Crack path selection at the interface of wrought and wire+arc additive manufactured Ti–6Al–4V

Crack propagation deviation tendency in specimens containing an interface between wrought alloy substrate and Wire + Arc Additive Manufacture (WAAM) built Ti–6Al–4V is investigated from the viewpoints of microstructure, residual stress and bi-material system. It is found that a crack initiated at the interface tends to grow into the substrate that has equiaxed microstructure and lower resistance to fatigue crack propagation. Experimental observations are interpreted by finite element modelling of the effects of residual stress and mechanical property mismatch between the WAAM and wrought alloy. Residual stresses retained in the compact tension specimens are evaluated based on measured residual stress in the initial WAAM built wall. Cracks perpendicular to the interface kept a straight path owing to the symmetrical residual stress distribution. In this case the tangential stress in bi-material model is also symmetric and has the maximum value at the initial crack plane. In contrast, cracks parallel to the interface are inclined to grow towards the substrate due to the mode II (or sliding mode) stress intensity factor caused by the asymmetric residual stress field. Asymmetric tangential stress in the bi-material model also contributes to the observed crack deviation trend according to the maximum tangential stress criterion

Machine Learning for Cutting Planes in Integer Programming: A Survey

We survey recent work on machine learning (ML) techniques for selecting cutting planes (or cuts) in mixed-integer linear programming (MILP). Despite the availability of various classes of cuts, the task of choosing a set of cuts to add to the linear programming (LP) relaxation at a given node of the branch-and-bound (B&B) tree has defied both formal and heuristic solutions to date. ML offers a promising approach for improving the cut selection process by using data to identify promising cuts that accelerate the solution of MILP instances. This paper presents an overview of the topic, highlighting recent advances in the literature, common approaches to data collection, evaluation, and ML model architectures. We analyze the empirical results in the literature in an attempt to quantify the progress that has been made and conclude by suggesting avenues for future research.Comment: Accepted in IJCAI 2023 Survey Trac

20 questions on Adaptive Dynamics

Abstract Adaptive Dynamics is an approach to studying evolutionary change when fitness is density or frequency dependent. Modern papers identifying themselves as using this approach first appeared in the 1990s, and have greatly increased up to the present. However, because of the rather technical nature of many of the papers, the approach is not widely known or understood by evolutionary biologists. In this review we aim to remedy this situation by outlining the methodology and then examining its strengths and weaknesses. We carry this out by posing and answering 20 key questions on Adaptive Dynamics. We conclude that Adaptive Dynamics provides a set of useful approximations for studying various evolutionary questions. However, as with any approximate method, conclusions based on Adaptive Dynamics are valid only under some restrictions that we discuss

The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism

Computer vision and other biometrics data science applications have commenced a new project of profiling people. Rather than using 'transaction generated information', these systems measure the 'real world' and produce an assessment of the 'world state' - in this case an assessment of some individual trait. Instead of using proxies or scores to evaluate people, they increasingly deploy a logic of revealing the truth about reality and the people within it. While these profiling knowledge claims are sometimes tentative, they increasingly suggest that only through computation can these excesses of reality be captured and understood. This article explores the bases of those claims in the systems of measurement, representation, and classification deployed in computer vision. It asks if there is something new in this type of knowledge claim, sketches an account of a new form of computational empiricism being operationalised, and questions what kind of human subject is being constructed by these technological systems and practices. Finally, the article explores legal mechanisms for contesting the emergence of computational empiricism as the dominant knowledge platform for understanding the world and the people within it