Quality control using convolutional neural networks applied to samples of very small size

Although there is extensive literature on the application of artificial neural networks (NNs) in quality control (QC), to monitor the conformity of a process to quality specifications, at least five QC measurements are required, increasing the related cost. To explore the application of neural networks to samples of QC measurements of very small size, four one-dimensional (1-D) convolutional neural networks (CNNs) were designed, trained, and tested with datasets of $n$-tuples of simulated standardized normally distributed QC measurements, for $1 \leq n \leq 4$. The designed neural networks were compared to statistical QC functions with equal probabilities for false rejection, applied to samples of the same size. When the $n$-tuples included at least two QC measurements distributed as $\mathcal{N}(\mu, \sigma^2)$, where $0.2 < |\mu| \leq 6.0$, and $1.0 < \sigma \leq 7.0$, the designed neural networks outperformed the respective statistical QC functions. Therefore, 1-D CNNs applied to samples of 2-4 quality control measurements can be used to increase the probability of detection of the nonconformity of a process to the quality specifications, with lower cost.Comment: Article: 21 pages, 5 figures, 8 tables. Appendix: 166 pages, 178 figure

Quality by design approach for tablet formulations containing spray coated ramipril by using artificial intelligence techniques

Different software programs based on mathematical models have been developed to aid the product development process. Recent developments in mathematics and computer science have resulted in new programs based on artificial neural networks (ANN) techniques. These programs have been used to develop and formulate pharmaceutical products. In this study, intelligent software was used to predict the relationship between the materials that were used in tablet formulation and the tablet specifications and to determine highly detailed information about the interactions between the formulation parameters and the specifications. The input data were generated from historical data and the results obtained from analyzing tablets produced by different formulations. The relative significance of inputs on various outputs such as assay, dissolution in 30 min and crushing strengths, was investigated using the artificial neural networks (ANNs), neurofuzzy logic and genetic programming (FormRules, INForm ANN and GEP).This study indicated that ANN and GEP can be used effectively for optimizing formulations and that GEP can be evaluated statistically because of the openness of its equations. Additionally, FormRules was very helpful for teasing out the relationships between the inputs (formulation variables) and the outputs

Quality by design approach for tablet formulations containing spray coated ramipril by using artificial intelligence techniques

Different software programs based on mathematical models have been developed to aid the product development process. Recent developments in mathematics and computer science have resulted in new programs based on artificial neural networks (ANN) techniques. These programs have been used to develop and formulate pharmaceutical products. In this study, intelligent software was used to predict the relationship between the materials that were used in tablet formulation and the tablet specifications and to determine highly detailed information about the interactions between the formulation parameters and the specifications. The input data were generated from historical data and the results obtained from analyzing tablets produced by different formulations. The relative significance of inputs on various outputs such as assay, dissolution in 30 min and crushing strengths, was investigated using the artificial neural networks (ANNs), neurofuzzy logic and genetic programming (FormRules, INForm ANN and GEP).This study indicated that ANN and GEP can be used effectively for optimizing formulations and that GEP can be evaluated statistically because of the openness of its equations. Additionally, FormRules was very helpful for teasing out the relationships between the inputs (formulation variables) and the outputs

Integration of knowledge-based system, artificial neural networks and multimedia for gear design

Design is a complicated area consisting of a combination of rules, technical information and personal judgement. The quality of design depends highly on the designer's knowledge and experience. This system attempts to simulate the design process and to capture design expertise by combining artificial neural networks (ANNs) and knowledge based system (KBS) together with multi-media (MM). It has been applied to the design of gears. Within the system the knowledge based system handles clearly defined design knowledge, the artificial neural networks capture knowledge which is difficult to quantify and multi-media provides a user-friendly interface prompting the user to input information and to retrieve results during design process. The finished system illustrates how features of different Artificial Intelligence techniques, KBS, ANNs and MM, are combined in a hybrid manner to conduct complicated design tasks

Accurate characterization of multi-resonant reflectarray cells by artificial neural networks

This paper describes the accurate characterization of the reflection coefficients of a multilayered reflectarray element by means of artificial neural networks. The procedure has been tested with different RA elements related to actual specifications. Up to 9 parameters were considered and the complete reflection coefficient matrix was accurately obtained, including cross polar reflection coefficients. Results show a good agreement between simulations carried out by the Method of Moments and the ANN model outputs at RA element level, as well as with performances of the complete RA antenna designed