Deviation compensation in LPBF series production via statistical predeformation and structural pattern analysis

This article proposes two approaches for a tailored geometrical deviation compensation for Laser-Powder-Bed-Fusion production. The deviation compensation is performed by a non-rigid deformation of the manufacturing geometry in each iteration to reduce the geometrical deviations from the target geometry. It is important for geometric compensation approaches to separate deterministic deviations from random scatter, since compensating scatter can result in unstable behaviour. In order to compensate only deterministic deviations two novel approaches for a local estimation of the scatter are successfully introduced and tested using a hybrid model of a series production cycle

Probing Entanglement in Adiabatic Quantum Optimization with Trapped Ions

Adiabatic quantum optimization has been proposed as a route to solve NP-complete problems, with a possible quantum speedup compared to classical algorithms. However, the precise role of quantum effects, such as entanglement, in these optimization protocols is still unclear. We propose a setup of cold trapped ions that allows one to quantitatively characterize, in a controlled experiment, the interplay of entanglement, decoherence, and non-adiabaticity in adiabatic quantum optimization. We show that, in this way, a broad class of NP-complete problems becomes accessible for quantum simulations, including the knapsack problem, number partitioning, and instances of the max-cut problem. Moreover, a general theoretical study reveals correlations of the success probability with entanglement at the end of the protocol. From exact numerical simulations for small systems and linear ramps, however, we find no substantial correlations with the entanglement during the optimization. For the final state, we derive analytically a universal upper bound for the success probability as a function of entanglement, which can be measured in experiment. The proposed trapped-ion setups and the presented study of entanglement address pertinent questions of adiabatic quantum optimization, which may be of general interest across experimental platforms.Comment: v1: 10 pages+appendix, 6 figures. v2: added explanations; accepted in Frontiers in Physics, Research Topic on Useful Quantum Simulator

Feasibility of acoustic print head monitoring for binder jetting processes with artificial neural networks

The clogging of piezoelectric nozzles is a typical problem in various additive binder jetting processes, such as the manufacturing of casting molds. This work aims at print head monitoring in these binder jetting processes. The structure-born noise of piezoelectric print modules is analyzed with an Artificial Neural Network to classify whether the nozzles are functional or clogged. The acoustic data are studied in the frequency domain and utilized as input for an Artificial Neural Network. We found that it is possible to successfully classify individual nozzles well enough to implement a print head monitoring, which automatically determines whether the print head needs maintenance

Data-driven process analysis for iron foundries with automatic sand molding process

This paper proposes a methodological framework to develop a data-driven process control using pure industrial production data from a cast iron foundry, despite the limitation of complete casting traceability. The aim is to help sand foundries to produce good castings. A reference foundry, which produces mainly automotive and oven parts with automatic sand molding and pouring machines, was selected. Past data, where only good castings were produced, were extracted from the database to determine parameter control limits (upper and lower control limits) with the aid of statistical approach. To identify critical process parameters associated with casting defects, process data from the zero and high scrap production batches were systematically compared. This method clearly identified unstable parameters without exact synchronization between inline and part quality data. Molding sand moisture, temperature and compactability, liquidus temperature of the melt, phosphorus content, carbon equivalent and pouring temperature were found to be the critical parameters to be stabilized. Finally, a regression model for predicting and controlling of molding sand moisture and liquidus temperature of the melt was created. The determined boundaries and the models were helpful for the foundry in assisting ongoing production control and correction of process inputs to achieve target casting quality

A method for characterising the influence of casting parameters on the metallurgical bonding of copper and steel bimetals

Traditional casting technology offers two mayor drawbacks towards research activities. On the one hand, time and resources needed for every casting are rather high. The mould has to be able to withstand the high temperatures introduced by the melt and provide cooling for the cast part. Preparation and installation of measuring equipment therefore takes time. Additionally, due to the high mass of the mould when compared to the cast part, parameter variations are rather limited in their resulting effect on the temperature-time profile being one of the most prominent factors regarding cast quality. Especially when pouring by hand, variations in casting times and rates superimpose effects created intentionally. Therefore, a different process was advanced and evaluated, allowing to minimise some of the drawbacks mentioned before. The key idea is to drastically reduce casting size to the dimensions of one specimen and to apply a highly automated production route. As such, a mirror furnace was modified as to allow the processing of melt. Due to the specimens size, an adaption of mechanical testing equipment was performed and evaluated. As an example, copper-iron bimetal specimens were examined by light microscopy, micro hardness testing, nanoindentation as well as tensile and torsion testing. As the results were consistent, the newly introduced method can be applied successfully in casting research. This allows for highly reproducible results, reducing the uncertainty of temperature measurements of a specimen due to the distance between them. The possibility of separating influencing variables like maximum temperature and cooling rate allows an analysis of the casting process, which would require different moulds to do so in traditional casting methods. The next steps will be directed at a broader variety of metals processed and at a direct comparison between the new process route and traditional casting technology

