scholarly journals Optimisation of Casting Geometries for A356 Alloy Composites Reinforced with Organic Materials using Box-Behnken Design Methodology

2020 ◽  
Vol 7 (2) ◽  
pp. 524-553
Author(s):  
Sunday Oke ◽  
Stephen Chidera Nwafor ◽  
Chris Abiodun Ayanladun
Keyword(s):  
Regression Models ◽  
Real Life ◽  
A356 Alloy ◽  
Dimensional Accuracy ◽  
Transformation Process ◽  
Comprehensive Treatment ◽  
Two Phase ◽  
Box Behnken Design ◽  
Life Circumstance ◽  
Geometrical Features

In an earlier article, the central composite design was applied to the determination of geometrical features of casts in a two-phase transformation process to produce the wheel covers of automobiles whereby the A356 alloy is reinforced with organic substances for composite property enhancement. This article reexamines the assumptions in that circumstance to revise and expand the optimisation through the response surface methodology to a new method, Box-Behnken design (BBD), to facilitate a comprehensive treatment of the sand casting product parameters. Casting geometrical optimisation can be modelled to involve lengths, breadths, widths, heights, densities of casts and weight loss, varied at three discrete levels. The parameters are translated into codes (–1,0,1) with specified actual, minimum and maximum values. The framework, validated by published literature data, indicates its feasibility in a real-life circumstance. This article assessed the effects of the casting geometry parameters on the responses. Besides, it examined the accuracy of the parameters to predict in the regression models deployed. It was concluded that the BBD and the regression models are adequate and predict correctly. The BBD can be applied by composite developers to improve casting dimensional accuracy and economics.

scholarly journals Geometrical Parametric Optimisation of A356 Alloy Composite in a Two-Stage Casting Process for Automobile Wheel Covers using Response Surface Methodology

2020 ◽  
Vol 7 (1) ◽  
pp. 457-478 ◽  
Author(s):  
Sunday Oke ◽  
Stephen Chidera Nwafor ◽  
Chris Abiodun Ayanladun
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Regression Models ◽  
Predictive Accuracy ◽  
A356 Alloy ◽  
Casting Process ◽  
Sand Casting ◽  
Phase Method ◽  
Technical Solution ◽  
Two Phase

In recent years, novel products from out–of–use A356 alloy engine components are increasingly produced for the automobile industries. Despite being a promising method the sand casting of these products reveals an inadequately understood cast geometry phenomenon for the process. At present, there is no technical solution to the optimisation of cast geometries for A356 alloy reconfigured into composites through organic matter reinforcements. This paper models and analyse sand casting process product geometries in a two–phase method. It utilises the response surface methodology with data on inputs and outputs to create the regression. Volume and density of the first casting process and the weight loss were evaluated for the various groupings of casting process variables, including length, weight, height, width of product for the first casting, weight, length, breadth of the product for the second casting, and the total weight of organic materials. The input and output associations were established in two models of regression analysis representing the central composite design, CCD. The influences of the cast geometrical variables on the evaluated responses were analysed. Furthermore, the predictive accuracy of the two regression models was evaluated. Results revealed that the applied CCD and the regression models reveals statistical adequacy and are competent to predict accurately.

In Implementing a Metal 3D AM Machine

2018 ◽  
Vol 786 ◽  
pp. 356-363
Author(s):  
Tero Jokelainen ◽  
Kimmo Mäkelä ◽  
Aappo Mustakangas ◽  
Jari Mäkelä ◽  
Kari Mäntyjärvi
Keyword(s):  
Additive Manufacturing ◽  
Selective Laser Melting ◽  
Dimensional Accuracy ◽  
Acceptance Test ◽  
Aisi 316L ◽  
Laser Melting ◽  
Geometrical Accuracy ◽  
Machine Performance ◽  
Geometrical Features ◽  
Made In

