Jesus Romero
Jesus Romero
Entrepreneur, creating a technology based startup
About
14
Publications
17,574
Reads
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87
Citations
Introduction
My research interests are welding sequence optimization, artificial intelligence and machine learning algorithms for manufacturing, robotics, vision and welding deformation validation
Additional affiliations
July 2017 - March 2018
Joining and Welding Research Institute Osaka University
Position
- PhD Student
Description
- My research topics are about welding sequence optimization, we are going to combine the current research and some fatigue life assessment
May 2016 - August 2016
Position
- Teacher
Description
- Introduction to Mechatronics - Master's degree
December 2012 - December 2013
John Deere Mexico
Position
- Project Engineer welding specialist
Description
- New product development (functional geometry review, design-process-assembly review, launch plan, production line design, plan for every part) • production support • nesting • welding fixture design • weldment prototype tryouts.
Education
September 2008 - September 2011
June 2003 - June 2008
Publications
Publications (14)
Welding Sequence Optimization (WSO) is very effective to minimize the structural deformation, however selecting proper welding sequence leads to a combinatorial optimization problem. State-of-the-art algorithms could take more than one week to compute the best sequence for an assembly of eight weld beads which is unrealistic for the early stages of...
Tensile residual stresses are one of the key factors when it comes to design and performance assessment of welded structures. Low Temperature Transformation (LTT) welding wires have been used recently with the aim of increasing the fatigue strength. On the other hand, several artificial intelligence (AI) and machine learning (ML) algorithms, have d...
This study presents a validation of weldment deformation through a Thermo-Mechanical-Metallurgical (TMM) Finite Element Analysis (FEA) and a non-invasive three dimensional optical scanning procedure. We have developed first, the 3D model of the workpiece through Computer Aided Design (CAD), then, it is manufacture and welding experiment is conducte...
This paper presents a computer vision guided peg hole insertion task conducted by a robot. We mounted two cameras: one capturing top view and the other capturing side view to calibrate the three dimensional co-ordinates of the center position of the peg and hole found in the image to the actual world co-ordinates so that the robot can grab and inse...
This paper reports the development and implementation of a Genetic Algorithm (GA) based on welding sequence optimization in which a structural deformation is computed as a fitness function. Moreover, a thermo-mechanical finite element analysis (FEA) was used to predict deformation. Elitism selection approach has been used to ensure that the three b...
Compared to deformation, residual stress has not been taken into account in the literature when it comes to welding process optimization. It also plays an important role to measure the weld quality. This paper reports the implementation of a multi-objective based Genetic Algorithm (GA) for welding sequence optimization, in which both structural def...
With heightened emphasis to improve the product quality and process efficiency, the welding industry is challenged to consider innovative approaches like artificial intelligence (AI) techniques. In terms of quality, deformation and residual stress are one of the major concerns. It has been proved that the welding sequence has significant effects on...
This paper presents the robot inverse kinematics solution for four Degrees of Freedom (DOF) through Differential Evolution (DE) algorithm. DE can handle real numbers (float, double) which leads more powerful than Genetic Algorithm (GA). We propose a multi-objective fitness function that makes an attempt to minimize the positional error and maximum...
This paper integrates template matching, calibration and triangulation algorithms in an efficient way to automate peg-hole insertion task using a pair of cameras. First we implement a fast template matching (fast correlation based block matching) algorithm for automatically finding the peg and hole using two cameras. We exploit the templates of the...
Accurate classification of robot execution failure during the manufacturing assembly operations guides to automate robot to perform the predefined tasks. In this paper we exploit the statistical transformations of time-series data for the classification of robot execution failure in the context of peg-hole insertion task. The statistical transforma...
Welding deformation plays a negative role in metal joining processes. It greatly impacts industries in several ways such as constraints in the design phase, reworks, quality cost and overall capital expenditure. Welding sequence optimization significantly reduces the welding deformation. Selecting an optimal welding sequence can be considered as a...
Instead of a single pass, welding sequence can minimize the inherent deformation associated with the localized welding heat input. Selecting an optimal welding sequence can be considered as a combinatorial optimization problem with many possible configurations which often make it computationally very expensive. In this paper, we developed and imple...
Questions
Questions (9)
Hello I have a point cloud and I would like to evaluate GD&T features like true position, flatness. etc.
Looking for software similar to Geomagic or Polyworks, etc.
Thanks
Hi, I am trying to identify what phases are present in these pictures, can someone help where can I learn about it?
I will really appreciate if someone can give me comments about these pictures.
Hi!
I want to create a material library for Simufact welding using Jmatpro.
I have the composition and from the literature I got Austenite start temperature and Austenite final temperature. (Attached picture).
Questions:
What would be the austenitisation temperature? (As, Af, or the maximum that my welding process can reach?
what is the difference of using Phases transformed or Total transformed calculation type?
In order to compute the estimated grain size, What do you recommend for a temperature and time?
Hi, I am looking for someone who has that software and wants to collaborate
Hello everyone, I would like to run a welding simulation where phase transformations are taken into account. So deformations and residual stress can be computed accurately after cooling the part down. The software that I am using is Simufact Welding 6.0. I know that material library must contain CCT/TTT diagrams, volume change per transformation strain, solidus and liquidus temperature, etc.
Before running, the software is giving me a warning, where it asks to define the phase fractions by enabling the 'use phase fraction' option in the components properties.
Where the initial conditions are taken?
Otherwise the default value (100% Ferrite) will be used.
When it comes to welding simulation through a finite element analysis, literature usually reports good agreement between simulated structural deformation values and those obtained by a certain measurement method such as CMM or full comparison using a 3D scanner. I'm looking for some table that reports how much does every step contribute to final deformation assessment.
How much accuracy can be achieved by using thermo-mechanical models?
Hi everyone
I want to simulate a fillet weld seam in a T joint using Comsol or Ansys. I know that there are specific software to do that like sysweld or simufact, however i don't have a license of them and the main purpose is to understand how to model an arc welding heat source and the use of transient thermal analysis.
if you have a tutorial please share or if you have some time to go on skype i will really appreciate it.
Hello everyone, I'm going to make some experimental validations of a welding simulation and when it comes to the thermal field, i've found several signal amplifiers and junction compensation IC's on the literature. I would like to know your experience filtering the noise and acquiring accurate data.
additional information:
GMAW process, AISI 304 material. Thermocouple type R.
thanks in advance.
