Virtually-Guided Certification With Uncertainty Quantification Applied to Die Casting

2018 ◽  
Author(s):  
Shantanu Shahane ◽  
Soham Mujumdar ◽  
Namjung Kim ◽  
Pikee Priya ◽  
Narayana Aluru ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Product Quality ◽  
Uncertainty Quantification ◽  
Process Parameters ◽  
High Speed ◽  
Prediction Models ◽  
Die Casting ◽  
Stochastic Variation ◽  
Complex Process ◽  
Mechanical Properties Prediction

Die casting is a type of metal casting in which liquid metal is solidified in a reusable die. In such a complex process, measuring and controlling the process parameters is difficult. Conventional deterministic simulations are insufficient to completely estimate the effect of stochastic variation in the process parameters on product quality. In this research, a framework to simulate the effect of stochastic variation together with verification, validation, and uncertainty quantification is proposed. This framework includes high-speed numerical simulations of solidification, micro-structure and mechanical properties prediction models along with experimental inputs for calibration and validation. Both experimental data and stochastic variation in process parameters with numerical modeling are employed thus enhancing the utility of traditional numerical simulations used in die casting to have a better prediction of product quality. Although the framework is being developed and applied to die casting, it can be generalized to any manufacturing process or other engineering problems as well.

Simulations of Die Casting With Uncertainty Quantification

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Shantanu Shahane ◽  
Soham Mujumdar ◽  
Namjung Kim ◽  
Pikee Priya ◽  
Narayana R. Aluru ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Product Quality ◽  
Uncertainty Quantification ◽  
Process Parameters ◽  
High Speed ◽  
Prediction Models ◽  
Die Casting ◽  
Stochastic Variation ◽  
Complex Process ◽  
Mechanical Properties Prediction

Die casting is a type of metal casting in which a liquid metal is solidified in a reusable die. In such a complex process, measuring and controlling the process parameters are difficult. Conventional deterministic simulations are insufficient to completely estimate the effect of stochastic variation in the process parameters on product quality. In this research, a framework to simulate the effect of stochastic variation together with verification, validation, and uncertainty quantification (UQ) is proposed. This framework includes high-speed numerical simulations of solidification, microstructure, and mechanical properties prediction models along with experimental inputs for calibration and validation. Both experimental data and stochastic variation in process parameters with numerical modeling are employed, thus enhancing the utility of traditional numerical simulations used in die casting to have a better prediction of product quality. Although the framework is being developed and applied to die casting, it can be generalized to any manufacturing process or other engineering problems as well.

Numerical simulations of high-speed flows in an axisymmetric ramjet

1988 ◽  
Author(s):  
K. KAILASANATH ◽  
J. GARDNER ◽  
E. ORAN ◽  
J. BORIS
Keyword(s):  
Numerical Simulations ◽  
High Speed ◽  
High Speed Flows

Joint High Speed Sealift (JHSS) Segmented Model Test Data Analysis and Validation of Numerical Simulations

2012 ◽  
Author(s):  
Dominic Piro ◽  
Kyle A. Brucker ◽  
Thomas T. O'Shea ◽  
Donald Wyatt ◽  
Douglas Dommermuth ◽  
...  
Keyword(s):  
Data Analysis ◽  
Numerical Simulations ◽  
Test Data ◽  
Model Test ◽  
High Speed ◽  
Segmented Model

scholarly journals Influence of process parameters on thermal cycle and intermetallic compounds formation in high speed laser weld-brazing of aluminium-steel angle joints

2018 ◽  
Vol 26 ◽  
pp. 690-699 ◽  
Author(s):  
Guillaume Filliard ◽  
Mohamed El Mansori ◽  
Mathieu De Metz-Noblat ◽  
Christian Bremont ◽  
Anthony Reullier ◽  
...  
Keyword(s):  
Intermetallic Compounds ◽  
Process Parameters ◽  
Thermal Cycle ◽  
High Speed ◽  
Laser Weld ◽  
Influence Of Process Parameters ◽  
Steel Angle

scholarly journals Optimization of injection molding process for car fender in consideration of energy efficiency and product quality

2014 ◽  
Vol 1 (4) ◽  
pp. 256-265 ◽  
Author(s):  
Hong Seok Park ◽  
Trung Thanh Nguyen
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Injection Molding ◽  
Product Quality ◽  
Process Parameters ◽  
Optimization Problems ◽  
Computational Cost ◽  
Injection Molding Process ◽  
Molding Process ◽  
Nsga Ii

