A framework for closed-loop supply chains of reusable articles

Reuse practices contribute to the environmental and economical sustainability of production and distribution systems. Surprisingly, reuse closed-loop supply chains (CLSC) have not been widely researched for the moment. In this paper, we explore the scientific literature on reuse and we propose a framework for reusable articles. This conceptual structure includes a typology integrating under the reusable articles term different categories of articles (transportation items, packaging materials, tools) and addresses the management issues that arise in reuse CLSC. We ground our results in a set of case studies developed in real industrial settings, which have also been contrasted with cases available in existing literature.reverse logistics;case studies;closed-loop supply chains;returns managment

On the use of local max-ent shape functions for the simulation of forming processes

In this work we review the opportunities given by the use of local maximum-\ud entropy approximants (LME) for the simulation of forming processes. This approximation can\ud be considered as a meshless approximation scheme, and thus presents some appealing features\ud for the numerical simulation of forming processes in a Galerkin framework.\ud Especially the behavior of these shape functions at the boundary is interesting. At nodes\ud on the boundary, the functions possess a weak Kronecker-delta property, hence simplifying the\ud prescription of boundary conditions. Shape functions at the boundary do not overlap internal\ud nodes, nor do internal shape functions overlap nodes at the boundary. Boundary integrals can be\ud computed easily and efficiently compared to for instance moving least-squares approximations.\ud Furthermore, LME shapes also present a controllable degree of smoothness.\ud To test the performance of the LME shapes, an elastic and a elasto-plastic problem was\ud analyzed. The results were compared with a meshless method based on a moving least-squares\ud approximation

Data-driven correction of models for deformable solids

Unveiling physical laws from data is seen as the ultimate sign of human intelligence. While there is a growing interest in this sense around the machine learning community, some recent works have attempted to simply substitute physical laws by data. We believe that getting rid of centuries of scientific knowledge is simply nonsense. There are models whose validity and usefulness is out of any doubt, so try to substitute them by data seems to be a waste of knowledge. While it is true that fitting well-known physical laws to experimental data is sometimes a painful process, a good theory continues to be practical and provide useful insights to interpret the phenomena taking place. That is why we present here a method to construct, based on data, automatic corrections to existing models. Emphasis is put in the correct thermodynamic character of these corrections, so as to avoid violations of first principles such as the laws of thermodynamics. These corrections are sought under the umbrella of the GENERIC framework [M. Grmela and H. Ch. Oettinger, Dynamics and thermodynamics of complex fluids. I. Development of a general formalism. Phys. Rev. E 56, 6620, 1997], a generalization of Hamiltonian mechanics to non-equilibrium thermodynamics. This framework ensures the satisfaction of the first and second laws of thermodynamics, while providing a very appealing context for the proposed automated correction of existing laws. In this work we focus on solid mechanics, particularly large strain (visco-) hyperelasticity

Empowering materials processing and performance from data and AI

[No abstract available

Scientific machine learning for coarse-grained constitutive models

We present here a review on some of our latest works concerning the development of thermodynamics-aware machine learning strategies for the data-driven construction of constitutive models. We suggest a methodology constructed upon three main ingredients. (i) the employ of manifold learning strategies to unveil the true dimensionality of data, thus pointing out the need for the definition of “internal” variables, different of the experimental ones. (ii) the process will be described by the so-called General Equation for the Non-Equilibrium Reversible-Irreversible Coupling (GENERIC). (iii) the precise form of the GENERIC terms will be unveiled by regression of data

