Intelligent Decision System Based on Fuzzy Logic Expert System to Improve Plastic Injection Molding Process

Chaves, Miryam Liliana; Márquez, Juan de Juanes; Pérez, Hilde; Sánchez, Lidia; Vizán, Antonio

doi:10.1007/978-3-319-67180-2_6

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…In the development of parts with micrometric characteristics, the manufacturing processes require unconventional designs that allow the systems to maintain the mechanical characteristics and at the same time have an industrial quality index. In order to produce these parts is necessary to invest a lot of time in geometric mold designs and in the adjustment of the control parameters, on many occasions these production and design times can be from weeks or months until reaching an optimal design and control [4].…”

Section: Problem Statementmentioning

confidence: 99%

“…The control processes for the manufacture of micro-parts are diverse, from the complete control of an expert operator [4], to autonomous systems verified by positioning algorithms and fed by data capture sensors. Many of these processes can be controlled by artificial intelligence techniques and expert systems based on fuzzy logic (FL), these are software capable of making decisions from a knowledge base and making inferences similar to human ones, some such as systems experts based on case reasoning, fuzzy logic or neural networks [9].…”

Section: Development and Implementation Of Smem Technologiesmentioning

confidence: 99%

“…The total controls of the operators on the production processes must be intervened by expert systems, these must have the ability to control and identify faults and irregularities that may occur during the manufacturing process [4]. The control of variables such as temperature, application pressure, flow of material, among others, search to reduce the possibility of defects such as deformations and reflows on the material.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Integration of CAE Modeling and Artificial Intelligence Systems to Support Manufacturing of Plastic Microparts

Rojas

Chaves

Bolivar

et al. 2019

2019 Congreso Internacional De Innovación Y Tendencias en Ingenieria (CONIITI )

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Section: Problem Statementmentioning

confidence: 99%

Section: Development and Implementation Of Smem Technologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Integration of CAE Modeling and Artificial Intelligence Systems to Support Manufacturing of Plastic Microparts

Rojas

Chaves

Bolivar

et al. 2019

2019 Congreso Internacional De Innovación Y Tendencias en Ingenieria (CONIITI )

Self Cite

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“…100 injection defects. The objective of such systems is to optimize the process conditions to obtain a part with the specified quality [14]. AI in injection molding machines can make an important contribution in the production of quality plastic parts, due to the action of sensors that monitor variations in the temperature of the grains, subjected to factors that may disqualify the results obtained in the injection [15].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks for Predicting the Generation of Acetaldehyde in Pet Resin in the Process of Injection of Plastic Packages

Nascimento

Alencar

Nascimento

et al. 2021

Int J Innov Educ Res

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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's 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.

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“…However, various existing sources of randomness have many limitations, such as limited generation rate, lack of randomness of data, requires an external device, and failures are difficult to detect [13]. In IoT, high entropy random numbers are hard to achieve due to lack of resources [14] [15]. Entropy in information theory is the measure of randomness of a random variable or outcome of a random process.…”

Section: Introductionmentioning

confidence: 99%

Entropy as a Service: A Lightweight Random Number Generator for Decentralized IoT Applications

Ullah

Meratnia

Havinga

2020

2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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Cryptographic Pseudorandom Number Generators (CPRNG) play a very crucial role in Internet of Things (IoT) security. Cryptographic protocols require random numbers for nonces, salts, and key generation. However, developing secure and lightweight CPRNG is strenuous. Insecure source of randomness can evolve in vulnerabilities and can jeopardize security mechanisms. As the number of IoT devices are expected to exceed over billions, the demand for distributed CPRNG increases. Manually configuring random numbers in large numbers of IoT devices is practically challenging and insecure. In this paper, we propose a cryptographically secure pseudorandom number generator based on sensor data as source of randomness. The appealing characteristic of a sensor data based random number generator is that sensor can possibly generate infinite data. Thus, having longer period and perhaps higher entropy. We also present proof-of-concept of potential usage of sensor data as a source of randomness. Furthermore, the mechanism is evaluated with the NIST statistical suite.

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Intelligent Decision System Based on Fuzzy Logic Expert System to Improve Plastic Injection Molding Process

Cited by 6 publications

References 7 publications

Integration of CAE Modeling and Artificial Intelligence Systems to Support Manufacturing of Plastic Microparts

Integration of CAE Modeling and Artificial Intelligence Systems to Support Manufacturing of Plastic Microparts

Artificial Neural Networks for Predicting the Generation of Acetaldehyde in Pet Resin in the Process of Injection of Plastic Packages

Entropy as a Service: A Lightweight Random Number Generator for Decentralized IoT Applications

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