How to efficiently machine pure Tungsten by turning?

We use pure Tungsten in our equipment, however its hardness and brittleness makes it complex to machine. We are interested in finding the appropriate turning tools (insert, support) and recommended parameters (cutting speed, feed, depth) for turning W.

Is it possible to produce copper-based and Inconel-based beam intercepting parts via AM, whose porosity, thermal and mechanical properties fulfil the application requirements?

Beam intercepting devices require the use of absorbing materials from low density carbon materials, Al alloys to refractory metals such as W. These are subjecteded to high thermal loads (up to 1000s of degrees Celsius, sometimes applied on a nano-second scale), consequent thermal stresses and repetitive operational cycles. Therefore, the properties of the absorbing materials need to be well known and compliant with the requirments, such as low porosity, fatigue life up to 10^7 nominal load cycles and adequate thermo-mechanical properties. Addictive Manufacturing presents an interesting option to obtain optimised designs for better cooling, assembly and integration. However, the final material needs to coop with all the functional requirments and tolerances of high precision beam intercepting devices. We would like to discuss about AM current possibilities and limitations and if it is applicable to our needs.

How to optimize an assembly for Hipping and to choose adequate parameters?

We have been joining multiple dessimiliar materials via Hot Isostatic Pressing, such as Copper,Stainless Steel and refractory metals. For instance, with this technology we achieved great thermal contact (bonded) between SS pipes and a copper based assembly of a beam intercepting device – essential for efficient cooling. Nevertheless, the later exercise required substantial engineering to tune the tolerances of the diferent parts, the HIPing parameters, the HIP capsule design and the post-Hip heat treatments. To ilustrate, in order to achieve good diffusion bonding between the interfaces, a SS capsule was build to ensure a vacuum level of 10^−3 mbar in these interfaces during the HIP. We would like to discuss possible ideas to make simplier assemblies and optimized HIPing cycles with a successfull outcome.

How can we use the perovskite crystal structure for the development of fast particle (high-energy) detectors?

Literature is rich in describing industrial detector applications that exploit the versatile structure of perovskite crystal and their photovoltaic, semiconductor-like and scintillation properties, in particular for detection and characterisation of X-rays and gamma rays, but also to alpha radiation. Perovskite structures has only recently attracted the interest in the community of people developing high-energy particle (MeV – GeV) detectors and it remains a largely unexplored territory. The opto-electrical features and the expected high radiation tolerance of perovskite make them very promising candidates in two different detector applications: electromagnetic calorimeters and particle tracking devices.

Electromagnetic calorimetry would profit from the very fast scintillation response of single perovskite crystals and the large light yield. The configuration would consist of a perovskite-based active detector medium embedded within a heavy converter medium to provoke the electromagnetic showers. The perovskite-based active medium would both have the role of generating scintillation light from the passage of minimum-bias particles, and to convey the light to photodetectors located at the back of the detector assembly. Currently the idea faces two main questions. The first is the scintillation response to minimum-bias particles, which does not seem to have been investigated in detail. The second comes from the small Stokes’ shift in perovskite crystals, which effectively leads to self-absorption and a very short attenuation length in the active medium. Possible solutions involve creating crystals that have larger Stokes’ shift, or to work with Quantum-Dots of perovskite embedded in a radiation-hard medium with very good light transmission.

The efficient mechanism of electron-hole pair creation, high charge-carrier mobility, and low dark current with single crystals, together with direct charge collection of the produced charge, makes it interesting to explore thin-layer perovskite as a potential lower-cost and compact alternative to silicon in high-spatial resolution tracking detectors for high-energy charged particles. Again very limited information is available on the electronic response of perovskite to minimum-ionising particles. Secondly, the optimal configuration and type of electrode bonding for this purpose needs expert advice and in-depth studies.

What infrastructure, software and supporting functions are required to maximize the outcome of a collaborations ecosystem for intelligent process control?

Big Science Facilities have been driven to the state of art by the rich scientific collaborations. This includes the control system themselves and suits of software application supporting machine operation. Collaboration on machine learning for process control has already been established between ESS and DESY. ESS has also taken part in two externally funded projects in this field together with Lund University, Big Science Sweden and two SMEs based in Lund. A third project between LU, DESY and ESS has also been initiated. Thus, applying new findings in computer science, automatic controls, electric and information technology and advanced mathematics to challenges in process controls is mutually beneficial for big science facilities, academia and industry. Inspired by the recently established “Center for Advanced Mathematics for Energy Research Applications (CAMERA)” in the US the question is: How to establish an ecosystem for collaboration on intelligent process control based in Lund?

How can we enhance the quality of control system data for machine learning purposes in order to lower costs for both data handling and computing?

Big science facilities produce large volumes of control system data. This rich mix of data will range from typical industrial process control systems to state-of-the-art control and data acquisition systems. However, a machine learning model can never perform better than the data it is trained on.

How can we enhance understanding of underlying processes in a highly complex and increasingly autonomous machine?

Big Science Facilities have some of the world’s most complex machines. These machines are becoming more and more autonomous, especially with the development in AI and machine learning. This imply that fewer individuals will operate more advanced systems, which will puts higher demand on operators to understand the underlying processes.

4372: Can we develop technologies to simulate a hot cell environment with augmented reality?

4375: Can we improve the workflow inside the hot cell to reduce the amount of waste?

4372: Hot Cell setup in all facilities uses about 80% similar components. However, there are not many software’s or technologies available to simulate the hot cell environment. In the future, can a certain hot cell components be simulated and combined together with others, and sold as one unit?

4375:

4357: How can we adopt industrial robots (Off the shelf) that can be used in an activated environment?

4360: How can we adopt industrial mobile platforms (Off the shelf) that can be used in an activated environment?

Q ESS: How can we make use of autonomous vehicles, robots and devices to enable inspection of areas that people cannot enter?

4357: Normally industrial robots are not designed for the activated surroundings.

4360: Mobile platforms are used to access the activate environment however there most AGVs are not equipped with redundant systems. In case of failure how can one recover it from the activated surroundings using redundant systems. How onboard electronics can be protected?

Q ESS: The neutron instruments have a significant portion of their equipment housed in areas that are off-limits to people due to high radiation levels. In order to access these areas, the source must be turned off and several days to weeks waited whilst the radiation levels drop and shielding is unstacked. The ability to inspect these areas with remote devices able to negotiate the complex environment within the shielded areas would reduce un-necessary down time and help to maximise the availability of the facility.