Robotics for Industry 4.0 – IoT, sensors, and easy programming

On 14 October 2020, TRINITY project continued with another episode of its webinar series on Robotics for Industry 4.0. The aim of the series was to present to the broader audience the robotic demonstrators that SMEs are currently implementing thanks to TRINITY funding, obtained through the first round of open call closed in March 2020. Each project will run from 6 to 12 months, and the SMEs can benefit of TRINITY expertise to benefit the most from their innovative robotic solutions for the all duration of their implementation.The webinar series is made of 4 events, each one grouping the demonstrators according to their field of application. In this episode, the companies that are now implementing their robotic demonstrators have presented their innovative solutions tackling IoT, sensors, and easy programming issues.

After a brief introduction on the TRINITY project and the opportunities that SMEs can have through our open calls, the first demonstrator to be presented was ARGRIND – Advanced robotics for accurate grinding of complex metal parts by Mrs Sara Mata, Researcher at Ideko, Spain. As the name suggests, AGRIN wants to perform a successful accurate grinding of a complex metal part with a complete robotic solution, including a controller to guarantee homogeneous material removal, and a companion software to accurately simulate the ground surface.
Possible benefits gained with the implementation of ARGRIND are the minimization of robot programming time for each new part model to reduce lead time, prevention of part scrapping during the validation tests of new parts thus reducing set-up costs, and implementation of higher automation and efficiency for increased competitiveness.

The second demonstrator to be presented was ‘SNIPE – Sensor Network for Intelligent Predictive Enterprise‘, by Mr Manuel Lobati, Innovation & Project Manager at FAE Technology S.p.a, Italy. SNIPE project proposes an AI-based decision support system for predictive maintenance and monitoring of processes in Foundry and Casting Industry. A modular Smart Monitoring infrastructure will be developed to collect machine process data (temperature, engine vibration, etc.) and predict maintenance on critical processes to reduce energy consumption and increase Overall Equipment Effectiveness (OEE). The project final goal is to reduce the down-time for melting process through monitoring of energy consumption (target – 5% vs.2019), reduction of down-time and improved performance of conveyor and belt transfer (target – 20% vs.2019) and prevention of bad manufacturing quality with a real-time control on green sand humidity (target – 8% vs.2019).

ROBOBEND – ‘World’s first standard bending robot‘ was the next demonstrator on the agenda and its overview was given by Mr Thomas Ronlev, CEO at RoboBend Aps, Denmark.
Robobend can be used at any of the existing press brakes replacing operators and increasing efficiency. The interfacing between the robot and the machine, the standard robot cell design (feeder + manipulator + collector as a standard setup) and the conversion of drawings to programs for the machine and movements of the robot make up a kit for process standardizing. Possible benefits that can be achieved include a reduced system set-up and programming time (up to 10-20 minutes from one batch to the next), reduction in BOM costs up to 20%, bending time cycle: approximately 10 seconds per bend and bending items: size up to 1.5m and 10 kg weight.

A complete solution of screwdriver applications has been given by Thomas Sølund, CTO at Spin Robotics IVS, Denmark with their project Digi-SAAP – Digitalization of collaborative Screwdriver Applications in Agile Productions‘. Digi-SAAP is the first digitized end-of-arm screwdriving tool for collaborative robots with integrated industry 4.0 functionalities for process quality control, and it aims at increasing the process quality during screw assembly tasks. Furthermore, we will develop task teach-in functions to lower the deployment time of a robot screwdriving assembly application. The main benefit is to being able to setup new screwdriving tasks at the platform fastly, in less than 20 min, and an online database with screws enables fast shift between different screw types (5 mins.).

The presentation of  ‘MYWAI-4-ROBOTICS – Prognostic Robots Maintenance Servitization Platform’ by Mr Fabrizio Cardinali, CEO at Knowhedge S.r.l., Italy, concluded the event. Ensuring proper maintenance of robotic workforces is a key need for manufacturing companies to stay competitive in a continuously growing global market. The  MYWAI 4 ROBOTICS™ demonstrator aims at delivering chip based Artificial Neural Networks (ANNs) trained to detect anomalies and near to fault operational conditions directly at the very edge of robotic workforces. By fine tuning the MYWAI 4 ROBOTICS demonstrator we expect to achieve more than 85% anomaly detection, reducing by 20-30% Mean Time to Repair (MTTR) and increasing % of preventative maintenance tasks completed by due date by 20-30%.