Search results for "Electron"
showing 10 items of 24810 documents
Selecting between CNC milling, robot milling and DMLS processes using a combined AHP and fuzzy approach
2017
Abstract Recent advancements in manufacturing technology allow now a much wider selection of machining processes. Milling with industrial robots or additive manufacturing could now replace traditional milling performed on CNC machine-tools, for certain applications. This work presents a decision-making process for selecting between CNC milling, robot milling and a process of additive manufacturing (DMLS) for a certain class of parts. The AHP method was used for selecting between the three variants of manufacturing processes. The criteria used for AHP were divided into crisp ones and criteria described by linguistic variables. For the last ones, fuzzy inference systems were built to extract …
Intelligent agents for feature modelling in computer aided design
2017
Abstract CAD modelling can be referred to as the process of generating an integrated multiple view model as a representation of multiple views of engineering design. In many situations, a change in the model of one view may conflict with the models of other views. In such situations, the model of some views needs to be adapted in order to make all models consistent. Thus, CAD models should be capable of adapting themselves to new situations. Recently, agent based technologies have been considered in order to increase both knowledge level and intelligence of real and virtual objects. The contribution of this paper consists in introducing the intelligent agents in intelligent CAD modelling. T…
Model-based Engineering for the Integration of Manufacturing Systems with Advanced Analytics
2016
To employ data analytics effectively and efficiently on manufacturing systems, engineers and data scientists need to collaborate closely to bring their domain knowledge together. In this paper, we introduce a domain-specific modeling approach to integrate a manufacturing system model with advanced analytics, in particular neural networks, to model predictions. Our approach combines a set of meta-models and transformation rules based on the domain knowledge of manufacturing engineers and data scientists. Our approach uses a model of a manufacturing process and its associated data as inputs, and generates a trained neural network model as an output to predict a quantity of interest. This pape…
Experimental Research on the Cutting of Metal Materials by Electrical Discharge Machining with Contact Breaking with Metal Band as Transfer Object
2020
The scientific paper presents practical research carried out by a mixed team of Romanian researchers from universities and the business environment. The research consists in applying the process of cutting metallic materials through electrical discharge machining with contact breaking using a metal band as a transfer object. The research was implemented with the help of a specially designed installation in the laboratory and subsequently all the necessary steps were taken to obtain the patent for it. Various metallic materials were cut using this process, but first of all, high alloy steels. In the global research conducted by the authors, active experimental programs and classic experiment…
Assembly Process Modeling Through Long Short-Term Memory
2021
This paper studies Long Short-Term Memory as a component of an adaptive assembly assistance system suggesting the next manufacturing step. The final goal is an assistive system able to help the inexperienced workers in their training stage or even experienced workers who prefer such support in their manufacturing activity. In contrast with the earlier analyzed context-based techniques, Long Short-Term Memory can be applied in unknown scenarios. The evaluation was performed on the data collected previously in an experiment with 68 participants assembling as target product a customizable modular tablet. We are interested in identifying the most accurate method of next assembly step prediction…
Diode laser welding of ABS: Experiments and process modeling
2009
International audience; The laser beam weldability of acrylonitrile/butadiene/styrene (ABS) plates is determined by combining both experimental and theoretical aspects. In modeling the process, an optical model is used to determine how the laser beam is attenuated by the first material and to obtain the laser beam profile at the interface. Using this information as the input data to a thermal model, the evolution of the temperature field within the two components can be estimated. The thermal model is based on the first principles of heat transfer and utilizes the temperature variation laws of material properties. Corroborating the numerical results with the experimental results, some impor…
Fuzzy Modeling for Uncertain Nonlinear Systems Using Fuzzy Equations and Z-Numbers
2018
In this paper, the uncertainty property is represented by Z-number as the coefficients and variables of the fuzzy equation. This modification for the fuzzy equation is suitable for nonlinear system modeling with uncertain parameters. Here, we use fuzzy equations as the models for the uncertain nonlinear systems. The modeling of the uncertain nonlinear systems is to find the coefficients of the fuzzy equation. However, it is very difficult to obtain Z-number coefficients of the fuzzy equations.
A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision
2020
Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with images acquired during a configuration stage or extracted from 3D reconstructed models of the space. This paper offers an overview of the computer vision based indoor localization domain, presenting application areas, commercial tools, existing benchmarks, and other reviews. It provides a survey of indoor localization research solutions, proposing a new classification based on the configuration stage…
Extreme Learning Machines for Data Classification Tuning by Improved Bat Algorithm
2018
Single hidden layer feed forward neural networks are widely used for various practical problems. However, the training process for determining synaptic weights of such neural networks can be computationally very expensive. In this paper we propose a new learning algorithm for learning the synaptic weights of the single hidden layer feedforward neural networks in order to reduce the learning time. We propose combining the upgraded bat algorithm with the extreme learning machine. The proposed approach reduces the number of evaluations needed to train a neural network and efficiently finds optimal input weights and the hidden biases. The proposed algorithm was tested on standard benchmark clas…
Pipeline Monitoring Architecture Based on Observability and Controllability Analysis
2019
Recently many techniques with different applicability have been developed for damage detection in the pipeline. The pipeline system is designed as a distributed parameter system, where the state space of the distributed parameter system has infinite dimension. This paper is dedicated to the problem of observability as well as controllability analysis in the pipeline systems. Some theorems are presented in order to test the observability and controllability of the system. Computing the rank of the controllability and observability matrix is carried out using Matlab.