Additive Manufacturing (AM) does not yet have a standardized way to measure performance. Here a AM machines dimensional accuracy is measured trough acceptance test (AT) and AM machines capability is tested trough test parts. Test parts are created with specific geometrical features using a 3D AM machine. Performance of the machine is then evaluated trough accuracy of test parts geometry. AM machine here uses selective laser melting (SLM) process. This machine has done Factory acceptance test (FAT) to ascertain this machine ́s geometrical accuracy with material AISI 316L. Manufacturer promises accuracy of ±0.05 mm. These parts are used as comparison to AT parts made in this study. After installation two AT parts are manufactured with AM machine. One with AISI 316L and one AlSi10Mg. Dimensional accuracy of geometrical features on these parts are then compared to FAT part and to one another. Machines capability is measured trough two test parts done with material AlSi10Mg. Two of the test parts are done at the same time using same model as the FAT. Parts are printed without supports and with features facing same directions. Features of these parts were then evaluated. Another test to find out AM machines capability was to create part consisting of pipes doing 90˚ angle resulting in horizontal and vertical holes. Dimensional accuracy and circularity of holes was measured. Through these tests machines capability is benchmarked.

Mining Weighted Frequent Itemsets without Candidate Generation in Uncertain Databases

2017 ◽  
Vol 16 (06) ◽  
pp. 1549-1579 ◽  
Author(s):  
Jerry Chun-Wei Lin ◽  
Wensheng Gan ◽  
Philippe Fournier-Viger ◽  
Tzung-Pei Hong ◽  
Han-Chieh Chao
Keyword(s):  
Real Life ◽  
Search Space ◽  
Frequent Itemset ◽  
Frequent Itemset Mining ◽  
Uncertain Databases ◽  
Two Phase ◽  
Itemset Mining ◽  
Meaningful Relationships ◽  
Weighted Probability ◽  
Novel Structures

Frequent itemset mining (FIM) is a fundamental set of techniques used to discover useful and meaningful relationships between items in transaction databases. In recent decades, extensions of FIM such as weighted frequent itemset mining (WFIM) and frequent itemset mining in uncertain databases (UFIM) have been proposed. WFIM considers that items may have different weight/importance. It can thus discover itemsets that are more useful and meaningful by ignoring irrelevant itemsets with lower weights. UFIM takes into account that data collected in a real-life environment may often be inaccurate, imprecise, or incomplete. Recently, these two ideas have been combined in the HEWI-Uapriori algorithm. This latter considers both item weights and transaction uncertainty to mine the high expected weighted itemsets (HEWIs) using a two-phase Apriori-based approach. Although the upper-bound proposed in HEWI-Uapriori can reduce the size of the search space, it still generates a large amount of candidates and uses a level-wise search. In this paper, a more efficient algorithm named HEWI-Utree is developed to efficiently mine HEWIs without performing multiple database scans and without generating candidates. This algorithm relies on three novel structures named element (E)-table, weighted-probability (WP)-table and WP-tree to maintain the information required for identifying and pruning unpromising itemsets early. Experimental results show that the proposed algorithm is generally much more efficient than traditional methods for WFIM and UFIM, as well as the state-of-the-art HEWI-Uapriori algorithm, in terms of runtime, memory consumption, and scalability.

scholarly journals Constitutive Modelling and Identification of Parameters of 316L Stainless Steel at Cryogenic Temperatures

2014 ◽  
Vol 8 (3) ◽  
pp. 136-140 ◽  
Author(s):  
Maciej Ryś
Keyword(s):  
Phase Transformation ◽  
Nonlinear Response ◽  
316L Stainless Steel ◽  
Parent Phase ◽  
Secondary Phase ◽  
Material Model ◽  
Transformation Process ◽  
Constitutive Modelling ◽  
Model Parameters ◽  
Two Phase

Abstract In this work, a macroscopic material model for simulation two distinct dissipative phenomena taking place in FCC metals and alloys at low temperatures: plasticity and phase transformation, is presented. Plastic yielding is the main phenomenon occurring when the yield stress is reached, resulting in nonlinear response of the material during loading. The phase transformation process leads to creation of two-phase continuum, where the parent phase coexists with the inclusions of secondary phase. An identification of the model parameters, based on uniaxial tension test at very low temperature, is also proposed.

scholarly journals An agent-oriented hierarchic strategy for solving inverse problems

2015 ◽  
Vol 25 (3) ◽  
pp. 483-498 ◽  
Author(s):  
Maciej Smołka ◽  
Robert Schaefer ◽  
Maciej Paszyński ◽  
David Pardo ◽  
Julen Álvarez-Aramberri
Keyword(s):  
Inverse Problems ◽  
Computational Cost ◽  
Real Life ◽  
Engineering Problem ◽  
Adaptive Finite Element Method ◽  
Adaptive Finite Element ◽  
Two Phase ◽  
Magnetotelluric Data ◽  
Complex Agent ◽  
Evolutionary Technique

