3D-printing: Technology, materials and selected applications

During the last several years, 3D printing has been receiving significant attention in the scientific literature and the popular press.  It has also generated exciting business interests and prospects.  To many, 3D printing is a transformative technology capable of impacting many areas of experimental Science, of Engineering, of Medicine (including dentistry), to development of robotic hands, to tissue engineering, and to fabrication and manufacturing technology (to name but a few).  In addition, 3D printing has often been described as a technology that has the potential to alter rapid prototyping (an early publication in reference [1]) and to influence (if not disrupt) traditional methods of manufacturing.  In fact, the capability of 3D printing to make personalized (i.e., one-of-a-kind) items on-demand may offer the necessary stepping stones for the implementation of the emerging Industry 4.0, and of the over the horizon Industry 5.0 and of Society 5.0.

The aim of this tutorial is to provide an introduction to 3D-printing and to highlight potential future directions.

The content of this tutorial is (roughly) divided into five parts:

1. 3D printing technologies.
2. Materials for 3D-printing.
3. Comparison of technologies, capabilities and materials used for 3D printing.
4. Selected applications of 3D printing, with examples drawn from the Engineering and the Scientific literature (with many) from the author's laboratory.
5. Multi-material 3D printing, for example, for simultaneous printing of metal and plastic or polymers. This will enable development of complex 3D objects having sensors and electronics embedded into the 3D printing structure. A case study we will be discussed and it involves the development of 3D printed robotic/prosthetic hand with intrinsic soft pressure sensors, e-skin and electronics embedded in the hand structure.
6. Future directions of 3D printing technology, the potential impacts of this technology on IoT, on Industry 4.0 and (potentially) on Industry 5.0 and of its capability to influence (if not enable) Society 5.0 approaches will be discussed.  General comments on future societal and economic impacts of 3D printing will be made.

[1]    S. Weagent, L. Li and V. Karanassios, “Rapid prototyping of hybrid, plastic-quartz 3D-chips for battery-operated microplasmas”, Open-access book chapter, Chapter 10, Pages 209-226, InTech publishing (2011), DOI: 10.5772/24994

Magnetic sensor technology has seen an important development in the past 20 years with the advent of numerous low noise and room temperature new sensor technologies. The market is large. Furthermore, numerous biological, geosciences, nondestructive testing, military and space applications require high sensitivity and low noise magnetic magnetometers.

Giant Magneto-Impedance (GMI) magnetometers clearly challenge classical magnetometers as the usual flux gates, now. In that way, this tutorial is about recent advances in GMI magnetometer modeling and development. It will help to understand physical principles, and outline the model of the GMI effect, its equivalent magnetic noise and long-term stability. The state of the art of GMI magnetometer performances compared to the given modeling will be pointed out. It yields to fix present magnetometers’ limitations. The approach wants to be pragmatic and aims scientists and engineers concerned with highly sensitive magnetic measurements.

This tutorial will be useful to workers in the field who wish to learn on highly sensitive GMI magnetometers. It will be focused on the state-of-the-art of GMI magnetometer modeling and development. Fundamentals of a GMI sensor design are reviewed starting from physical equations, material properties, fluctuation dissipation theorem, two-port networks modeling and electronic conditioning. Also, the GMI sensor performances in terms of sensitivity, magnetic equivalent noise, range, linearity and accuracy, bandwidth and long-term stability will be detailed, explained and synthesized based on the modeling and phenomenological description. Magnetometer architecture will be discussed, too. All aspects of these performances will be given and the main parameters (intrinsic or extrinsic) will be pointed out. The different techniques, which permit the sensor performance improvements, will be discussed and explained based on the physical modeling, too. To conclude, state of the art of GMI magnetometer performances will be compared to some highly sensitive vector magnetometers.

OUTLINE

1. Theoretical aspects (Physical equations, Sensor behavior, Noise, Long-term stability)
2. Implementation & Optimization (Bias technic, Electronic conditioning, Modulation techniques, Expected noise and Long-term stability)
3. Magnetometer development (Design, Characterization & Performances)
4. Future trends & Conclusion

The availability of extensive sensing capability and ubiquitous computing has led to numerous technological breakthroughs, from smart gadgets and personal assistants to Internet-of-Things and personalized medicine. In addition to their traditional roles in industry and consumer applications, sensors are nowadays embedded in numerous systems to provide a better understanding of the ambient conditions. In many cases, these sensors are nearing their limits of theoretical performance. Nonetheless, there are many existing and upcoming applications that require more accuracy, precision, or information. The current trend is to take advantage of the available computing power at different levels (Edge, Fog, Cloud) to improve some quality metric(s) of the measurements on parameters of interest. The available signal processing algorithms include the traditional techniques such as filtering but go well beyond them to not only produce numbers but also understandings of the environment.

Objectives

The aim of this course is to provide the audience with the basic understanding of techniques that can be used to improve the quality of data collected by sensors or to gain additional knowledge from sensors. The discussion will have the following components:

1. Introduction to sensors and measurements (20%):
   1. Measurement of physical and chemical quantities
   2. Basic transduction principles and common interfaces
   3. Noise and nonidealities in measurements
2. Sensor fusion and Kalman filtering (20%):
   1. Optimal method to combine signals from similar sensors
   2. Kalman filter for sensor fusion and noise reduction
3. Machine learning for sensors (60%):
   1. Continuous models (i.e., regression) for linear and nonlinear sensor outputs
   2. Algorithms for discrete models for sensor output (i.e., classification), including k nearest neighbors, support vector machines, logistic regression, and neural networks

Prerequisites

Basic knowledge of electrical and mechanical measurements; Elementary statistics; Basic system theory; Introductory linear algebra.

Required tools

Lecture slides will be printed and provided to the attendees. Sample datasets and codes for the material covered in the course will be provided to the attendees as download links (will require Matlab).