Fault diagnosis in injection moulding via cavity pressure signals

Zhang, J.; Alexander, Suraj M.

doi:10.1080/00207540701429918

Cited by 22 publications

(5 citation statements)

References 21 publications

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“…Recording of signals by pressure and temperature sensors in the forming cavity can be correlated with changes in selected processing parameters set on the machine to optimize the injection molding process [40,41]. As a result, it is possible to automatically eliminate defective molded pieces by a comparative analysis of signals from the mold cavity and reference data for a good product [42][43][44]. Although there is a partial convergence of signals generated in the processing machine and the injection mold, the data obtained from the forming cavity are more informative and may be of greater importance [45].…”

Section: Entry In the Literature Listmentioning

confidence: 99%

The Microcellular Structure of Injection Molded Thick-Walled Parts as Observed by In-Line Monitoring

2020

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The aim of the study was to detect the influence of nitrogen pressure on the rheological properties and structure of PA66 GF30 thick-walled parts, produced by means of microcellular injection molding (MIM), using the MuCell® technology. The process was monitored in-line with pressure and temperature sensors assembled in the original injection mold. The measured data was subsequently used to evaluate rheological properties inside an 8.4 mm depth mold cavity. The analysis of the microcellular structure was related to the monitored in-line pressure and temperature changes during the injection process cycle. A four-times reduction of the maximum filling pressure in the mold cavity for MIM was found. At the same time, the holding pressure was taken over by expanding cells. The gradient effect of the cells distribution and the fiber arrangement in the flow direction were observed. A slight influence of nitrogen pressure on the cells size was found. Cells with a diameter lower than 20 µm dominate in the analyzed cases. An effect of reduction of the average cells size in the function of distance to the gate was observed. The creation of structure gradient and changes of cells dimensions were evaluated by SEM images and confirmed with the micro CT analysis.

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Section: Entry In the Literature Listmentioning

confidence: 99%

The Microcellular Structure of Injection Molded Thick-Walled Parts as Observed by In-Line Monitoring

2020

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“…When looking into utilising the machine and cavity time resolved profiles, in the form of short time series, there are a variety of options. Zhang and Alexander (2008)…”

Section: Feature Learning Methods For Profile Datamentioning

confidence: 99%

“…Unfortunately, this part has in many cases been neglected with the focus being firmly on getting the best predictive power irrespective of the extra cost for sensors, data collection, analysis and not least the continuous maintenance of the whole data utilisation chain. Zhang and Alexander (2008) conclude that cavity signals can be used as process fingerprints and that these can be used for process monitoring, This might be a valid conclusion but it is not considered if the same results could be achieved using less expensive data source, such as machine profiles. Farahani et al (2019) present a relevant evaluation of different data sources against the cost of computation (by reducing data complexity), but ignore the cost of data collection and therefore recommend the use of cavity sensors to enable significant prediction opportunities.…”

Section: Introductionmentioning

confidence: 99%

An investigation of the utilisation of different data sources in manufacturing with application in injection moulding

Rønsch

Külahçı

Dybdahl

2021

International Journal of Production Research

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This work focuses on the effective utilisation of varying data sources in injection moulding for process improvement through a close collaboration with an industrial partner. The aim is to improve productivity in an injection moulding process consisting of more than 100 injection moulding machines. It has been identified that predicting quality through Machine Process Data is the key to increase productivity by reducing scrap. The scope of this work is to investigate whether a sufficient prediction accuracy (less than 10% of the specification spread) can be achieved by using readily available Machine Process Data or additional sensor signals obtained at a higher cost are needed. The latter comprises Machine Profile and Cavity Profile Data. One of the conclusions is that the available Machine Process Data does not capture the variation in the raw material that impacts element quality and therefore fails to meet the required prediction accuracy. Utilising Machine Profiles or Cavity Profiles have shown similar results in reducing the prediction error. Since the cost of implementing cavity sensors in the entire production is higher than utilising the Machine Profiles, further exploration around improving the utilisation of Machine Profile Data in a setting where process variation and labelled data are limited is proposed.

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“…Das Neogi [22] forecasted the analysing outputs of the change in dependent variables linked on the independent variables in the plastic injection process, by using the linear regression model. Zhang and Alexander [23] diagnosed the difficulties in the plastic injection process by observing the pressure signals in mould cavitation via the pressure cavity pressure signals.…”

Section: Methodsmentioning

confidence: 99%

Optimization of Nozzle Section in Plastic Injection Moulding Process

Gürel

Ipek

Başoğul

et al. 2019

Hittite J. Sci. Eng.

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P lastics was discovered at the end of the 19th century and developed further, especially after the Second World War. They were begun to be used in the industry and in our daily life with its thousands of varieties [1-3]. For plastic part manufacturing determined according to the physical and chemical characteristics of plastic, various manufacturing techniques have been developed (injection, extrusion, blowing, etc.). Almost 33% of plastic parts are manufactured by using injection moulding technique [4,5]. It is widely used in mechanical construction, automotive components, aerospace and aeronautical industries, and household products. Many studies have been made on manufacturing with injection moulding technique that has an extensive manufacturing capacity [6-9]. This technique enables a low cost and efficiency, fast production cadence, and high dimensional accuracy of complex plastic parts. The quality, geometrical structure, dimension tolerance, surface quality and factors affecting strength of the parts manufactured with the Article History:

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Fault diagnosis in injection moulding via cavity pressure signals

Cited by 22 publications

References 21 publications

The Microcellular Structure of Injection Molded Thick-Walled Parts as Observed by In-Line Monitoring

The Microcellular Structure of Injection Molded Thick-Walled Parts as Observed by In-Line Monitoring

An investigation of the utilisation of different data sources in manufacturing with application in injection moulding

Optimization of Nozzle Section in Plastic Injection Moulding Process

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