Session 4 Enabling Technologies III

Wednesday, 11.11.2020, 15:40-17:20 o'clock


Keynote by Prof. MacDonald: 3D Printing of Multi-Functional Structures

15:40-16:20 o'clock

(Paper ID: K2)

3D printing has generally been relegated to fabricating conceptual models and prototypes; however, increasingly, research is now focusing on fabricating functional end-use products. As patents for 3D printing expire, new low cost desktop systems are being adopted more widely and this trend is leading to products being fabricated locally. However, currently the technology is limited in the number of materials used in fabrication and consequently is confined to fabricating simple static structures. For additively manufactured products to be economically meaningful, additional functionalities are required to be incorporated in terms of electronic, electromechanical, electromagnetic, thermodynamic, chemical and optical content. By interrupting the 3D printing and employing complementary manufacturing processes, additional functional content can be included in mass-customized structures.

This presentation will review work in multi-process 3D printing for creating structures with consumer-specific wearable electronics, electromechanical actuation, electromagnetics, propulsion and embedded sensors in soft tooling and even in metal and ceramic structures.

Simon Nicolas Gottwald: Concept of an Assembly Technology for Dies below 150 Micrometers

16:20-16:40 o'clock

(Paper ID: 1102)

In the field of packaging bare electronic components, assembly processes are indispensable. During assembly, components like dies are applied to a carrier substrate and are subsequently contacted. This electrical contact and mechanical fixation is called bonding. The demand for bonded dies increases constantly. Thus modern die bonders need to process more electronic components at faster rates to remain economically feasible. Additonally, progressive miniaturization leads to ever-smaller components. Bonding components with edge length smaller than 150 µm with a precision of ±10 µm evoke problems in the mechanical handling of the dies. For handling the smallest dies and increasing the assembly rate, a new assembly technology is required.

We are currently researching an optical induced, contactless transfer of bare dies. The transfer is triggered with a 8 ns laser impulse at a wavelength of 1064 nm, which dissolves the adhesive layer of the dicing tape, causing a single die to fall and land on carrier substrate.

Our process consists of the following three phases: the application of adhesive onto the substrate, the placement of components into the adhesive and the curing of the adhesive. During curing of the anisotropic conductive adhesive, the electrical contact is also established, though it is not crucial for the assembly technology. The assemblys challgenges are highlighted by the general bonding process. The laser die transfer must successfully detach dies from the wafer foil and attach these onto the carrier substrate in a controlled manner.

Besides handling small dies we want to increase the assembly rate by changing to moving the carrier substrate consistently. A consistent and continuous movement raises various challenges like no mechanical forces are allowed during die transfer. Furthermore maintaining an equivalent good accuracy increases with smaller die dimensions.

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Michael Koerdt: Monitoring of the vacuum infusion process by integrated RFID-Transponders

16:40-17:00 o'clock

(Paper ID: 1106)

In the composite production market in Germany, a greater resource efficiency and higher standards of quality are needed. For the vacuum infusion process, this paper demonstrates the use of RFID transponders as sensors for process monitoring in carbon fiber and glass fiber. Furthermore, we show the possibility of using the RFID transponders as sensors for the resin flow in the fiber layers below the sensor. Summarizing the results, the use of RFID transponders as sensors allows an efficient and process friendly monitoring of the resin behavior during vacuum infusion, and the transponders can be further used in logistics.

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Stefan Bosse: Learning Damage Event Discriminator Functions with Distributed Multi-instance RNN/LSTM Machine Learning - Mastering the Challenge

17:00-17:20 o'clock

(Paper ID: 1171)

Common Structural Health Monitoring systems are used to detect past damages occurred in structures with sensor networks and external sensor data processing. The time of the damage creation event is commonly unknown. Numerical methods and Machine Learning are used to extract relevant damage information from sensor signals that is characterised by a high data volume and dimension. In this work, distributed multi-instance learning applied to raw time-series of sensor data is deployed to predict the event of the occurrence of a hidden damage in a mechanical structure using typical vibrations of the structure. The sensor processing and learning is performed locally on sensor node level with a global fusion of prediction results to estimate the damage location and the time of the damage creation. Recurrent neural networks with a long-short-term memory architecture are considered implementing a damage discriminator function. The sensor data required for the evaluation of the proposed approach is generated by a multi-body physics simulation approximating material properties.

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