Mould wear-out prediction in the plastic injection moulding industry: a case study

Frumosu, Flavia Dalia; Rønsch, Georg Ørnskov; Külahçı, Murat

doi:10.1080/0951192x.2020.1829062

Cited by 7 publications

(3 citation statements)

References 34 publications

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“…is method is similar to how a normal human being learns on his or her own. e method in [72] shows the best results using unsupervised cluster analysis that finds the molds wearing out and predicts in real-time, and it has an excellent early warning system which predicts before they wear out and cause trouble.…”

Section: Discussion: ML Implementations and Outcomesmentioning

confidence: 99%

A Review on Machine Learning Models in Injection Molding Machines

Selvaraj

Raj

Mahadevan

et al. 2022

Advances in Materials Science and Engineering

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One of the most suitable methods for the mass production of complicated shapes is injection molding due to its superior production rate and quality. The key to producing higher quality products in injection molding is proper injection speed, pressure, and mold design. Conventional methods relying on the operator’s expertise and defect detection techniques are ineffective in reducing defects. Hence, there is a need for more close control over these operating parameters using various machine learning techniques. Neural networks have considerable applications in the injection molding process consisting of optimization, prediction, identification, classification, controlling, modeling, and monitoring, particularly in manufacturing. In recent research, many critical issues in applying machine learning and neural network in injection molding in practical have been addressed. Some problems include data division, collection, and preprocessing steps, such as considering the inputs, networks, and outputs, algorithms used, models utilized for testing and training, and performance criteria set during validation and verification. This review briefly explains working on machine learning and artificial neural network and optimizing injection molding in industries.

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Section: Discussion: ML Implementations and Outcomesmentioning

confidence: 99%

A Review on Machine Learning Models in Injection Molding Machines

Selvaraj

Raj

Mahadevan

et al. 2022

Advances in Materials Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Such quantities of data typically lead to a high need for computational power for analysing large datasets gathered from the process [ 90 ]. These methods have been implemented in various industrial processes and proved capable of generating predictions for wear out of the tool [ 91 ].…”

Section: Decision-making Algorithmsmentioning

confidence: 99%

Condition Monitoring of Additively Manufactured Injection Mould Tooling: A Review of Demands, Opportunities and Potential Strategies

Weinert

Tormey

O’Hara

et al. 2023

Sensors

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Injection moulding (IM) is an important industrial process, known to be the most used plastic formation technique. Demand for faster cycle times and higher product customisation is driving interest in additive manufacturing (AM) as a new method for mould tool manufacturing. The use of AM offers advantages such as greater design flexibility and conformal cooling of components to reduce cycle times and increase product precision. However, shortcomings of metal additive manufacturing, such as porosity and residual stresses, introduce uncertainties about the reliability and longevity of AM tooling. The injection moulding process relies on high volumes of produced parts and a minimal amount of tool failures. This paper reviews the demands for tool condition monitoring systems for AM-manufactured mould tools; although tool failures in conventionally manufactured tooling are rare, they do occur, usually due to cracking, deflection, and channel blockages. However, due to the limitations of the AM process, metal 3D-printed mould tools are susceptible to failures due to cracking, delamination and deformation. Due to their success in other fields, acoustic emission, accelerometers and ultrasound sensors offer the greatest potential in mould tool condition monitoring. Due to the noisy machine environment, sophisticated signal processing and decision-making algorithms are required to prevent false alarms or the missing of warning signals. This review outlines the state of the art in signal decomposition and both data- and model-based approaches to determination of the current state of the tool, and how these can be employed for IM tool condition monitoring. The development of such a system would help to ensure greater industrial uptake of additive manufacturing of injection mould tooling, by increasing confidence in the technology, further improving the efficiency and productivity of the sector.

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“…After entering the natural environment, these plastic wastes will continue to undergo a series of changes. In particular, microplastics are widely detected in water, soil, atmosphere and various organisms, and their occurrence level, distribution characteristics, environmental fate and ecological toxicity are widely concerned [1,2].…”

Section: Introductionmentioning

confidence: 99%

Plastic Industry Development and Natural Environment Protection

2020

NEP

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With the improvement of China's industrial manufacturing capacity and people's living standards, more and more plastic products appear in people's daily life. Because of the use and loss of plastic products, as well as the participation of plastic products in related industrial activities, the amount of micro plastics entering the environment is also increasing, both ecological health and human health are threatened. At present, the problem of plastic pollution in the environment is becoming increasingly serious worldwide. In order to reduce the harm of plastic pollution to the environment, this paper conducts a plastic ecological risk assessment on the sewage generated in the production process of a plastic industry, explores the potential risks of plastic pollution to the natural environment, can deeply understand the current situation of plastic pollution in the environment, and then uses SMBR process to treat plastic production sewage, analyzes the removal rate of CODcr. The results show that:, SMBR has a good removal effect on micro plastics, which provides a technical reference for the implementation of plastic pollution prevention.

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Mould wear-out prediction in the plastic injection moulding industry: a case study

Cited by 7 publications

References 34 publications

A Review on Machine Learning Models in Injection Molding Machines

A Review on Machine Learning Models in Injection Molding Machines

Condition Monitoring of Additively Manufactured Injection Mould Tooling: A Review of Demands, Opportunities and Potential Strategies

Plastic Industry Development and Natural Environment Protection

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