Designing nanoindentation simulation studies by appropriate indenter choices: Case study on single crystal tungsten

Atomic simulations are widely used to study the mechanics of small contacts for many contact loading processes such as nanometric cutting, nanoindentation, polishing, grinding and nanoimpact. A common assumption in most such studies is the idealisation of the impacting material (indenter or tool) as a perfectly rigid body. In this study, we explore this idealisation and show that active chemical interactions between two contacting asperities lead to significant deviations of atomic scale contact mechanics from predictions by classical continuum mechanics. We performed a testbed study by simulating velocity-controlled, fixed displacement nanoindentation on single crystal tungsten using five types of indenter (i) a rigid diamond indenter (DI) with full interactions, (ii) a rigid indenter comprising of the atoms of the same material as that of the substrate i.e. tungsten atoms (TI), (iii) a rigid diamond indenter with pairwise attraction turned off, (iv) a deformable diamond indenter and (v) an imaginary, ideally smooth, spherical, rigid and purely repulsive indenter (RI). Corroborating the published experimental data, the simulation results provide a useful guideline for selecting the right kind of indenter for atomic scale simulations

Budget-Feasible Mechanism Design for Non-Monotone Submodular Objectives: Offline and Online

The framework of budget-feasible mechanism design studies procurement auctions where the auctioneer (buyer) aims to maximize his valuation function subject to a hard budget constraint. We study the problem of designing truthful mechanisms that have good approximation guarantees and never pay the participating agents (sellers) more than the budget. We focus on the case of general (non-monotone) submodular valuation functions and derive the first truthful, budget-feasible and $O(1)$-approximate mechanisms that run in polynomial time in the value query model, for both offline and online auctions. Prior to our work, the only $O(1)$-approximation mechanism known for non-monotone submodular objectives required an exponential number of value queries. At the heart of our approach lies a novel greedy algorithm for non-monotone submodular maximization under a knapsack constraint. Our algorithm builds two candidate solutions simultaneously (to achieve a good approximation), yet ensures that agents cannot jump from one solution to the other (to implicitly enforce truthfulness). Ours is the first mechanism for the problem where---crucially---the agents are not ordered with respect to their marginal value per cost. This allows us to appropriately adapt these ideas to the online setting as well. To further illustrate the applicability of our approach, we also consider the case where additional feasibility constraints are present. We obtain $O(p)$-approximation mechanisms for both monotone and non-monotone submodular objectives, when the feasible solutions are independent sets of a $p$-system. With the exception of additive valuation functions, no mechanisms were known for this setting prior to our work. Finally, we provide lower bounds suggesting that, when one cares about non-trivial approximation guarantees in polynomial time, our results are asymptotically best possible.Comment: Accepted to EC 201

New insights into the methods for predicting ground surface roughness in the age of digitalisation

Grinding is a multi-length scale material removal process that is widely employed to machine a wide variety of materials in almost every industrial sector. Surface roughness induced by a grinding operation can affect corrosion resistance, wear resistance, and contact stiffness of the ground components. Prediction of surface roughness is useful for describing the quality of ground surfaces, evaluate the efficiency of the grinding process and guide the feedback control of the grinding parameters in real-time to help reduce the cost of production. This paper reviews extant research and discusses advances in the realm of machining theory, experimental design and Artificial Intelligence related to ground surface roughness prediction. The advantages and disadvantages of various grinding methods, current challenges and evolving future trends considering Industry-4.0 ready new generation machine tools are also discussed

Budget-feasible mechanism design for non-monotone submodular objectives: Offline and online

The framework of budget-feasible mechanism design studies procurement auctions where the auctioneer (buyer) aims to maximize his valuation function subject to a hard budget constraint. We study the problem of designing truthful mechanisms that have good approximation guarantees and never pay the participating agents (sellers) more than the budget. We focus on the case of general (non-monotone) submodular valuation functions and derive the first truthful, budget-feasible and O(1)-approximation mechanisms that run in polynomial time in the value query model, for both offline and online auctions. Since the introduction of the problem by Singer [40], obtaining efficient mechanisms for objectives that go beyond the class of monotone submodular functions has been elusive. Prior to our work, the only O(1)-approximation mechanism known for non-monotone submodular objectives required an exponential number of value queries. At the heart of our approach lies a novel greedy algorithm for non-monotone submodular maximization under a knapsack constraint. Our algorithm builds two candidate solutions simultaneously (to achieve a good approximation), yet ensures that agents cannot jump from one solution to the other (to implicitly enforce truthfulness). Ours is the first mechanism for the problem where-crucially-the agents are not ordered according to their marginal value per cost. This allows us to appropriately adapt these ideas to the online setting as well. To further illustrate the applicability of our approach, we also consider the case where additional feasibility constraints are present, e.g., at most k agents can be selected. We obtain O(p)-approximation mechanisms for both monotone and non-monotone submodular objectives, when the feasible solutions are independent sets of a p-system. With the exception of additive valuation functions, no mechanisms were known for this setting prior to our work. Finally, we provide lower bounds suggesting that, when one cares about non-trivial approximation guaran

