que usa um martelo oscilante para forjar o material à medida que este vai sendo depositado, melhorando as propriedades mecânicas e a microestrutura das peças produzidas. Este trabalho tem como objetivo usar e validar o WAAM e HF-WAAM para robotizar a produção de peças metálicas com geometria complexa. Para isto, uma tocha de soldadura com capacidade de realizar forjamento foi redesenhada, fabricada, e montada num robô manipu-lador de 6 graus de liberdade (6-DoF). Foram produzidas peças em aço inoxidável 316LSi uti-lizando os processos de WAAM e HF-WAAM. Durante a sua produção, o sinal de vibração do robô foi adquirido e posteriormente processado e comparado. O sistema robótico de MA demonstrou ser adequado para produzir peças quando a velocidade da ponta da ferramenta e a distância da ponta da ferramenta ao substrato estavam controladas e o percurso da ferramenta otimizado. Também se observou que a vibração não afetou negativamente a qualidade das peças produzidas

Nonterrestrial utilization of materials: Automated space manufacturing facility

Four areas related to the nonterrestrial use of materials are included: (1) material resources needed for feedstock in an orbital manufacturing facility, (2) required initial components of a nonterrestrial manufacturing facility, (3) growth and productive capability of such a facility, and (4) automation and robotics requirements of the facility

Development and automation of a robotic welding cell Using machine vision in Halcon programming environment

The current Project is developed in ACRO, Automatisering Centrum Research en Opleiding. ACRO is a Research and project Group in the field of automation, it offers a complete package of trainings and services in automation. The project consists in the upgrade of a robotic welding cell into a complete automated application through the implementation of a visual recognition system. In order to achieve this big objective the total project have been segmented into three different task: 1. The installation and functionality of the robotic welding cell without machine vision. 2. Introduction, development and achievement of a vision solution that provides the position and orientation information of the recognised pieces to the industrial robot. 3. Encapsulation of the vision solution deployed into a visual basic environment to offer a friendly interface to the different users and operators. Following the technology used in the project it can be encompassed into three different systems (they will be extensively described in section 3 of this paper): Robotic System. Welding System. Vision System. The final objective piece to recognise and weld is a metal cylinder that will be fixed into a flat square piece. This piece has been selected attending to its welding and visual recognition challenges, which can represent an acceptable example of the potential of the final welding cell once the solution is properly developed. Actually, the current project isn´t an isolated development carried out by ACRO, it is also inside a bigger industrial project developed by different partners and it has the company Sirris as a main contractor. Sirris is the collective centre of the Belgian technological industry. They help companies in the implementation of technological innovations, enabling them to strengthen their competitive position over the long-term. Their employees visit companies on site, offer them technological advice, launch innovation paths, and provide guidance until they reach the implementation phase. It is their aim to find concrete solutions to the real challenges facing Belgian entrepreneurs. The project is called “Smart Factories. Towards the Factory of the Future”. It began in 2012 and it will finish in May of 2016. The goal of the project is support the manufacturing industry in Flanders by the development of intelligent factories increasing substantially the manufacturing production. The result is create a flexible production system able to produce small series with productivity in order to response to the current market trends. A list of concrete steps have been defined in order to achieve the purpose of the project. There are a total of seven technological phases: 1. Zero ramp-up: production of small test series or trial products to check that the specifications of the project are satisfied. 2. Safe human-robot interaction: safe human-robot work in order to the production remain accessible for operators. 3. Auto programming: challenge of achieve the automated programming of the robot according with the information captured by the vision system. 4. Intelligent automated quality control: integration and automation of quality control where the series are controlled 100 per cent. 5. Offline robot programming: development of the required software to ensure complex robot can be programmed remotely. 6. Remote monitoring production: generation of feed-back in order to achieve real-time monitoring. 7. To stand-alone to network manufacturing cells: cells created in the project doesn’t work as isolated islands there are communication with each other and with a Smart Factory. In that way as a final objective once the project is finished, we are focus on the achievement of a real robotic welding cell that presents small, flexible and functional characteristics for companies that does not have the necessarily incomes to invest in the expensive robotic welding solution already implemented in the market.Escuela Técnica Superior de Ingeniería IndustrialUniversidad Politécnica de Cartagen