Abstract The paper discusses the complex, agent-oriented hierarchic memetic strategy (HMS) dedicated to solving inverse parametric problems. The strategy goes beyond the idea of two-phase global optimization algorithms. The global search performed by a tree of dependent demes is dynamically alternated with local, steepest descent searches. The strategy offers exceptionally low computational costs, mainly because the direct solver accuracy (performed by the hp-adaptive finite element method) is dynamically adjusted for each inverse search step. The computational cost is further decreased by the strategy employed for solution inter-processing and fitness deterioration. The HMS efficiency is compared with the results of a standard evolutionary technique, as well as with the multi-start strategy on benchmarks that exhibit typical inverse problems’ difficulties. Finally, an HMS application to a real-life engineering problem leading to the identification of oil deposits by inverting magnetotelluric measurements is presented. The HMS applicability to the inversion of magnetotelluric data is also mathematically verified.

Modeling of Semi-Solid A356 Alloy under Upsetting Process

2006 ◽  
Vol 116-117 ◽  
pp. 622-625
Author(s):  
M. Shakiba ◽  
Hossein Aashuri
Keyword(s):  
Solid Fraction ◽  
Yield Criterion ◽  
Flow Behavior ◽  
A356 Alloy ◽  
Two Phase ◽  
Thermomechanical Process ◽  
Modeling Process ◽  
High Solid Fraction ◽  
Flow Through ◽  
Semi Solid

The flow behavior of a semi-solid A356 alloy at high solid fraction was studied. The mushy zone was considered as an effective two-phase, so that the solid continuum can be compressible porous media, and the liquid phase interaction with the solid skeleton was of Darcy type. The semi-solid flow through the upsetting test was modeled in ABAQUS finite element method software. The Gurson yield criterion has been developed for the modeling process of the flow behavior of solid porous medium. Specimens were globulized by a thermomechanical process and then were tested for various percentages of upsetting. The distribution of solid fraction along the radius of the specimens at different height reduction showed a good correlation with model prediction.

scholarly journals Do Rescuers’ Physiological Responses and Anxiety Influence Quality Resuscitation under Extreme Temperatures?

2020 ◽  
Vol 17 (12) ◽  
pp. 4241 ◽  
Author(s):  
José Luis Martin-Conty ◽  
Francisco Martin-Rodríguez ◽  
Juan José Criado-Álvarez ◽  
Carmen Romo Barrientos ◽  
Clara Maestre-Miquel ◽  
...  
Keyword(s):  
Room Temperature ◽  
Real Life ◽  
Health Sciences ◽  
Extreme Heat ◽  
Cross Sectional ◽  
La Mancha ◽  
Life Circumstance ◽  
Before And After ◽  
Different Temperatures ◽  
Academic Year

Teaching and training cardiopulmonary resuscitation (CPR) through simulation is a priority in Health Sciences degrees. Although CPR is taught as a simulation, it can still be stressful for the trainees since it resembles a real-life circumstance. The aim of this study was to assess the physiological effects and anxiety levels of health sciences undergraduates when faced with CPR process in different temperatures (room temperature, extremely cold, or extremely warm). This was a descriptive cross-sectional before–after study conducted during the 2018/2019 academic year with 59 students registered in the Faculty of Health Sciences of the Castilla-La Mancha University (UCLM). State Trait Anxiety Inventory (STAI) questionnaires were distributed among the students before and after the CPR simulation. We found greater level of situational anxiety in undergraduates faced with extreme adverse temperature scenarios (extreme heat and cold), especially in conditions of extreme heat compared to controlled environment (at room temperature). We discovered differences regarding sex, in which men scored 6.4 ± 5.55 points (STAI after CPR score) and women scored 10.4 ± 7.89 points (STAI after CPR score). Furthermore, there was less lactate in blood, before and during the event in individuals with anxiety. In addition, beginning in Minute 7, we observed a remarkable decrease (but not significant) in the performance of rescuers with anxiety. Programs targeted at promoting coping mechanisms to reduce anxiety before a critical clinic situation should be implemented in academic training.

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