Fuzzy Method for in Control Acetaldehyde Generation in Resin Pet in the Process of Packaging Pre-Forms of Plastic Injection

In order to control the drying temperature of the PET resin in the silo of the plastic injection molding machine, during the plastic injection process in the industries producing preforms for the manufacture of beverage bottles, care is taken in the ideal temperature regulation for the better performance in controlling the generation of Acetaldehyde (AA), which alters the taste of carbonated or non-carbonated drinks, providing a citrus nuance to the palate and questioning the quality of the packaged products The objective of this work is to develop a tool based on Fuzzy logic to support the control of the drying temperature of PET resin, allowing specialists to make the ideal temperature control decisions necessary to control the generation of Acetaldehyde (AA). For the development of the proposed Fuzzy inference model, we used the Matlab Fuzzy toolbox tool, where the input variables, the fuzzyfication rules and the output variable were implemented based on the data collected from the preform injection process. From the inference model, we obtained a more precise management of the variables that influence the generation of AA, estimating a reduction of $ 240,044.00 in annual costs in the production of preforms

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Intelligent system to support micro injection process through artificial intelligent techniques and cae model integration

Trabajo de investigaciónIn this project a propose of integration of CAE Modeling and artificial intelligence systems to support the process in the production of micro plastic parts is presented. Based on analysis provided by CAE systems, studies will be carried out for diverse parts, to be analyses and throw to artificial intelligent techniques give recommendations of optimal values of plastic micro injection process.1. INTRODUCTION 2. PROBLEM STATEMENT 3. OBJECTIVES 4. CONCEPTUAL FRAMEWORK 5. THEORETICAL FRAMEWORK 6. STATE OF THE ART 7. METHODOLOGY 8. DESCRIPCION OF PROJECT 9. RESULTS 10. VALIDATION OF PROJECT 11. CONCLUSIONS AND FUTURE WORKS 12. REFERENCES 13. ANNEXESMaestríaMagister en Ingeniería y Gestión de la Innovació

Artificial neural networks for predicting the generation of acetaldehyde in pet resin in the process of injection of plastic packages

The industrial production of preforms for the manufacture of PET bottles, during the plastic injection process, is essential to regulate the drying temperature of the PET resin, to control the generation of Acetaldehyde (ACH), which alters the flavor of carbonated or non-carbonated drinks, giving the drink a citrus flavor and putting in doubt the quality of packaged products. In this work, an Artificial Neural Network (ANN) of the Backpropagation type (Cascadeforwardnet) is specified to support the decision-making process in controlling the ideal drying temperature of the PET resin, allowing specialists to make the necessary temperature regulation decisions  for the best performance by decreasing ACH levels. The materials and methods were applied according to the manufacturer\u27s characteristics on the moisture in the PET resin grain, which may contain between 50 ppm and 100 ppm of ACH. Data were collected for the method analysis, according to temperatures and residence times used in the blow injection process in the manufacture of the bottle preform, the generation of ACH from the PET bottle after solid post-condensation stage reached residual ACH levels below (3-4) ppm, according to the desired specification, reaching levels below 1 ppm. The results found through the Computational Intelligence (IC) techniques applied by the ANNs, where they allowed the prediction of the ACH levels generated in the plastic injection process of the bottle packaging preform, allowing an effective management of the parameters of production, assisting in strategic decision making regarding the use of temperature control during the drying process of PET resin

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154105/1/kampfner1997.pd

Neural networks optimization through genetic algorithm searches: A review

Neural networks and genetic algorithms are the two sophisticated machine learning techniques presently attracting attention from scientists, engineers, and statisticians, among others. They have gained popularity in recent years. This paper presents a state of the art review of the research conducted on the optimization of neural networks through genetic algorithm searches. Optimization is aimed toward deviating from the limitations attributed to neural networks in order to solve complex and challenging problems. We provide an analysis and synthesis of the research published in this area according to the application domain, neural network design issues using genetic algorithms, types of neural networks and optimal values of genetic algorithm operators (population size, crossover rate and mutation rate). This study may provide a proper guide for novice as well as expert researchers in the design of evolutionary neural networks helping them choose suitable values of genetic algorithm operators for applications in a specific problem domain. Further research direction, which has not received much attention from scholars, is unveiled

Modern methods in engine knock signal detection

In this paper, a review is given of some of the modern methods in the detection of knock in internal-combustion engines and some comparisons are made between these methods and the effectiveness of each one of them is indicated through a statement of the advantages and disadvantages of each method. In this way it will be possible to clarify how to deal with the original signal and the associated signal noise through some of the modern algorithms in the field of soft computing such as an Artificial Neural Network (ANN), Genetic Algorithms (GA), Wavelet Transform (WT), Fuzzy logic, Supported Vector Machine (SVM) and some statistical methods