Using regression models for predicting the product quality in a tubing extrusion process

García, Vicente; Sánchez, J. Salvador; Rodríguez‐Picón, Luis Alberto; Méndez‐González, Luis Carlos; Domínguez, Humberto de Jesús Ochoa

doi:10.1007/s10845-018-1418-7

Cited by 62 publications

(30 citation statements)

References 47 publications

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“…Six (6) different grades of PVC were used with the developed model to attempt to predict the process parameters and the results were satisfactory. The results of this work, when compared with the best work in literature so far, have proven to be better by providing a reduced MSE value [ 25 ]. reported eight different methods of predicting product quality with the best method having an MSE value of

which is still higher than the MSE recorded in this thesis.…”

Section: Resultsmentioning

confidence: 99%

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Predicting extrusion process parameters in Nigeria cable manufacturing industry using artificial neural network

Adesanya

Abdulkareem

Adesina

2020

Heliyon

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The extrusion process is a very complex process due to the number of process parameters that are associated with it which are prone to high fluctuations. The main purpose of this work is to determine the realistic extrusion process parameters in the thermoplastic extrusion process in Nigeria cable manufacturing industries with the use of an artificial neural network. Conventionally, the use of trial and error technique which involves full-size experiments is generally used to determine the process parameters in the thermoplastic extrusion process. This conventional technique is expensive and it is also time-consuming. The use of an artificial neural network to predict extrusion process parameters before plant execution will make extrusion process operations more efficient. This technique also bridges the gap that exists between theoretical analysis and real manufacturing system because real manufacturers' data was used. The neural network was developed in a MATLAB environment and was trained with a supervised learning method based on Levenberg Marquardt Algorithm and the developed ANN model is capable of predicting manufacturing process parameters for different grades of PVC thermoplastic material.

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which is still higher than the MSE recorded in this thesis.…”

Section: Resultsmentioning

confidence: 99%

“…Vicente and colleagues proposed the use of regression models to predict the quality in a tubing extrusion process. They discussed how quality cannot be overemphasized in the manufacturing process [ 25 , 26 ]. Chamil and colleagues developed an extruder melt temperature control with fuzzy logic [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting extrusion process parameters in Nigeria cable manufacturing industry using artificial neural network

Adesanya

Abdulkareem

Adesina

2020

Heliyon

View full text Add to dashboard Cite

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“…Measurements within the processes as well as sensor data in the tool or on its surface [15] further improve the accuracy of the digital twin. For the manufacturing of structural components, several data-driven approaches for virtual quality inspection have been proposed, for example, metal casting [16,17], extrusion of tubes [18], injection molding [19] as well as automated dry material placement for composite structures [20].…”

Section: Introductionmentioning

confidence: 99%

Combining Simulation and Machine Learning as Digital Twin for the Manufacturing of Overmolded Thermoplastic Composites

Hürkamp

Gellrich

Ossowski

et al. 2020

JMMP

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The design and development of composite structures requires precise and robust manufacturing processes. Composite materials such as fiber reinforced thermoplastics (FRTP) provide a good balance between manufacturing time, mechanical performance and weight. In this contribution, we investigate the process combination of thermoforming FRTP sheets (organo sheets) and injection overmolding of short FRTP for automotive structures. The limiting factor in those structures is the bond strength between the organo sheet and the overmolded thermoplastic. Within this process chain, even small deviations of the process settings (e.g., temperature) can lead to significant defects in the structure. A cyber physical production system based framework for a digital twin combining simulation and machine learning is presented. Based on parametric Finite-Element-Method (FEM) studies, training data for machine learning methods are generated and a FEM surrogate is developed. A comparison of different data-driven methods yields information on the estimation accuracy of task-specific data-driven methods. Finally, in accordance with experimental cross tension tests, the investigated FEM surrogate model is able to predict the interface bond strength quality in dependence of the process settings. The visualization into different quality domains qualifies the presented approach as decision support.

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“…The introduction of data-driven [46] economy in the manufacturing industry has promoted the so called fourth industrial revolution or "Industry 4.0", also referred to as "Smart Manufacturing", which is defined in [8] upon two main concepts: the compilation of manufacturing records of products and the application of artificial intelligence techniques to analyze those records. Thus, the captured raw data (time series generated by the continuous operation of the manufacturing process or equipment to be analyzed) are usually stored in cloud computing infrastructures [59] for further analysis processes (product quality [11], fault detection [19], predictive maintenance of equipment [48], etc. ).…”

Section: Introductionmentioning

confidence: 99%

A flexible alarm prediction system for smart manufacturing scenarios following a forecaster–analyzer approach

2020

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Using regression models for predicting the product quality in a tubing extrusion process

Cited by 62 publications

References 47 publications

Predicting extrusion process parameters in Nigeria cable manufacturing industry using artificial neural network

Predicting extrusion process parameters in Nigeria cable manufacturing industry using artificial neural network

Combining Simulation and Machine Learning as Digital Twin for the Manufacturing of Overmolded Thermoplastic Composites

A flexible alarm prediction system for smart manufacturing scenarios following a forecaster–analyzer approach

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