Warpage prediction of Injection-molded PVC part using ensemble machine learning algorithm

Ahmed, Tazim; Sharma, Parveen; Karmaker, Chitra Lekha; Nasir, S. M.

doi:10.1016/j.matpr.2020.11.463

Cited by 12 publications

(13 citation statements)

References 17 publications

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“…With this, it is possible to understand the accuracy and variability of the forecasting technique in relation to wind speed. The percentage of errors is calculated by comparing the ANNs applied individually and using the impact factor defined in Equations ( 10) and (11). These errors related to wind forecasts can occur at different times of the day when the resource is available on site.…”

Section: Prevptmentioning

confidence: 99%

“…8 Errors of forecasting techniques applied individually are often higher than errors found from the integration of techniques (hybrid models) or from the use of tools such as portfolio theory (PT). 9 Several methodologies that integrate predictions from individually implemented models have been found in the literature, including prediction errors to improve the results, 9 some set models mixed standard models to achieve a higher performance final product, 10 some set methods applied directly, 11,12 and others combined the results obtained separately by different models. 13 Considering wind energy, "Previs ão" (in Portuguese) by PT (PrevPT) allows to integrate different prediction methodologies and compensates for forecasting errors.…”

Section: Introductionmentioning

confidence: 99%

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Methodology for integration of wind resource forecasts based on artificial neural networks

Carneiro

Lima²,

Carvalho

et al. 2022

Intl J of Energy Research

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Summary An adaptation of the portfolio theory (PT) is proposed in this article, denoted as PrevPT, “Previsão” (in Portuguese) by PT, to integrate the three artificial neural networks, namely multilayer perceptron (MLP) backpropagation, radial basis function (RBF), and self‐organizing map (SOM), based forecasting techniques, aiming to analyze the impact of wind speed forecasting errors and achieve more accurate results. In its first use, the PT goal was to maximize a financial return, at any risk, through the diversification of securities or investments that are not positively correlated. Based on the development of PrevPT, which was used until this work only for solar forecasting, the proposed technique is applied in this paper to integrate and improve the results of individual wind forecasts. Four‐year wind speed data (January 2007 to December 2010) from two different locations (Algeciras, Spain and Petrolina, Brazil) were used. Our methodology develops a topology that integrates the forecasts obtained by MLP, RBF, and SOM aiming to obtain smaller forecast errors. By diversifying the forecasted asset, when one of the assets has negative prediction errors, another compensates for them and, thus, the total or partial cancellation of the errors occurs. PrevPT obtains a mean absolute percentage error of 1.13% for Spain and 2.35% for Brazil. PrevPT surpassed the results obtained by the three techniques applied individually in the two locations. The main innovations of the methodology are the significant reduction of errors and optimization of resource planning, and the beneficial features compared to other predictor integration techniques.

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Section: Prevptmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Methodology for integration of wind resource forecasts based on artificial neural networks

Carneiro

Lima²,

Carvalho

et al. 2022

Intl J of Energy Research

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“…Ogorodnyk et al [12] proposed a multi-layer perceptronbased algorithm to predict the quality of thermoplastic products. Besides, many other studies, such as [13][14][15][16][17][18][19][20][21], have developed machine learning-based quality prediction algorithms for injection molding products.…”

Section: Related Workmentioning

confidence: 99%

Development of a Vision-Image-Based Quality Prediction Neural-Network Algorithm for an Injection Molding Machine Considering Cavity Sensor and Vibration Data

Kim

Lee²

2022

Preprint

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This research develops a neural-network-based algorithm for predicting the quality levels of injection molding products to handle quality regarding problems caused by occurrences of low-quality products. With an assumption that vibration data and temperature and pressure data of the cavities in mold generated from the injection molding machine would be different according to the products’ quality, main objective of this research is to predict the quality grade for each product utilizing the vibration data of the machine and temperature and pressure data of each cavity collected during a product is processed. Among diverse features that can represent quality of injection molding products, we especially focus on the features that could be driven from vision images of the products. We firstly explain how the infrastructure is constructed for collecting the vibration data, cavity sensor data, and vision-image data. Then, for the vibration and cavity sensor data, statistical features that stand for specific patterns of each data utilized as independent variables are derived. Quality grades of each product are also distinguished by two indicators: flection of the product’s housing and alignment of pinholes derived from the vision images of products utilizing the Canny-Edge algorithm. Finally, utilizing statistical features of the vibration and cavity sensor data as independent variables and distinguished quality grades of each product as dependent variables, a neural-network-based quality prediction algorithm is developed, and the performance of the algorithm is evaluated.

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“…Injection molding of plastics is a non-linear and considerably complex process with several dependent process parameters that drive the quality of the produced parts [1,2]. There is an increasing demand for in-line and real-time inspection of the quality of injectionmolded parts, which requires in-line quality feature measurements, machine learning for quality feature prediction, and smart adaptive control of the injection molding process.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, quality disturbances of an injection-molded part include sink marks, weld lines, diesel effect, matt points, jetting, grooves, streaks, flashing, blister, underfilling, flaking, cold slug, voids, shrinkage, or warpage. Usually, the weight and the dimensional properties of the part are considered as optimization goals [1,2,[7][8][9][10][11][12][13]15], as they are easy to evaluate. In industry, the dimensional properties of the produced part caused by shrinkage and warpage during cooling from liquid to solid state are the most important features conforming to the application of the part, typically in an assembly group.…”

Section: Introductionmentioning

confidence: 99%

An Automatic, Contactless, High-Precision, High-Speed Measurement System to Provide In-Line, As-Molded Three-Dimensional Measurements of a Curved-Shape Injection-Molded Part

et al. 2022

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In the manufacturing of injection-molded plastic parts, it is essential to perform a non-destructive (and, in some applications, contactless) three-dimensional measurement and surface inspection of the injection-molded part to monitor the part quality. The measurement method depends strongly on the shape and the optical properties of the part. In this study, a high-precision (±5 µm) and high-speed system (total of 24 s for a complete part dimensional measurement) was developed to measure the dimensions of a piano-black injection-molded part. This measurement should be done in real time and close to the part’s production time to evaluate the quality of the produced parts for future online, closed-loop, and predictive quality control. Therefore, a novel contactless, three-dimensional measurement system using a multicolor confocal sensor was designed and manufactured, taking into account the nominal curved shape and the glossy black surface properties of the part. This system includes one linear and one cylindrical moving axis, as well as one confocal optical sensor for radial R-direction measurements. A 6 DOF (degrees of freedom) robot handles the part between the injection molding machine and the measurement system. An IPC coordinates the communications and system movements over the OPC UA communication network protocol. For validation, several repeatability tests were performed at various speeds and directions. The results were compared using signal similarity methods, such as MSE, SSID, and RMS difference. The repeatability of the system in all directions was found to be in the range of ±5 µm for the desired speed range (less than 60 mm/s–60 degrees/s). However, the error increases up to ±10 µm due to the fixture and the suction force effect.

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Warpage prediction of Injection-molded PVC part using ensemble machine learning algorithm

Cited by 12 publications

References 17 publications

Methodology for integration of wind resource forecasts based on artificial neural networks

Methodology for integration of wind resource forecasts based on artificial neural networks

Development of a Vision-Image-Based Quality Prediction Neural-Network Algorithm for an Injection Molding Machine Considering Cavity Sensor and Vibration Data

An Automatic, Contactless, High-Precision, High-Speed Measurement System to Provide In-Line, As-Molded Three-Dimensional Measurements of a Curved-Shape Injection-Molded Part

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