Application of numerical analysis in the design and small-scale intelligent manufacturing process of porous fences

2016 ◽  
Author(s):  
Yizhong Xu ◽  
Mohamad Y. Mustafa ◽  
Muhammad S. Virk ◽  
Rajnish K. Calay
Keyword(s):  
Numerical Analysis ◽  
Manufacturing Process ◽  
Intelligent Manufacturing ◽  
Small Scale

scholarly journals Tensile Tests for the Improvement of Adhesion between Rubber and Steel Layers in Elastomeric Isolators

2020 ◽  
Vol 10 (22) ◽  
pp. 8063
Author(s):  
Margherita Pauletta ◽  
Federico Pinzano ◽  
Giada Frappa ◽  
Gaetano Russo
Keyword(s):  
Manufacturing Process ◽  
Seismic Isolation ◽  
Tensile Tests ◽  
Small Scale ◽  
Shear Tests ◽  
Good Adhesion ◽  
Single Body ◽  
Adhesion Behavior ◽  
Shear Strains ◽  
Direct Tensile

Steel reinforced elastomeric isolators are currently the most used bearings for seismic isolation purposes. The steel reinforcements are cut to the desired shape, sandblasted, cleaned with acid, and coated with bonding compound during the manufacturing process. Then the elastomer and steel layers are stacked in a mold and subjected to vulcanization so that they are glued together and constitute a single body. Good adhesion between the layers is very important for the correct functioning of the device. Adhesion conditions become critical when the isolators are subjected to tensile stresses, which arise under direct tensile actions or large shear strains. To analyze the influence of changes in the manufacturing process on the isolator adhesive behavior, the authors performed tensile tests on square-shaped small-scale specimens rather than expensive shear tests on full-scale isolators. Hence, the adhesion behavior between elastomer and steel layers was investigated through the tensile tests discussed herein. Among the influencing parameters that were considered, it was found that an increase in vulcanization time does not improve the adhesion, but it may actually worsen the capacity of the isolator in terms of strength. Moreover, it was found that using elastomer without an oily component improves the adhesion between the layers and increases the isolator’s dissipative capacity.

Studies in the Numerical Analysis of Complex Rain-Forest Communities: IV. A Method for the Elucidation of Small-Scale Forest Pattern

1969 ◽  
Vol 57 (3) ◽  
pp. 635 ◽  
Author(s):  
W. T. Williams ◽  
G. N. Lance ◽  
L. J. Webb ◽  
J. G. Tracey ◽  
J. H. Connell
Keyword(s):  
Numerical Analysis ◽  
Rain Forest ◽  
Small Scale ◽  
Forest Communities

Experimental and numerical analysis of small-scale panels with indented stiffeners

2018 ◽  
Vol 150 ◽  
pp. 7-22 ◽  
Author(s):  
John H. Chujutalli ◽  
Segen F. Estefen ◽  
C. Guedes Soares
Keyword(s):  
Numerical Analysis ◽  
Small Scale

scholarly journals The new design of the wood stove based on the numerical analysis and experimental research

2020 ◽  
Vol 154 ◽  
pp. 04001
Author(s):  
Przemysław Motyl ◽  
Marcin Wikło ◽  
Julita Bukalska ◽  
Bartosz Piechnik ◽  
Rafał Kalbarczyk
Keyword(s):  
Fluid Flow ◽  
Heat Exchange ◽  
Numerical Analysis ◽  
Heat Release ◽  
Experimental Research ◽  
Numerical Study ◽  
Combustion Process ◽  
Small Scale ◽  
Wood Stove ◽  
Wood Stoves

In Europe, especially in Poland, wood-fired stoves remain one of the most popular renewable household heating. The use of wood logs in small-scale units stoves are expected to increase substantially. The work proposes a comprehensive approach to modify the design of wood stoves with heating power up to 20 kW, including design works, simulations, and experimental research. The article also presents the numerical study of a combustion process including fluid flow, chemical combustion reaction, and heat exchange in the wood stove. The retrofit enhanced a more stable heat release from the wood stove, which increased efficiency and reduction of the harmful components of combustion.

scholarly journals Modeling Product Manufacturing Reliability with Quality Variations Centered on the Multilayered Coupling Operational Characteristics of Intelligent Manufacturing Systems

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5677
Author(s):  
Anqi Zhang ◽  
Yihai He ◽  
Xiao Han ◽  
Yao Li ◽  
Xiuzhen Yang ◽  
...  
Keyword(s):  
Product Quality ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Coupling Effect ◽  
Intelligent Manufacturing ◽  
Intelligent Manufacturing Systems ◽  
Operational Characteristics ◽  
Product Manufacturing ◽  
Quality Variation ◽  
Variation Model

For intelligent manufacturing systems, there are many deviations in operational characteristics, and the coupling effect of harmful operational characteristics leads to the variations in quality of the work-in-process (WIP) and the degradation of the reliability of the finished product, which is reflected as a loss of product manufacturing reliability. However, few studies on the modeling of product manufacturing reliability and mechanism analysis consider the operating mechanism and the coupling of characteristics. Thus, a novel modeling approach based on quality variations centered on the coupling of operational characteristics is proposed to analyze the formation mechanism of product manufacturing reliability. First, the PQR chain containing the co-effects among the manufacturing system performance (P), the manufacturing process quality (Q), and the product manufacturing reliability (R) is elaborated. The connotation of product manufacturing reliability is defined, multilayered operational characteristics are determined, and operational data are collected by smart sensors. Second, on the basis of the coupling effect in the PQR chain, a multilayered product quality variation model is proposed by mining operational characteristic data obtained from sensors. Third, an integrated product manufacturing reliability model is presented on the basis of the variation propagation mechanism of the multilayered product quality variation model. Finally, a camshaft manufacturing reliability analysis is conducted to verify the validity of the proposed method. The method proposed in this paper proved to be effective for evaluating and predicting the product reliability in the smart manufacturing process.

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