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SMErobotics Demonstrator D4 Video

Welding robot assistant

The SMErobotics Consortium has produced a Project Video to explain its vision & mission, its main research and development topics as well as its four main demonstrators. Each of the four demonstrators is displayed in a demonstrator video explaining the workflow of a robotic system in a real-world SME-environment.

The following chapter by chapter description of the Demonstrator D4 video showcases the workflow welding tasks using a "Welding robot assistant".

Chapter 1: Introduction

Chapter 1: Introduction

Link to video: 00:00

[Overview over the robotic system within its working environment, its work space and a work piece. An ongoing welding process is shown, followed by manual welding to face the benefits of a robotic welding cell.]

"Welding is a key industrial joining process. Typically used by small and mid-sized companies, it can be found in workshops across Europe.
Welding requires specialist skills and experience. Yet it can also be hazardous and unergonomic. The aim of SMErobotics is to help SMEs make automated welding more cost-effective and less strenuous for the worker by delivering robot technologies that intuitively interact with the human welder and learn from them."

Chapter 2: Job arrives

Link to video: 00:43

[Robot executes the welding task]

"Welding in SMEs is characterized by short production cycles, small lot sizes and a high number of variants. To date, this has often made the use of a standard robot system impossible. What is needed is a system that automatically analyses the incoming jobs and interacts with the human to allow quick and simple programming."

Chapter 3: Programming of weld seams (selection of seams)

Link to video: 01:08

[A job arrives on the workers tablet. According to the weld part the worker selects and defines the "Welding technology" needed for processing the job.]

"A welder is an expert who knows how, in what order and with what parameters a part needs welding. Intuitive modes of interaction are designed to enable the welder to easily teach this information to the robot. The robot analyses this human input, plans the welding process accordingly and learns from past welding tasks to improve its future performance. The goal is to make manual programming significantly easier and to make it possible to cope with a lack of information."

Chapter 4: Scanning of seams

Link to video: 01:45

[The worker places the work piece into the robotic welding cell and selects the parts to be seam welded. Next, the robot scans those parts to calculate the welding parameters. Additionally, the work checks the calculated parameters and - if necessary - adjusts the parameters manually.]

"Here, the robot uses its sensors to determine the exact location of the workpiece as well as any shape deviations. This information is taken into account in planning the welding process."

"The strengths of both human worker and robot should be exploited to the maximum. A robot is good at identifying an exact location and repeating a process with high accuracy and endurance."

Chapter 5: Error recovery

Link to video: 02:13

[For the localisation of the work piece within the work space a vision system first detects the parts and the seams to be welded.]

"Errors may prevent successful program execution. Usually, a robotics expert is required to solve such errors. We are working to give the robot the cognitive capabilities with which to analyse the sources of errors and to suggest how they can be resolved or prevented."

Chapter 6: Welding I

Link to video: 02:33

[A preview of the process indicates the movement of the robot during welding.]

"Cognitive capabilities and intuitive interfaces allow a robot to be programmed much faster and to execute a welding task more flexibly, which delivers a cost benefit for the SME. Freed from monotonous and tedious tasks, workers can use their expertise more effectively and stay on the job longer."

Chapter 7: Welding II

Link to video: 02:57

[As not all seams are reachable to the robot offers solutions. The human worker follows the solution instructions by the robot and moves the work piece manually in a defined position, calculated by the robot. A rescan by the robot prooves the localisation of the work piece. As everything is in a reachable position the robot starts to process the seam weling task.]

"In mass production, an automation system can be set up and tuned for a specific task, because it will execute the same task for a long time. In SME production, however, reconfiguration of the robot cell is a daily occurrence. Yet it takes too long to reconfigure the robots of today. If a welding robot can be made easier to reprogram and reconfigure, this will make automation cost-effective for many SME production scenarios."

Chapter 8: Seam inspection

Link to video: 03:32

[The worker checks the quality of the work piece. With the feedback given by the worker via the interface, the robot improves its skills. Based on its vision system the robot inspects the seams to "learn" the improved welding parameters.]

"The welding expert can give feedback on weld seam quality. This type of feedback is based on human expertise and cannot be acquired automatically. On the other hand, the robot can collect accurate numeric information during the welding process and automatic quality inspection. All this information is assembled and used by the SMErobotics welding assistant to learn from past welding tasks and to improve its future performance. Just the same as a human worker."

Chapter 9: Statement

Link to video: 04:06

Original statement (German):

Worker: "Die Programmierung von Schweißteilen ist meistens sehr aufwendig und daher lohnen sich Kleinserien in den meisten Fällen nicht. Unsere Teile werden noch dazu manuell geheftet was große Toleranzen mit sich führt - und daraufhin müssen die Teile einzeln "nachgeteacht" werden, was einen sehr großen Zeitaufwand mit sich bringt. Die Bauteilerkennung und Bahnplanung hilft uns somit das Ganze zu optimieren und den Aufwand einfacher zu gestalten."

Subtiles (English):

Worker: "t takes a lot of effort to program a welding process, so it’s not usually worth it for small-lot production. Also, our parts need to be tacked by hand, which means large tolerances. After that, we have to teach the robot what to do with each individual part, which takes a great deal of time. Component recognition and path planning help us to optimise the whole process and save on time and effort."

Chapter 10: Outro

Chapter 10: SMErobotics statement

Link to video: 04:32

[Summary of the benefits of using robots in SME-manufacturing]

"Providing robots with cognitive capabilities and intuitive interfaces is the key to making the automation of welding tasks technically and economically feasible for small lot sizes. This contributes to sharpen the competitive edge of European industry, keep jobs in Europe and improve working conditions."

Chapter 10: SMErobotics statement

Chapter 10: SMErobotics statement

Link to video: 03:54

[SMErobotics logo animation including all the logos of the consortium partners]

"Europe’s leading robot manufacturers and research institutes have teamed up with the European Robotics Initiative for Strengthening the Competitiveness of SMEs in Manufacturing - To make the vision of cognitive robotics in manufacturing a reality."

News

Events

Thursday, 2017-05-11
Thursday, 2016-06-30
Tuesday, 2016-06-21

AUTOMATICA 2016

Wednesday, 2016-06-01

RoboBusiness Europe