Macro and nanoscale wear behaviour of Al-Al 2 O 3 nanocomposites fabricated by selective laser melting

Aluminium-based composites are increasingly applied within the aerospace and automotive industries. Tribological phenomena such as friction and wear, however, negatively affect the reliability of devices that include moving parts; the mechanisms of friction and wear are particularly unclear at the nanoscale. In the present work, pin-on-disc wear testing and atomic force microscopy nanoscratching were performed to investigate the macro and nanoscale wear behaviour of an Al-Al2O3 nanocomposite fabricated using selective laser melting. The experimental results indicate that the Al2O3 reinforcement contributed to the macroscale wear-behaviour enhancement for composites with smaller wear rates compared to pure Al. Irregular pore surfaces were found to result in dramatic fluctuations in the frictional coefficient at the pore position within the nanoscratching. Both the size effect and the working-principle difference contributed to the difference in frictional coefficients at both the macroscale and the nanoscale

Towards using a multi-material, pellet-fed additive manufacturing platform to fabricate novel imaging phantoms

The design freedom afforded by additive manufacturing (AM) is now being leveraged across multiple applications, including many in the fields of imaging for personalised medicine. This study utilises a pellet-fed, multi-material AM machine as a route to fabricating new imaging phantoms, used for developing and refining algorithms for the detection of subtle soft tissue anomalies. Traditionally comprising homogeneous materials, higher-resolution scanning now allows for heterogeneous, multi-material phantoms. Polylactic acid (PLA), a thermoplastic urethane (TPU) and a thermoplastic elastomer (TPE) were investigated as potential materials. Manufacturing accuracy and precision were assessed relative to the digital design file, whilst the potential to achieve structural heterogeneity was evaluated by quantifying infill density via micro-computed tomography. Hounsfield units (HU) were also captured via a clinical scanner. The PLA builds were consistently too small, by 0.2 − 0.3%. Conversely, TPE parts were consistently larger than the digital file, though by only 0.1%. The TPU components had negligible differences relative to the specified sizes. The accuracy and precision of material infill were inferior, with PLA exhibiting greater and lower densities relative to the digital file, across the 3 builds. Both TPU and TPE produced infills that were too dense. The PLA material produced repeatable HU values, with poorer precision across TPU and TPE. All HU values tended towards, and some exceeded, the reference value for water (0 HU) with increasing infill density. These data have demonstrated that pellet-fed AM can produce accurate and precise structures, with the potential to include multiple materials providing an opportunity for more realistic and advanced phantom designs. In doing so, this will enable clinical scientists to develop more sensitive applications aimed at detecting ever more subtle variations in tissue, confident that their calibration models reflect their intended designs

Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation

The additive manufacturing (AM) process is characterised as a high energy-consuming process, which has a significant impact on the environment and sustainability. The topic of AM energy consumption modelling, prediction, and optimisation has then become a research focus in both industry and academia. This issue involves many relevant features, such as material condition, process operation, part and process design, working environment, and so on. While existing studies reveal that AM energy consumption modelling largely depends on the design-relevant features in practice, it has not been given sufficient attention. Therefore, in this study, design-relevant features are firstly examined with respect to energy modelling. These features are typically determined by part designers and process operators before production. The AM energy consumption knowledge, hidden in the design-relevant features, is exploited for prediction modelling through a design-relevant data analytics approach. Based on the new modelling approach, a novel deep learning-driven particle swarm optimisation (DLD-PSO) method is proposed to optimise the energy utility. Deep learning is introduced to address several issues, in terms of increasing the search speed and enhancing the global best of PSO. Finally, using the design-relevant data collected from a real-world AM system in production, a case study is presented to validate the proposed modelling approach, and the results reveal its merits. Meanwhile, optimisation has also been carried out to guide part designers and process operators to revise their designs and decisions in order to reduce the energy consumption of the designated AM system under study

Machine Learning-enabled feedback loops for metal powder bed fusion additive manufacturing

Metal Powder Bed Fusion (PBF) has been attracting an increasing attention as an emerging metal Additive Manufacturing (AM) technology. Despite its distinctive advantages compared to traditional subtractive manufacturing such as high design flexibility, short development time, low tooling cost, and low production waste, the inconsistent part quality caused by inappropriate product design, non-optimal process plan and inadequate process control has significantly hindered its wide acceptance in the industry. To improve the part quality control in metal PBF process, this paper proposes a novel Machine Learning (ML)-enabled approach for developing feedback loops throughout the entire metal PBF process. A categorisation of metal PBF feedback loops is proposed along with a summary of the critical PBF manufacturing data in each process stage. A generic framework of ML-enabled metal PBF feedback loops is proposed with detailed explanations and examples. The opportunities and challenges of the proposed approach are also discussed. The applications of ML techniques in metal PBF process allow efficient and effective decision-makings to be achieved in each PBF process stage, and hence have a great potential in reducing the number of experiments needed, thus saving a significant amount of time and cost in metal PBF production

