Escuela Superior de Enseñanzas Técnicas

This document aims to make a retrospective of our work in the Ford research group in collaboration with researchers from the CEU Cardenal Herrera University and the University of Valencia. The research group originated from the doctoral thesis by Eduardo García Magraner and his thesis was directed by Nicolás Montés in 2016. The Mini-terms were formulated for the first time in this thesis. From then on, the research group grew as the mini-terms began to consolidate both industrially and scientifically. At industrial level we were provided with a CDTI (Centre for the Development of Industrial Technology) which made it possible to massify the mini-terms at Ford factory in Valencia and at scientific level we attended different congresses. Especially relevant was ICINCO 2018 since the concept of the mini-terms could be presented to the programme chair of the congress, Oleg Gusikhin, (Global Data Insight & Analytics, Ford Motor Company, United States). His support led to the consolidation of the mini-terms through their standardization within Ford and also the consolidation of the group through the inclusion of the CEU Cardenal Herrera University in the URP (University Research Program). The success of Eduardo García’s doctoral thesis motivated the Foundation for Development and Innovation (FDI) to decide to fund doctoral theses within Ford, financing a thesis in collaboration with the University of Valencia and another one with the CEU Cardenal Herrera University. Moreover, Eduardo García’s thesis motivated the staff of the plant to take the step to carry out doctoral theses, funded by the INNODOCTO programme of the Generalitat Valenciana. Throughout this journey different awards have been won such as the Henry Ford Technology Awards in 2019, the Factories of the Future Awards in 2021, the Global Manufacturing Technical Excellence Award in 2023 and the Angel Herrera Award for the best research work in 2024. Twenty-four communications have been made to congresses, ICINCO being the congress with the highest number of communications. In particular, at ICINCO 2020, one of these articles was selected as the Best Industrial Paper Award. Thirteen articles have been published in indexed journals with an impact index and also three book chapters. This document aims at reviewing the different tools and concepts developed and introduced by the research group as well as trying to define its objective.

In recent days there are many possibilities in develop solutions for industrial manufacturing process thanks to the emerging technology based in Industry 4.0, where one can measure and manage data from an industrial process in real time been able to know more information than ever before from the process. But still having challenges in complex process where monitoring data and give a solution is less intuitive, mostly due to a complex physical definition of the process and manufacturing car body parts in automotive is a clear example. In deep drawing process is common to have variations in the process parameters and they can carry out bad manufactured parts. The cycle time, the robust process and the complex physics in the process are the main problems to obtain feasible information from the process. In the following it is proposed a new methodology to have full knowledge of the process applying the so-called method Hybrid Twin.

Post-processing meshing algorithms are widely used to achieve the desired quality in quadrilateral meshes. Assuming that the mesh quality depends on the distortion and the size error of each of its convex quadrilaterals, deficiencies arise by considering solutions based in minimizing either the distortion or the size error. To solve this undesirable situation, in this paper we propose a new smoothing post-processing meshing algorithm. This procedure provides a good compromise between the distortion and the size of each element in the mesh. It is formulated by using an elasticity-based argument and allows to be implemented either in sequential or parallel form. Moreover, it provides a good quality output compared with some of the usual smoothing post-processing meshing algorithms.

Solutions of partial differential equations can exhibit multiple time scales. Standard discretization techniques are constrained to capture the finest scale to accurately predict the response of the system. In this paper, we provide an alternative route to circumvent prohibitive meshes arising from the necessity of capturing fine-scale behaviors. The proposed methodology is based on a time-separated representation within the standard Proper Generalized Decomposition, where the time coordinate is transformed into a multi-dimensional time through new separated coordinates, each representing one scale, while continuity is ensured in the scale coupling. For instance, when considering two different time scales, the governing Partial Differential Equation is commuted into a nonlinear system that iterates between the so-called microtime and macrotime, so that the time coordinate can be viewed as a 2D time. The macroscale effects are taken into account by means of a finite element-based macro-discretization, whereas the microscale effects are handled with unidimensional parent spaces that are replicated throughout the time domain. The resulting separated representation allows us a very fine time discretization without impacting the computational efficiency. The proposed formulation is explored and numerically verified on thermal and elastodynamic problems.

The present paper aims at analyzing the topological content of the complex trajectories that weeder-autonomous robots follow in operation. We will prove that the topological descriptors of these trajectories are affected by the robot environment as well as by the robot state, with respect to maintenance operations. Most of existing methodologies enabling efficient diagnosis are based on the data analysis, and in particular on some statistical quantities derived from the data. The present work explores the use of an original approach that instead of analyzing quantities derived from the data, analyzes the “shape” of the data, that is, the time series topology based on the homology persistence. We will prove that this procedure is able to extract valuable patterns able to discriminate the trajectories that the robot follows depending on the particular patch in which it operates, as well as to differentiate the robot behavior before and after undergoing a maintenance operation. Even if it is a preliminary work, and it does not pretend to compare its performances with respect to other existing technologies, this work opens new perspectives in considering quite natural and simple descriptors based on the intrinsic information that data contains, with the aim of performing efficient diagnosis and prognosis.