Influence of salt support structures on material jetted aluminum parts

Like most additive manufacturing processes for metals, material jetting processes require support structures in order to attain full 3D capability. The support structures have to be removed in subsequent operations, which increases costs and slows down the manufacturing process. One approach to this issue is the use of water-soluble support structures made from salts that allow a fast and economic support removal. In this paper, we analyze the influence of salt support structures on material jetted aluminum parts. The salt is applied in its molten state, and because molten salts are typically corrosive substances, it is important to investigate the interaction between support and build material. Other characteristic properties of salts are high melting temperatures and low thermal conductivity, which could potentially lead to remelting of already printed structures and might influence the microstructure of aluminum that is printed on top of the salt due to low cooling rates. Three different sample geometries have been examined using optical microscopy, confocal laser scanning microscopy, energy-dispersive X-ray spectroscopy and micro-hardness testing. The results indicate that there is no distinct influence on the process with respect to remelting, micro-hardness and chemical reactions. However, a larger dendrite arm spacing is observed in aluminum that is printed on salt

PyHopper -- Hyperparameter optimization

Hyperparameter tuning is a fundamental aspect of machine learning research. Setting up the infrastructure for systematic optimization of hyperparameters can take a significant amount of time. Here, we present PyHopper, a black-box optimization platform designed to streamline the hyperparameter tuning workflow of machine learning researchers. PyHopper's goal is to integrate with existing code with minimal effort and run the optimization process with minimal necessary manual oversight. With simplicity as the primary theme, PyHopper is powered by a single robust Markov-chain Monte-Carlo optimization algorithm that scales to millions of dimensions. Compared to existing tuning packages, focusing on a single algorithm frees the user from having to decide between several algorithms and makes PyHopper easily customizable. PyHopper is publicly available under the Apache-2.0 license at https://github.com/PyHopper/PyHopper

Analysis of salts for use as support structure in metal material jetting

Material jetting (MJT) is a category of additive manufacturing processes where the build material is deposited in the form of individual droplets. MJT has recently been expanded into the field of metal processing due to a potentially high printing speed at low equipment and raw material cost. For full 3D capability, support structures are needed that have to be removed after the print job. We examine water soluble salts and suitable nozzle materials to realise the printing of molten salt in a MJT process. Here, the wetting characteristics of the melt and nozzle are crucial because pronounced wetting is problematic for the ejection of droplets. A sessile-drop contact angle test stand was set up to evaluate the wetting characteristics of three salts or salt mixtures (NaCl, KCl–NaCl and NaCl–Na2CO3) on six different nozzle materials (various ceramics and graphite), i.e. potential nozzle materials. The results indicate a high wetting tendency of most of the examined samples with the exception of KCl-NaCl on graphite. Application of these materials on a MJT test stand confirm the feasibility of our findings

Binder content and storing conditions of inorganically-bound foundry cores determine the intensity and onset time of gas release in metal casting

Organically-bound foundry cores are substituted by inorganically-bound cores increasingly. This trend is due to regulatory efforts, workplace safety issues, and increasing costs for waste deposits. Changing the binder system reduces the emissions to mostly water vapor, solving health and safety issues. Yet, the difference in the behavior of the gas phase, namely, the condensation potential of water, changes the casting process drastically. In contrast with the continuous generation and discharge of combustion products in the case of organic binders, water accumulates within the foundry core. Only once the cold spots of the core reach boiling temperature noteworthy amounts of vapor are created, increasing the chance for gas defects of the cast parts. Countermeasures have to be taken when designing the core’s geometry. We conducted the following research to improve the understanding of core gas release and its interactions with the foundry core’s binder content and storage conditions. Both binder content and relative humidity during storage were varied in three steps. Their influence on the core gas amount, time of gas generation, and gas permeability of the cores were investigated. The experiments were performed in the institute’s Induction Analysis Furnace and an aluminum melt bath. We found a strong dependency of storage humidity, further increased by increasing binder content on the gas amount and time of the gas release