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“Internet of Things” (IoT) and “Industry 4.0” (I4.0) are buzzwords in the process manufacturing and automation world, with far-reaching benefits understood by most at a high level. But, what relevant and practical data and understanding can I4.0 bring your company in everyday operations, such as monitoring compressed air consumption, process optimization, enabling energy efficiencies, and providing preventative diagnostics?

An artificial bird is introduced which was developed using two new

features in biologically-inspired flight, active torsion and partially linear

kinematics. Active torsion rests on well established theoretical

predictions in unsteady aerodynamics. The concept of partially linear

kinematics is inspired by zoological observations on flying locusts.

When the wings flap upwards, the servomotor for the active torsion

turns the outer wing from a positive angle of incidence within

a short fraction of the flapping period into a negative angle of incidence.

Between the turning points the angle of torsion remains

constant. Numerical calculations confirm the expected benefits

compared to passive torsion.

On a novel pneumatic four-finger gripper with three degrees of freedom per finger we apply reinforcement learning to learn dexter- ous manipulation of objects. In order to reduce the search space, we implemented hierarchical learning on two levels. Low-level learning is used for basic movement primitives like grabbing, lifting or rotation of an object around three cartesian axes, whereas in high-level learning we use the already learned low-level actions to find a policy that ena- bles the gripper to move a target point on the surface of a sphere to the top position in a few seconds. It turns out that Q-learning with a finite state- and action space solves the learning task very well.

Dragonfly flight is unique: Dragonflies can manoeuvre in all directions, glide without having to beat their wings and hover in the air. Their ability to move each of their four wings independently ena- bles them to slow down and turn abruptly, to accelerate swiftly and even to fly backwards. We looked into the mechanics of the dragonfly flight and managed to transfer its flight dynamics into an ultralight flying object: the Bionicüpter. With a wingspan of 63 cm and a body length of 44 cm, the model dragonfly weighs just 175 g. A brushless motor actuates the four wings and is used to alter the flapping fre- quency. Eight servo motors allow the amplitude and the twisting angle of each wing to be changed independently making the Bionicüpter almost as agile and fast as its natural role model. Here we present

how dragonfly flight dynamics can inspire future design of MAVs.

basic design – Ordering data

To develop
a robotic system for a complex task
is a time- consuming process. Merging
methods available today, a new ap- proach for a faster realization of a multi-finger soft robotic hand is presented here. This
paper introduces a robotic hand with four fingers and 12 Degrees of Freedom
(DoFs) using bellow actuators. The hand is generated via Selective
Laser Sintering (SLS), an Additive Manufacturing method. The complex task execution of a specific action,
i.e. the lifting, rotating and precise positioning of a handling- object with this robotic hand, is used to structure
the whole develop- ment process. To
validate reliable functionality of the hand from the beginning, each development
stage is SLS-generated and the targeted task execution is trained
via Reinforcement Learning, a machine learning approach.
Optimization points are subsequently derived
and fed back into the hardware development. With this Concurrent
Engineering strategy a fast development of this robotic
hand is possible. The paper
outlines the relevant key strategies and gives insight into
the design process. At the end, the
final hand with its capabilities is presented and discussed.

Artificial intelligence such as the Festo Automation Experience AX (Festo AX) brings preventive quality assurance, preventative energy management and preventive maintenance within reach in four steps.
Festo AX delivers the modules needed to collect data from systems and machines. Using machine learning, models are trained to detect anomalies, predict errors or downtimes and correct the energy demand.

Functional safety in the process industry is a topic that has very much moved centre stage since IEC 61508 came into force. It is frequently only indicated by the abbreviation SIL. But what exactly is SIL and how can it be used to control process valves?

If electrical and pneumatic subsystems are separate, it can often take a lot of time to construct relatively simple systems in terms of system design, purchasing, logistics, installation and  commissioning. Function integration is the answer here, allowing a saving of time throughout the value creation chain. This White Paper shows you how valve terminals can boost efficiency and productivity throughout your company.

In biotechnology, the content and growth of biomass is an important process variable. There are different ways of quantifying this directly in the reactor. The methods currently used have advantages and disadvantages with regard to optimal process control and costs. This white paper demonstrates how biomass concentration and growth in biotech processes can be calculated using soft sensors and existing measurement signals.