In this paper, we propose a new splitting algorithm for dynamical low-rank approximation motivated by the fibre bundle structure of the set of fixed rank matrices. We first introduce a geometric description of the set of fixed rank matrices which relies on a natural parametrization of matrices. More precisely, it is endowed with the structure of analytic principal bundle, with an explicit description of local charts. For matrix differential equations, we introduce a first order numerical integrator working in local coordinates. The resulting algorithm can be interpreted as a particular splitting of the projection operator onto the tangent space of the low-rank matrix manifold. It is proven to be exact in some particular case. Numerical experiments confirm this result and illustrate the robustness of the proposed algorithm.

Since its emergence in March 2020, the SARS-CoV-2 global pandemic has produced more than 116 million cases and 2.5 million deaths worldwide. Despite the enormous efforts carried out by the scientific community, no effective treatments have been developed to date. We applied a novel computational pipeline aimed to accelerate the process of identifying drug repurposing candidates which allows us to compare three-dimensional protein structures. Its use in conjunction with two in silico validation strategies (molecular docking and transcriptomic analyses) allowed us to identify a set of potential drug repurposing candidates targeting three viral proteins (3CL viral protease, NSP15 endoribonuclease, and NSP12 RNA-dependent RNA polymerase), which included rutin, dexamethasone, and vemurafenib. This is the first time that a topological data analysis (TDA)-based strategy has been used to compare a massive number of protein structures with the final objective of performing drug repurposing to treat SARS-CoV-2 infection.

This paper presents a real-time global path planning method for mobile robots using harmonic functions, such as the Poisson equation, based on the Proper Generalized Decomposition (PGD) of these functions. The main property of the proposed technique is that the computational cost is negligible in real-time, even if the robot is disturbed or the goal is changed. The main idea of the method is the off-line generation, for a given environment, of the whole set of paths from any start and goal configurations of a mobile robot, namely the computational vademecum, derived from a harmonic potential field in order to use it on-line for decision-making purposes. Up until now, the resolution of the Laplace or Poisson equations has been based on traditional numerical techniques unfeasible for real-time calculation. This drawback has prevented the extensive use of harmonic functions in autonomous navigation, despite their powerful properties. The numerical technique that reverses this situation is the Proper Generalized Decomposition. To demonstrate and validate the properties of the PGD-vademecum in a potential-guided path planning framework, both real and simulated implementations have been developed. Simulated scenarios, such as an L-Shaped corridor and a benchmark bug trap, are used, and a real navigation of a LEGO®MINDSTORMS robot running in static environments with variable start and goal configurations is shown. This device has been selected due to its computational and memory-restricted capabilities, and it is a good example of how its properties could help the development of social robots.

In this paper, we introduce a new geometric description of the manifolds of matrices of fixed rank. The starting point is a geometric description of the Grassmann manifold Gr(Rk) of linear subspaces of dimension r < k in Rk, which avoids the use of equivalence classes. The set Gr(Rk) is equipped with an atlas, which provides it with the structure of an analytic manifold modeled on R(k􀀀r) r. Then, we define an atlas for the set Mr(Rk r) of full rank matrices and prove that the resulting manifold is an analytic principal bundle with base Gr(Rk) and typical fibre GLr, the general linear group of invertible matrices in Rk k. Finally, we define an atlas for the setMr(Rn m) of non-full rank matrices and prove that the resulting manifold is an analytic principal bundle with base Gr(Rn) Gr(Rm) and typical fibre GLr. The atlas ofMr(Rn m) is indexed on the manifold itself, which allows a natural definition of a neighbourhood for a given matrix, this neighbourhood being proved to possess the structure of a Lie group. Moreover, the setMr(Rn m) equipped with the topology induced by the atlas is proven to be an embedded submanifold of the matrix space Rn m equipped with the subspace topology. The proposed geometric description then results in a description of the matrix space Rn m, seen as the union of manifoldsMr(Rn m), as an analytic manifold equipped with a topology for which the matrix rank is a continuous map.

Polymorphisms in the PER3 gene have been associated with several human disease phenotypes, including sleep disorders and cancer. In particular, the long allele of a variable number of tandem repeat (VNTR) polymorphism has been previously linked to an increased risk of breast cancer. Here we carried out a combined germline and somatic genetic analysis of the role of the PER3VNRT polymorphism in breast cancer. The combined data from 8284 individuals showed a non-significant trend towards increased breast cancer risk in the 5-repeat allele homozygous carriers (OR = 1.17, 95% CI: 0.97–1.42). We observed allelic imbalance at the PER3 locus in matched blood and tumor DNA samples, showing a significant retention of the long variant (risk) allele in tumor samples, and a preferential loss of the short repetition allele (p = 0.0005). Gene co-expression analysis in healthy and tumoral breast tissue samples uncovered significant associations between PER3 expression levels with those from genes which belong to several cancerassociated pathways. Finally, relapse-free survival (RFS) analysis showed that low expression levels of PER3 were linked to a significant lower RSF in luminal A (p = 3 × 10−12) but not in the rest of breast cancer subtypes.