Open Access. Powered by Scholars. Published by Universities.®
Operations Research, Systems Engineering and Industrial Engineering Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Industrial Engineering (81)
- Physical Sciences and Mathematics (14)
- Operational Research (13)
- Environmental Sciences (7)
- Aerospace Engineering (5)
-
- Electrical and Computer Engineering (5)
- Computer Sciences (4)
- Electrical and Electronics (4)
- Medicine and Health Sciences (4)
- Oil, Gas, and Energy (4)
- Civil and Environmental Engineering (3)
- Computer Engineering (3)
- Mechanical Engineering (3)
- Statistics and Probability (3)
- Applied Mathematics (2)
- Discrete Mathematics and Combinatorics (2)
- Environmental Engineering (2)
- Health and Medical Administration (2)
- Mathematics (2)
- Robotics (2)
- Software Engineering (2)
- Sustainability (2)
- Architecture (1)
- Art and Design (1)
- Arts and Humanities (1)
- Biomedical (1)
- Biomedical Devices and Instrumentation (1)
- Biomedical Engineering and Bioengineering (1)
- Keyword
-
- Optimization (14)
- Genetic Algorithms (6)
- Industry 4.0 (5)
- Simulation (5)
- Design of Experiments (3)
-
- Manufacturing (3)
- Renewable (3)
- Smart Manufacturing (3)
- Supply Chain (3)
- Automation (2)
- Combinatorics (2)
- Constraints (2)
- Digital Twin (2)
- Digitization (2)
- Energy (2)
- Ergonomics (2)
- Evolutionary algorithm (2)
- FOD (2)
- Integration (2)
- Internet of Things (2)
- Logistic Regression (2)
- Machine Learning (2)
- Microgrid (2)
- Multiple objective optimization (2)
- PFC2D (2)
- Redundancy Allocation Problem (2)
- Reliability (2)
- Sentiment Analysis (2)
- Software engineering (2)
- Solar (2)
- Publication Year
- Publication
- Publication Type
Articles 91 - 91 of 91
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
E-Quality Control: A Support Vector Machines Approach, Kalyan Reddy Aleti
E-Quality Control: A Support Vector Machines Approach, Kalyan Reddy Aleti
Open Access Theses & Dissertations
The web-enabled quality control process presents many benefits to industry, such as universal access, remote control capability, and integration of production equipment into information networks for improved efficiency. This capability has a great potential, since engineers can access and control the equipment anytime, anywhere as the design stages evolve. In this context, this work uses innovative methods in remote part tracking and quality control with the aid of the modern equipment and application of Support Vector machine learning approach to predict the outcome of the quality control process. The classifier equations are built on the data obtained from the experiments …