Objectives:
1. Development of a digital platform for designing the Tool Kit dedicated for robotics, inkjet technology, physical electronics and IT communication;
2. Development of the right mechanical set-ups that include robot with sensors and gripping surfaces “sensing task” and robot with inkjet printhead “printing task”;
3. Selection, implementation and adaptation of the appropriate printable electronic materials, equipments and deposition parameters for manufacturing state-of-the-art printed sensor devices and nodes;
4. Elaborating the developed sensors and nodes suitable to 2D substrates for testing and finally to 3D objects;
5. Characterization, adaptation and validation of the printed sensor electronics and nodes;
6. Extraction and postprocessing of the feedback data from sensors to the robotic interface;
7. Machine learning for the robot in accordance to the situation and application complexity data
acquisition, data processing, establishment of routines and suitable algorithms;
8. Advanced multilevel system communication and their integration or compilation.
About TREND project:
The proposed project “TREND” aims at developing a Digital Tool Kit (DTK) for smart robotics that would be relevant for Industry 4.0 & IoTs. The DTK will immensely benefit the commercial production-based EU companies, where integration of smart robot and its advanced automation capabilities will allow sensing early damage, reduce manufacturing wastage & simultaneously facilitates customized products without the struggle to change the entire manufacturing workflow. TREND will focus on the challenges that explicitly describes the inarticulate capability of the industrial pick & place robots i.e. inability to sense product specific processing complexity & execute appropriate process regulation. We want to exploit consortium’s diverse interdisciplinary, but complementing corecompetencies in science & technology, by demonstrating the inline implementation of sensors & multifunctional features to the robot’s hand using advanced IT communications. It would be made possible to smartly detect the product variability, anticipate process complexities & run adapted routines to follow-up manufacturing of different products without changing the production line & with very low ramp-up costs.
TREND project rationale
The overall market changes from mass production towards to small product volumes. The products become more and more individual. So today, the manufactures have the need of production lines which are (inline) flexible for a wide product range and have very short (instant) ramp up times. In an EUbased
industrial environment where products are mass produced constantly in established production lines, it is very difficult to make adjustments or adaptations for changing products. A planned adaption though advanced information technology (IT) and machine learnt artificial intelligence (AI) based technology in today’s world would potentially result into several benefits e.g. value added to the existing product or can mean prevention against a major shutdown/contamination within the entire production line or even can make the entire manufacturing automation and process chain much more smarter. The reason behind these mentioned challenges is the use of typical analogue based manufacturing systems e.g. forming, molding, welding, assembling, picking, placing, looping etc. and utilization of a single product based process chain. In such cases, integration of an external analogue based intermediate process step seems to be either impossible or require series of modifications throughout the production line, thereby costing money and time. On the contrary, inclusion of a digitalized production technique, can easily be integrated and maneuvered, accordingly to the intended changes.
Within the project TREND (Tool Kit for Robotics for Manufacturing Electronic components and Nodes using Digital Fabrication Technologies), we propose to make the production environment much smarter. We intend to put our scientific and technological R&D focus on a smart Industry 4.0 environment, where the production line already includes robotics that pick and place partially finished products/items that are in particular very sensitive to mechanical stress, environmental contamination and physical handling. One of the typical application environments can be the product development and assembling of electronic appliances that include articles e.g. OLED, QLED or high-end thin displays, for the fabrication of an entire display module or console in automotive or aviation sector. These displays typically need extreme care during process handling, especially in terms of sensitivity of mechanical stress and environmental contaminations. An improper mechanical or physical handling of such articles can result into damage of the display and failure of the infotainment console within a car or flight.
Robots are commonly inarticulate and are not able to evaluate by themselves the degree of care and process steps it need to offer for the series of products with different size, kind and degree of sophistication. Therefore, we envisage an Industry 4.0 production line, where the implemented robotics define by themselves the required control for handling the pick and place mechanical sequence within the production line, by taking into account the right in-process mechanical handling, cooling or drying and sterilization or decontamination steps. We intend to integrate some of the relevant sensors (e.g. either pressure, moisture or temperature) and individualized gripping surfaces on the “artificial hand” (grippers & holders) of the robot’s arm. The sensor should detect and send different feedbacks according to the prevailing complexity of processed article and in-situ can communicate with the robot, while the individualized surface would ensure a nondestructive handling. The communication is established
between the process automation program and feedback from the sensors, the robot can potentially be taught (machine learning) to execute the necessary correction steps to either alter the gripping mechanism of the artificial hand or implement adapted actions (e.g. adaptive gripping, temperature management, decontamination etc.) with respect to variation in the handling of various articles.
In continuation to this, we also propose to make one robot uniquely smarter among others by triggering a second robot to print (using inkjet technology) the essential sensor electronic components & nodes (electronic part responsible to process digital data) along with certain multifunctional 3D gripping surface features (e.g. haptics) directly on the artificial hand (treated as 3D object) of the robotic arm. The main novelty within the thematic field of additive manufacturing is the printing of sensors and functional surfaces/layers with high degree of freedom that potentially replaces the integration of conventional rigid sensors and 3D gripping setups, through the support of advanced IT communication and AI technologies. The benefit of using inkjet technology along with robotics is that, they are both seamless digital fabrication technologies, and secondly that they together can contribute towards the state-of-the-art (SOA) development of sensors on an additive manufacturing platform, but with the challenge to go beyond SOA and deposit printed layers with required functional properties, addressed now to the 3D surfaces of the artificial hand. This would consolidate communication between robots, sensors and robots with respect to the already established production line, with the aim to shorten the ramp-up time for changing products and prevent / eliminate / modify / adapt production failures, minimize wastage and promote customization of the final endproducts.