The NASA SBIR product catalog

The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected

Learning for a robot:deep reinforcement learning, imitation learning, transfer learning

Dexterous manipulation of the robot is an important part of realizing intelligence, but manipulators can only perform simple tasks such as sorting and packing in a structured environment. In view of the existing problem, this paper presents a state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning. The paper first reviews the main achievements and research of the robot, which were mainly based on the breakthrough of automatic control and hardware in mechanics. With the evolution of artificial intelligence, many pieces of research have made further progresses in adaptive and robust control. The survey reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots. Furthermore, deep reinforcement learning, imitation learning, and transfer learning in robot control are discussed in detail. Finally, major achievements based on these methods are summarized and analyzed thoroughly, and future research challenges are proposed

Recent Advancements in Augmented Reality for Robotic Applications: A Survey

Robots are expanding from industrial applications to daily life, in areas such as medical robotics, rehabilitative robotics, social robotics, and mobile/aerial robotics systems. In recent years, augmented reality (AR) has been integrated into many robotic applications, including medical, industrial, human–robot interactions, and collaboration scenarios. In this work, AR for both medical and industrial robot applications is reviewed and summarized. For medical robot applications, we investigated the integration of AR in (1) preoperative and surgical task planning; (2) image-guided robotic surgery; (3) surgical training and simulation; and (4) telesurgery. AR for industrial scenarios is reviewed in (1) human–robot interactions and collaborations; (2) path planning and task allocation; (3) training and simulation; and (4) teleoperation control/assistance. In addition, the limitations and challenges are discussed. Overall, this article serves as a valuable resource for working in the field of AR and robotic research, offering insights into the recent state of the art and prospects for improvement

Advanced Automation for Space Missions

The feasibility of using machine intelligence, including automation and robotics, in future space missions was studied

Intelligent 3D seam tracking and adaptable weld process control for robotic TIG welding

Tungsten Inert Gas (TIG) welding is extensively used in aerospace applications, due to its unique ability to produce higher quality welds compared to other shielded arc welding types. However, most TIG welding is performed manually and has not achieved the levels of automation that other welding techniques have. This is mostly attributed to the lack of process knowledge and adaptability to complexities, such as mismatches due to part fit-up. Recent advances in automation have enabled the use of industrial robots for complex tasks that require intelligent decision making, predominantly through sensors. Applications such as TIG welding of aerospace components require tight tolerances and need intelligent decision making capability to accommodate any unexpected variation and to carry out welding of complex geometries. Such decision making procedures must be based on the feedback about the weld profile geometry. In this thesis, a real-time position based closed loop system was developed with a six axis industrial robot (KUKA KR 16) and a laser triangulation based sensor (Micro-Epsilon Scan control 2900-25). [Continues.

Vision-guided tracking of complex tree-dimensional seams for robotic gas metal arc welding

Automation of welding systems is often restricted by the requirements of spatial information of the seams to be welded. When this cannot be obtained from the design of the welded parts and maintained using accurate xturing, the use of a seam teaching or tracking system becomes necessary. Optical seam teaching and tracking systems have many advantages compared to systems implemented with other sensor families. Direct vision promises to be a viable strategy for implementing optical seam tracking, which has been mainly done with laser vision. The current work investigated direct vision as a strategy for optical seam teaching and tracking. A robotic vision system has been implemented, consisting of an articulated robot, a hand mounted camera and a control computer. A description of the calibration methods and the seam and feature detection and three-dimensional scene reconstruction is given. The results showed that direct vision is a suitable strategy for seam detection and learning. A discussion of generalizing the method used as an architecture for simultanious system calibration and measurement estimation is provided