Modeling the Cost of International Trade in Global Supply Chains

In a global economy, international trade plays an important role of the economic development. This is especially relevant in emerging markets, where trade could contribute significantly to the economic growth of the country. Many studies have pointed out the relationship between logistics performance and the volume of bilateral trade. Limão and Venables (2001) analyze transport costs, Hummels (2001) analyzes transport time and Hausman et al. (2013) evaluate the impact of specific improvements in logistics performance in terms of time, cost and reliability (variability in time) on increased trade. International Trade ads complexity as goods move across borders where are subject to import and export activities that increase lead times and variability on financial and physical flows (e.g. more documents per trade transaction, more signatures per trade transaction, export clearance, and customs inspection). Also, these global supply chains often involve more actors and agencies that support the trade process such as inspection agencies and custom brokers. Surveys aimed at calculating these costs suggest that they may range from 2% to 15% of the value of traded goods. This paper provides a general framework to model the impact of international trade of a global supply chain. A cost function is proposed for the buyer, the seller and the upstream suppliers that explicitly refers to the additional elements of international trade. The model is applied to compare the impact of different Incoterms rules (see section 3.2.1) in an International Trade taking into account the total cost of the supply chain Blanco, E.E. and Ponce-Cueto, E. – MIT Center for Transportation & Logistics – March 2015 2 for the main actors, including the buyer (importer) and the seller (exporter). The paper is organized as follows. Section 2 includes a succinct literature review of relevant papers in global trade management, and more specifically a review of those papers that focus on the total cost in global supply chains. Section 3 defines the global supply chain under study and presents the key events in a global trade. A total global trade function is formulated in Section 4, one function cost for buyers and another for upstream sellers. In section 5 the supply chain costs under various trade scenarios are presented and a numerical example is developed in order to illustrate the applicability of the model. Discussion and conclusion are included in section 6

Model and system learners, optimal process constructors and kinetic theory-based goal-oriented design: a new paradigm in materials and processes informatics

Traditionally, Simulation-Based Engineering Sciences (SBES) has relied on the use of static data inputs (model parameters, initial or boundary conditions, ... obtained from adequate experiments) to perform simulations. A new paradigm in the field of Applied Sciences and Engineering has emerged in the last decade. Dynamic Data-Driven Application Systems [9, 10, 11, 12, 22] allow the linkage of simulation tools with measurement devices for real-time control of simulations and applications, entailing the ability to dynamically incorporate additional data into an executing application, and in reverse, the ability of an application to dynamically steer the measurement process. It is in that context that traditional "digital-twins" are giving raise to a new generation of goal-oriented data-driven application systems, also known as "hybrid-twins", embracing models based on physics and models exclusively based on data adequately collected and assimilated for filling the gap between usual model predictions and measurements. Within this framework new methodologies based on model learners, machine learning and kinetic goal-oriented design are defining a new paradigm in materials, processes and systems engineering

On the performances of different nodal integration techniques and their stabilization

Finite element method was successfully applied in the simulation of several forming processes; however, it does not represent an absolute reference point. In fact, large deformation corresponds to a heavy mesh distortion. Powerful rezoning-remeshing algorithms strongly reduce the effects of such a limitation but the computational time significantly increases and additional errors occur. Nodal Integration is a recently introduced technique that allows finite element method to provide reliable results also when meshes becomes distorted in traditional FEMs. Furthermore, volumetric locking problems seem to be avoided using this integration technique instead of other methods such as coupled formulations. Nevertheless, spurious low-energy modes appear due to the nodal averaging of strain. For this reason stabilizing methods application seems to be suitable. What is more, different nodal integration techniques have been proposed, although spurious modes are a common problem. In this paper the performances of three different nodal integration techniques and the effects of a recently introduced stabilization methodology are studied simulating a classical forming process

Parametric numerical solutions of additive manufacturing processes

Additive manufacturing is the more and more considered in industry, however efficient simulation tools able to perform accurate predictions are still quite limited. The main difficulties for an efficient simulation are related to the multiple scales, the multiple and complex physics involved, as well as the strong dependency on the process trajectory. In [21] authors proposed the use of advanced model reduction techniques for performing parametric simulations of additive manufacturing processes, where deposition trajectory, the intensity of the thermal shrinkage and the deposited layers were considered as model parameters. The resulting simulation tool allowed evaluating in real-time the impact of the parameters just referred on the part distortion, and proceed to the required geometrical compensation. In the present work we address the use of that parametric solution with three different purposes: (i) evaluating the parameters leading to the minimal part distortion; (ii) evaluating the solution sensitivity to the different parameters, and in particular to the ones related to the deposition trajectory; and (iii) propagating the uncertainty related to the intensity of the thermal shrinkage