THOMAS | Mobile dual arm robotic workers with embedded cognition for hybrid and dynamically reconfigurable manufacturing systems
01-10-2016
-31-03-2021
01-10-2016
-31-03-2021
01-10-2016
-30-09-2019
01-10-2016
-30-09-2019
01-10-2016
-30-09-2020
01-11-2017
-28-02-2021
01-10-2017
-31-03-2021
SERENA aims towards the data-driven condition evaluation of machine and production equipment, which through machine learning techniques can provide insight in the remaining useful life of the equipment enabling the avoidance of production stops and thus reducing its overall costs. The combination of data-driven and physics based techniques is envisioned to increase the reliability of the prediction and contribute to a high perfromance production without undesired interruptions.
THe evalaution and assessment of the equipment condition through the predictive manitenance and data analytics of the SERENA project will move towards the preservation of the production equipment in normal workiong conditions ensuring high quality products.
SERENA solutions on predictive maintenance and maintenance-aware scheduling are expected to reduce the overall ratio of cost to perfromance by the on-time scheduling of maintenance operations with the minimum intervention to the production schedule.
Production equipment uses a number of process resources to operate such as water, air, lubricants, other. In time maintenance activities wnabled by the SERENA predictive maintenance platfrom and optimising the scheduling of maintenance operations can have a significant impact on the consumption of such resources when the equipment is not in proper working condition.
The prediction of maintenance needs of the production equipment thorugh the predictive analytics and scheduling of the SERENA project is expected to reduce the defective workpieces caused by manufacturing equipments not in proper working condition.
The predictive maintenance solutions of the SERENA project are expected to contribute to the sustainability of the production equipment ot proper functional condition thus reducing the energy consumption that is present when machine's are close to the end of their life or in a need of maintenance.
Indutrial equipment not in proper working condition consumes greater quantities of input and operational sources than normal. The SERENA data-driven condition evaluation and prediction of potential failures will enabled the sustainability of the production machines to proper operational condition, thus contributing to reduced process resources.
01-10-2017
-31-03-2021
PROGRAMS solution will allow to gain a “10% increased in-service efficiency through reduced failure rates, downtime due to repair, unplanned plant or production system outages and extension of component life.”
•reduced failure rates:All these aspects will reduce the costs related to maintenance activities, thus increasing the sustainability of the production process.
Precise determination of the RUL of components will allow to replace them before their status degrades production equipment performances beyond unacceptable levels.
Reducing failure rates and production equipment unavailability will improve factory productivity.
PROGRAMS solutions will allow a more widespread adoption of predictive maintenance as a result of the demonstration of more accurate, secure and trustworthy techniques at component, machine and system level. In fact one of the biggest obstacles is getting people to change long-held maintenance practices. PROGRAMS will:
PROGRAMS solution will increase accident mitigation capability”
01-10-2017
-31-03-2021
01-10-2017
-30-09-2020
01-05-2018
-31-10-2021
01-07-2015
-31-07-2018
01-06-2018
-31-05-2021
01-06-2018
-31-05-2021
01-01-2018
-01-01-2021
01-05-2017
-31-10-2020
01-06-2016
-30-09-2019
01-10-2018
-31-03-2022
Cobots and in general HRC solutions, which can be used for multiple functions due to their flexibility in deployment, offer higher returns on investment and faster payback, compared to legacy industrial robots. Moreover, cobots’ safety features and ease of use make integration and implementation less costly than the traditional industrial robots. In high-cost countries, having skilled human workers engaged in suitable configured manufacturing, assembly, finishing and inspection operations side by side with cobots is one of the most cost- effective ways to leverage the unique value-adding capabilities of a skilled human workforce
ROSSINI develops a Design Level that allows users to follow and evaluate process designs on multiple dimensions, where job quality and related metrics are the primary outcomes together with productivity, product quality and cost
The joint action of the dynamic scheduler and of the dynamic planner can lead to a reduction of up to 45% of the overall task execution time
The workcells employing the ROSSINI human-robot mutual understanding framework can increase production flexibility by 5%.
HRC solutions make it possible to integrate production machinery, warehousing systems and production facilities into single human-centred cyber-physical systems. As such, the traditional frontier between the production and logistical tasks of manufacturing can be expected to become increasingly interconnected. Lastly, an increased uptake of HRC leads to a change in the robotics value chain itself. Suppliers, integrators and users are bound to collaborate more intensively which is already leading to new business models such as rental/leasing agreements, pay-on-production, predictive maintenance, etc.
ROSSINI delivers high performance HRC workcells, combining the safety of traditional cobots with the working speed and payloads of industrial robots, capable of optimising task execution. This triggers manufacturers’ investment in HRC technology, increasing European factories productivity and thus competitiveness versus low-cost manufacturers.
ROSSINI is expected to unleash new market opportunities for the consortium industrial partners, resulting in a total yearly turnover of worth 125M€ by 2027
ROSSINI ambition is to develop a framework for Human-Robot Mutual Understanding in collaborative operations which will incorporate a human-centred process design level to address and account for human factors like job quality, user experience, trust, feeling of safety, and liability, in the early design stages.
The Rossini Smart and Safe Sensing System (RS4) combine information from several different customised sensing technologies (Vision, Laser Scanning, Radar, Mat, etc.) in order track not only the position but also the speed of each operator and object in the scene, thus ensuring operators’ safety. Moreover, the use of cobots reduces the amount of working hours spent in physical working thus shifting the workforce away from more physically laborious tasks, towards those of assembly, programming etc. In this way, jobs could become more interesting given their need for higher levels of creativity, problem solving and decision making, definitively resulting in an improved job satisfaction.
01-09-2018
-31-08-2022
01-10-2018
-31-12-2021
01-01-2019
-31-07-2022
Concerning the ecological and economic operation of a factory, data analytics tools in combination with simulation approaches can contribute to improved throughput, bottleneck-reduction, or both for the production line. Through the optimization of the processes, production execution on organization and logistic level can be optimized by reducing the amount of material within the system, the lead times, or both.
improve false positive rate by 20%. Measured as false positives rate, actual value is considered confidential.
Quality- Fall Off Rate, from 95% (as is) to 98,5% (to be)
Improve OEE (A) from 80% (as is) to 87% (to be)
Deviation on cycle-time, from 98% (as is) to 99% (to be)
Sintef delivered information to put “soft” part of organization also in daily management structure. Nowadays the KPI’s are hard technical related. Other topic is to use digital tools for operator- whiteboard sessions. People claim they are more digital oriented at home compared to work floor.
Stakeholder-training Logbook: No results obtained yet, as the implementation is not far enough to train stakeholders.
Improve OEE from 75% (as is) to 85% (to be)
Improve OEE (A) from 80% (as is) to 87% (to be)
Deviation on cycle-time, from 98% (as is) to 99% (to be)
Quality- Fall Off Rate, from 95% (as is) to 98,5% (to be)
01-01-2019
-31-12-2022
01-10-2018
-31-03-2022
Expected impact: Demonstrating the potential to bring back production to Europe
Improvement of productivity in different assembly tasks:
i.Performing Car Starter Assembly
ii.Windshield visual quality check and preassembly
iii.Performing LCD TV Assembly
iv.Αircraft parts assembly
Expected impact of 15% increase in OECD Job Quality Index through work environment and safety improvement
01-10-2018
-31-03-2023