Digital twin-enabled collaborative data management for metal additive manufacturing systems

Metal Additive Manufacturing (AM) has been attracting a continuously increasing attention due to its great advantages compared to traditional subtractive manufacturing in terms of higher design flexibility, shorter development time, lower tooling cost, and fewer production wastes. However, the lack of process robustness, stability and repeatability caused by the unsolved complex relationships between material properties, product design, process parameters, process signatures, post AM processes and product quality has significantly impeded its broad acceptance in the industry. To facilitate efficient implementation of advanced data analytics in metal AM, which would support the development of intelligent process monitoring, control and optimisation, this paper proposes a novel Digital Twin (DT)-enabled collaborative data management framework for metal AM systems, where a Cloud DT communicates with distributed Edge DTs in different product lifecycle stages. A metal AM product data model that contains a comprehensive list of specific product lifecycle data is developed to support the collaborative data management. The feasibility and advantages of the proposed framework are validated through the practical implementation in a distributed metal AM system developed in the project MANUELA. A representative application scenario of cloud-based and deep learning-enabled metal AM layer defect analysis is also presented. The proposed DT-enabled collaborative data management has shown great potential in enhancing fundamental understanding of metal AM processes, developing simulation and prediction models, reducing development times and costs, and improving product quality and production efficiency

Evaluation of heat transfer at the cavity-polymer interface in microinjection moulding based on experimental and simulation study

YesIn polymer melt processing, the heat transfer coefficient (HTC) determines the heat flux across the interface of the polymer melt and the mould wall. The HTC is a dominant parameter in cooling simulations especially for microinjection moulding, where the high surface to volume ratio of the part results in very rapid cooling. Moreover, the cooling rate can have a significant influence on internal structure, morphology and resulting physical properties. HTC values are therefore important and yet are not well quantified. To measure HTC in micromoulding, we have developed an experimental setup consisting of a special mould, and an ultra-high speed thermal camera in combination with a range of windows. The windows were laser machined on their inside surfaces to produce a range of surface topographies. Cooling curves were obtained for two materials at different processing conditions, the processing variables explored being melt and mould temperature, injection speed, packing pressure and surface topography. The finite element package Moldflow was used to simulate the experiments and to find the HTC values that best fitted the cooling curves, so that HTC is known as a function of the process variables explored. These results are presented and statistically analysed. An increase in HTC from the standard value of 2500 W/m2C to values in the region 7700 W/m2C was required to accurately model the observations.EPSR

Bulk Metallic Glass based Tool-Making Process Chain for Micro- and Nano- Replication

Existing and emerging micro-engineered products tend to integrate a multitude of functionalities into single enclosures/packages. Such functions generally require different length scale features. In practice, devices having complex topographies, which incorporate different length scale features cannot be produced by employing a single fabrication technology but by innovatively, integrating several different complementary manufacturing techniques in the form of a process chain. In order to design novel process chains that enable such function and length scale integration into miniaturised devices, it is required to utilise materials that are compatible with the various component manufacturing processes in such chains. At the same time, these materials should be able to satisfy the functional requirements of the produced devices. One family of materials, which can potentially fulfil these criteria, is bulk metallic glasses (BMGs). In particular, the absence of grain boundaries in BMGs makes them mechanically and chemically homogeneous for processing at all length scales down to a few nanometres. In this context, this research presents an experimental study to validate a novel process chain. It utilizes three complementary technologies for producing a Zr-based BMG replication master for a microfluidic device that incorporates micro and nano scale features. Then, to validate the viability of the fabricated BMG masters, they are utilized for serial replication of the microfluidic device by employing micro-injection moulding

Spatially organizing future genders: an artistic intervention in the creation of a hir-toilet

Toilets, a neglected facility in the study of human relations at work and beyond, have become increasingly important in discussions about future experiences of gender diversity. To further investigate the spatial production of gender and its potential expressions, we transformed a unisex single-occupancy toilet at Uppsala University into an all-gender or ‘hir-toilet’.1 With the aim to disrupt and expose the dominant spatial organization of the two binary genders, we inaugurated the hir-toilet with the help of a performance artist. We describe and analyse internal and external responses thereto, using Lefebvre’s work on dialectics and space. Focusing on how space is variously lived, conceived and perceived, our analysis questions the very rationale of gender categorizations. The results contribute to a renewed critique of binary thinking in the organization of workplaces by extending our understanding of how space and human relations mutually constitute each other

Selective laser melting: the use of metrology to assure process integrity

Selective Laser Melting (SLM) provides the capability to produce complex geometrical shapes and innovative weight reducing parts. Currently, however, there is a need to confirm that the parts being produced are acceptable because the process cannot be fully assured. This is a challenge to companies wishing to use parts constructed via this manufacturing process for safety-critical components