Abstract Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using nondominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

Influence of Process Parameters and Geometry of the Spraying Nozzle on the Properties of Titanium Deposits Obtained in Wire Arc Spraying

2015 ◽  
Vol 1111 ◽  
pp. 211-216
Author(s):  
Bogdan Florin Toma ◽  
Iulian Ionita ◽  
Diana Antonia Gheorghiu ◽  
Lucian Eva ◽  
Costică Bejinariu ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Process Parameters ◽  
High Speed ◽  
Electrical Current ◽  
Electric Arc ◽  
Arc Spraying ◽  
Oxide Content ◽  
Spray Parameters ◽  
Influence Of Process Parameters ◽  
Wire Arc Spraying

Influence of the process parameters and geometry of the spraying nozzle on the properties of titanium deposits obtained in wire arc spraying. Wire arc spraying is a process in which through minor modifications of the spray parameters, they can have a major impact on the coatings properties. In this paper there is presented a study on the influence of process parameters and fluid dynamics of the atomization gas on the properties of titanium deposits (14T - 99.9% Ti). For this there were used three different frontal spraying nozzles, having different geometries, and were varied the spraying gas pressure and the electrical current on three levels. There were evaluated the particles velocity, coating density, chemical composition and characteristic interface between deposition and substrate. Obviously, the high speed of the atomization gas determinate the improving of all properties, but in the same time increased the oxide content in the layer. However, the oxidation can be drastically reduced if the melting and atomization of the wire droplets is produced at the point of formation of the electric arc, and the spraying jet is designed to constrain the electric arc. The assessment of deposits adherence allowed the observation of process parameters that contribute to its improvement.

scholarly journals Cutting Force Prediction Models by FEA and RSM When Machining X56 Steel with Single Diamond Grit

Micromachines ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 326
Author(s):  
Lan Zhang ◽  
Xianbin Sha ◽  
Ming Liu ◽  
Liquan Wang ◽  
Yongyin Pang
Keyword(s):  
Cutting Force ◽  
Coefficient Of Friction ◽  
High Speed ◽  
Prediction Models ◽  
Pipeline Steel ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Force Prediction ◽  
Force Prediction ◽  
Diamond Wire Saw

In the field of underwater emergency maintenance, submarine pipeline cutting is generally performed by a diamond wire saw. The process, in essence, involves diamond grits distributed on the surface of the beads cutting X56 pipeline steel bit by bit at high speed. To find the effect of the different parameters (cutting speed, coefficient of friction and depth of cut) on cutting force, the finite element (FEA) method and response surface method (RSM) were adopted to obtain cutting force prediction models. The former was based on 64 simulations; the latter was designed according to DoE (Design of Experiments). Confirmation experiments were executed to validate the regression models. The results indicate that most of the prediction errors were within 10%, which were acceptable in engineering. Based on variance analyses of the RSM models, it could be concluded that the depth of the cut played the most important role in determining the cutting force and coefficient the of friction was less influential. Despite making little direct contribution to the cutting force, the cutting speed is not supposed to be high for reducing the coefficient of friction. The cutting force models are instructive in manufacturing the diamond beads by determining the protrusion height of the diamond grits and the future planning of the cutting parameters.

Machine Learning Acoustic Emission Based Monitoring of Cold Forging for Smart Manufacturing: A Review

2021 ◽  
Vol 2 (3) ◽  
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Product Quality ◽  
High Speed ◽  
High Rate ◽  
Cold Forging ◽  
Smart Manufacturing ◽  
Forming Process ◽  
Cold Forming ◽  
Effective Manner

Cold forging is a high-speed forming technique used to shape metals at near room temperature. and it allows high-rate production of high strength metal-based products in a consistent and cost-effective manner. However, cold forming processes are characterized by complex material deformation dynamics which makes product quality control difficult to achieve. There is no well defined mathematical model that governs the interactions between a cold forming process, material properties, and final product quality. The goal of this work is to provide a review for the state of research in the field of using acoustic emission (AE) technology in monitoring cold forging process. The integration of AE with machine learning (ML) algorithms to monitor the quality is also reviewed and discussed. It is realized that this promising technology didn’t receive the deserving attention for its implementation in cold forging and that more work is needed.

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