Smart hybrid process for small series additive manufacturing of high performance composites (SMARTPRO)
Realization period:01.03. 2022 – 28.02. 2025
Leader at TUL:doc. Dr. Ing. Mgr. Jaroslav Hlava
Additive manufacturing and especially 3D print systems are very efficient methods for producing various components on individual basis or in small series. In this case (below the so called break-even point) they are the most cost effective and energy/resource saving method. However, the existing technology has limitations. Surface quality is far from being perfect and there may be differences between the desired and real geometry of the 3D printed components for a variety of reasons. Further, the mechanical strength of these components is limited even if fibre reinforced 3D printing is used. Methods for improving surface quality and/or mechanical properties exist but they usually require additional processing by a different machine or even manual post-processing, which is awkward and slows down the production.
To avoid these drawbacks and limitations while keeping the advantages the SMARTPRO project proposes the following concept. The 3D printing technology will be combined with automated tape laying in one stand-alone robotic cell. This cell includes two cooperating multi-axial robots. There is one robot with 3D printing head and another robot with tape laying head. In this way, the 3D printed component with limited mechanical strength will be strengthened by adding a UD tape. This process is hybrid in the sense that it combines different technologies but at the same time it is one integrated cell the operation of which is coordinated by one control system.
Existing tape laying heads are suitable for large and relatively uncomplicated surfaces. In order to apply tape to highly complex curved surfaces of 3D printed components it will be necessary to develop a miniaturized tape laying head as a first step. Further, it will be necessary to consider that additional degrees of freedom are needed for precise laying of the tape on complex surfaces and especially for draping tapes on undercuts and precise contact force control is necessary in order to guarantee that the tape is laid correctly.
These requirements will be addressed by developing a smart robotic table. This table will provide the additional degrees of freedom and it will include sensors to enable force/distance control. In this way, the objective of correct tape laying will be achieved even if the two robots are industry standard stiff robots.
The path on which the tape is laid will be optimized in order to achieve maximum strength in the directions in which the component will be most loaded. This will enable production of high performance composite components using additive manufacturing methods, while all operations will be integrated in one cell.
The key component of this cell is the control system. Since this cell includes both local controls of individual components as well as global control it is natural to conceive this control system as hierarchic system with several levels. The lowest level will be the contact force/distance control of the smart table coordinated with the path following control of the stiff robotic arms holding the end-effectors. Higher level will be the adaptation of the parameters/structure of the smart table control based on geometry and material of the actual 3D component and further corrected in the real time based on sensor data.
Third level will be the vision based closed loop feedback control of the tape laying process that will guarantee consistent product quality because the tape-laying will be controlled in the real time. The highest level will be the coordinating control of the whole robotic production cell coordinating the operation of the complete production cell.
Control system will use a digital twin of the processed component and machine learning methods both for the purpose of real time adaptation and for the purpose of future operation improvement i.e. if 3D printing or tape laying operation is not performed successfully with one component, the control system will learn on the basis of this and the processing of next component will be autonomously improved.