NUMERICAL ANALYSIS OF KEY FACTORS IN SILICON CARBIDE MANUFACTURING PROCESS

2020 ◽  
Author(s):  
Maria Vartanyan ◽  
Mariya Gordienko ◽  
Nikolay Makarov
Keyword(s):  
Silicon Carbide ◽  
Numerical Analysis ◽  
Manufacturing Process ◽  
Key Factors

scholarly journals Finding and optimising the key factors for the multiple-response manufacturing process

2009 ◽  
Vol 47 (9) ◽  
pp. 2327-2344 ◽  
Author(s):  
Jeh-Nan Pan ◽  
Jianbiao Pan ◽  
Chun-Yi Lee
Keyword(s):  
Manufacturing Process ◽  
Multiple Response ◽  
Key Factors

scholarly journals Online Monitoring of Manufacturing Process Based on autoCEP

2017 ◽  
Vol 13 (06) ◽  
pp. 22
Author(s):  
Qu Jing Lei ◽  
Li Shao Bo ◽  
Chen Jing Kun
Keyword(s):  
Association Rules ◽  
Manufacturing Process ◽  
Online Monitoring ◽  
Limiting Factor ◽  
Quality Factors ◽  
Key Factors ◽  
Association Rules Mining ◽  
Manufacturing Enterprises ◽  
Product Manufacturing ◽  
Process Constraints

Complex Event Processing (CEP), which can identify patterns of interest from a large number of continuous data steam, is becoming more and more popular in manufacturing process monitoring. CEP rules are specified manually by domain expert, which is a limiting factor for its application in manufacturing enterprises. How to analysis historical data and automatically generate CEP rules is becoming a challenge research. This paper proposed a model of autoCEP for online monitoring in product manufacturing, which can automatically generate CEP rules based on association rules mining in key processes. First, the key quality factors in manufacturing process were extracted by grey entropy correlation analysis. Then, association rules mining method based on product process constraints was used to find the association rules between key factors and product quality. At last, the extracted rules are algorithmically transformed into CEP rules. The experimental results show the effectiveness and practicability of the proposed method.

An empirical study-based state space model for multilayer overlay errors in the step-scan lithography process

RSC Advances ◽  
2015 ◽  
Vol 5 (126) ◽  
pp. 103901-103906 ◽  
Author(s):  
Fuyun He ◽  
Zhisheng Zhang
Keyword(s):  
Empirical Study ◽  
State Space ◽  
Integrated Circuit ◽  
Manufacturing Process ◽  
Semiconductor Manufacturing ◽  
State Space Model ◽  
Key Factors ◽  
Space Model ◽  
Lithography Process ◽  
Multistage Manufacturing

In semiconductor manufacturing, the multilayer overlay lithography process is a typical multistage manufacturing process; one of the key factors that restrict the reliability and yield of integrated circuit chips is overlay error between the layers.

Nonlinear Comparative Optimization for Biomaterials Wear in Artificial Implant Technology

2019 ◽  
Vol 800 ◽  
pp. 52-59
Author(s):  
Francisco Casesnoves
Keyword(s):  
Experimental Study ◽  
Comparative Analysis ◽  
Manufacturing Process ◽  
Optimization Method ◽  
Additional Constraint ◽  
Objective Data ◽  
Key Factors ◽  
Material Hardness ◽  
Erosion Corrosion ◽  
Specific Material

Today, artificial implants (AI) industry depends strongly on tribological constitution of the material (s) of the implant. Erosion, corrosion, tribocorrosion and biocorrosion are essential factors to determine both functionality and lifetime of the AIs. Histo-Biocompatibility is also an additional constraint, indispensable for implant manufacturing process. The prediction of durability, based on the computational and experimental study of constituents of AI material (s) are key factors to obtain objective data of any AI characteristics. This contribution deals with a computational comparative analysis of materials for hip implants using Archard’s model mainly. Selected hip implant material hardness are Co-Cr alloy and Titanium types. Method is carried out with specific material data, e.g., hardness or wear constants, nonlinear optimization and graphical subroutines. Results presented are both numerical and graphical. Particular interest is focused on application of the 3D Graphical Optimization method.

Numerical Analysis of the Manufacturing Impact on Magnetic Stray Fields at Electromagnetic Actuators

2013 ◽  
Vol 769 ◽  
pp. 27-33 ◽  
Author(s):  
Matthäus Brela ◽  
Hans-Jörg Gebhardt
Keyword(s):  
Magnetic Properties ◽  
Numerical Analysis ◽  
Manufacturing Process ◽  
Magnetic Circuit ◽  
Electromagnetic Actuators ◽  
Magnetic Actuators ◽  
Air Gaps ◽  
Technical Parameters ◽  
Measurement Systems ◽  
Inline Measurement

Abstract. The technical parameters of magnetic actuators, such as electromagnets, resonance actuators, reluctance actuators etc., are determined by the magnetic properties of the materials as well as the manufacturing and the design configuration. Some exemplary defects in electromagnetic actuators due to the manufacturing are heterogeneous magnetic properties of the materials, cracks, defects and parasitic air gaps in and directly around the magnetic circuit. To implement inline measurement systems to characterise defects within the manufacturing process a study has been carried out to display the influences of production impacts on magnetic stray fields outside the magnetic circuit.

Ablated Transformation and Dielectric of SiO2/SiO2 Nanocomposites Dipped with Silicon Resin

2007 ◽  
Vol 336-338 ◽  
pp. 1239-1241 ◽  
Author(s):  
Mao Sheng Cao ◽  
Hai Bo Jin ◽  
Jin Gang Li ◽  
Liang Zhang ◽  
Qiang Xu ◽  
...  
Keyword(s):  
Silicon Carbide ◽  
Phase Transformation ◽  
High Temperature ◽  
Dielectric Properties ◽  
Physical State ◽  
Raw Material ◽  
Key Factors ◽  
Factors Influencing ◽  
Sio2 Nanocomposites ◽  
Silicon Resin

SiO2/ SiO2 nanocomposites dipped with silicon resin was ablated and the physical state and phase transformation were characterized. Trace impurity in raw material and compound obtained by chemical reaction were analyzed. Moreover, the high-temperature dielectric properties were investigated. On the basis of above, it is found that the impurity carbon and silicon carbide are the key factors influencing dielectric properties.

scholarly journals New Business and Operating Models. Optimization of a Blast Furnace in the Steel Industry. Machine Learning as a Process Optimization

2021 ◽  
Author(s):  
Andrés Redchuk ◽  
Federico Walas Mateo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Blast Furnace ◽  
Conceptual Framework ◽  
Manufacturing Process ◽  
Smart Manufacturing ◽  
Key Factors ◽  
Steel Company ◽  
Steel Manufacturing ◽  
New Business

The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Method: The article takes the case of the adoption of machine learning in a steel manufacturing process through a platform provided by a novel Canadian startup, Canvass Analytics. This way the steel company could optimize the process in a blast furnace. The content of the paper includes a conceptual framework on key factors around steel manufacturing and machine learning. Results: This case is relevant for the authors by the way the business model proposed by the startup attempts to democratize Artificial Intelligence and Machine Learning in industrial environments. This way the startup delivers value to facilitate traditional industries to obtain better operational results, and contribute to a better use of resources. Conclusion: This work is focused on opportunities that arise around Artificial Intelligence as a driver for new business and operating models. Besides the paper looks into the framework of the adoption of Artificial Intelligence and Machine Learning in a traditional industrial environment towards a smart manufacturing approach.

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