Machine learning model of acoustic signatures: towards digitalised thermal spray manufacturing.

Thermal spraying, an important industrial surface manufacturing process in sectors such as aerospace, energy and biomedical, remains a skill intensive process often involving multiple trial runs impacting the yield. The core research challenge in digitalisation of the thermal spraying process lies in instrumenting the manufacturing platform, as the process includes harsh conditions such as UV rays, high-plasma temperatures, dusty chemical environments and inaccessibility of the spray booth. This paper introduces a novel application of machine learning to the acoustic emission spectra of thermal spraying. By transitioning from the amplitude-time domain to a Fourier-transformed frequency-time domain, it is possible to predict anomalies in real-time - a crucial step towards sustainable material and manufacturing digitalisation. Our experimental results also indicate that this method is suitable for industrial applications, by generating useful data that can be used to develop Visual Geometry Group (VGG) transfer learning models to overcome the traditional limitations of convoluted neural networks (CNN)

Atomistic investigation on the structure-property relationship during thermal spray nanoparticle impact

AbstractDuring thermal spraying, hot particles impact on a colder substrate. This interaction of crystalline copper nanoparticles and copper substrate is modeled, using MD simulation. The quantitative results of the impacts at different velocities and temperatures are evaluated using a newly defined flattening aspect ratio. This ratio between the maximum diameter after the impact and the height of the splat increases with increasing Reynolds numbers until a critical value is reached. At higher Reynolds numbers the flattening aspect ratio decreases again, as the kinetic energy of the particle leads to increasing substrate temperature and, therefore, decreases the substrate resistance. Thus, the particle penetrates into the substrate and deforms less

Resilient and agile engineering solutions to address societal challenges such as coronavirus pandemic

The world is witnessing tumultuous times as major economic powers including the US, UK, Russia, India, and most of Europe continue to be in a state of lockdown. The worst-hit sectors due to this lockdown are sales, production (manufacturing), transport (aerospace and automotive) and tourism. Lockdowns became necessary as a preventive measure to avoid the spread of the contagious and infectious “Coronavirus Disease 2019” (COVID-19). This newly identified disease is caused by a new strain of the virus being referred to as Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS CoV-2; formerly called 2019-nCoV). We review the current medical and manufacturing response to COVID-19, including advances in instrumentation, sensing, use of lasers, fumigation chambers and development of novel tools such as lab-on-the-chip using combinatorial additive and subtractive manufacturing techniques and use of molecular modelling and molecular docking in drug and vaccine discovery. We also offer perspectives on future considerations on climate change, outsourced versus indigenous manufacturing, automation, and antimicrobial resistance. Overall, this paper attempts to identify key areas where manufacturing can be employed to address societal challenges such as COVID-19

A multi-targeted approach to suppress tumor-promoting inflammation

Cancers harbor significant genetic heterogeneity and patterns of relapse following many therapies are due to evolved resistance to treatment. While efforts have been made to combine targeted therapies, significant levels of toxicity have stymied efforts to effectively treat cancer with multi-drug combinations using currently approved therapeutics. We discuss the relationship between tumor-promoting inflammation and cancer as part of a larger effort to develop a broad-spectrum therapeutic approach aimed at a wide range of targets to address this heterogeneity. Specifically, macrophage migration inhibitory factor, cyclooxygenase-2, transcription factor nuclear factor-κB, tumor necrosis factor alpha, inducible nitric oxide synthase, protein kinase B, and CXC chemokines are reviewed as important antiinflammatory targets while curcumin, resveratrol, epigallocatechin gallate, genistein, lycopene, and anthocyanins are reviewed as low-cost, low toxicity means by which these targets might all be reached simultaneously. Future translational work will need to assess the resulting synergies of rationally designed antiinflammatory mixtures (employing low-toxicity constituents), and then combine this with similar approaches targeting the most important pathways across the range of cancer hallmark phenotypes