Wavelet Packet Decomposition to Characterize Injection Molding Tool Damage

Kek, Tomaž; Kusić, Dragan; Grum, Janez

doi:10.3390/app6020045

Cited by 6 publications

(5 citation statements)

References 24 publications

(24 reference statements)

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“…They are associated with a wide variety of pulse waveforms, short duration and strong noisiness by natural and industrial sources [ 14 ]. Thus, the application of time-frequency analysis methods used to solve such problems in allied science fields (Short Time Fourier transform [ 15 ], wavelet transform [ 16 ], wavelet packets [ 17 ] etc.) is of low efficiency [ 18 ].…”

Section: Acoustic Emission Signal Processingmentioning

confidence: 99%

Sound Range AE as a Tool for Diagnostics of Large Technical and Natural Objects

Marapulets

Solodchuk

Lukovenkova

et al. 2023

Sensors

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Application of acoustic emission of the sound frequency range is under consideration. This range is of current interest for the diagnostics of the stability of mountain slopes, glaciers, ice covers, large technical constructions (bridges, dams, etc.) as well as for the detection of rock deformation anomalies preceding earthquakes. Acoustic sensors, which can be used to record and to determine the directivity of acoustic emission of the sound frequency range, are under consideration. The structure of the system for acoustic emission recording, processing and analysis is described. This system makes it possible to determine the direction to the acoustic emission source using one multi-component sensor. We also consider the algorithms for detection of acoustic emission pulses in a noisy background, and for the analysis of their structure using the Adaptive Matching Pursuit algorithm. A method for the detection of the direction to an acoustic emission signal source based on multi-component sensors is described. The results of application of sound range acoustic emission for the detection of the intensification of rock deformations, associated with earthquake preparation and development in the seismically active region of Kamchatka peninsula, are presented.

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Section: Acoustic Emission Signal Processingmentioning

confidence: 99%

Sound Range AE as a Tool for Diagnostics of Large Technical and Natural Objects

Marapulets

Solodchuk

Lukovenkova

et al. 2023

Sensors

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“…The number of levels depends on how much low-resolution detail the user requires. In studies using wavelet decomposition in TCM and SHM, the number of decompositions typically lies between 5 and 6 [ 70 , 83 , 84 , 88 ].…”

Section: Signal Processingmentioning

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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“…Moreover, considering the high nonstationarity and large size of AE signals sampled at a high rate, the WPT analysis is employed in this work as it is more effective and efficient in dealing with these types of signals, especially when compared against widely used short time Fourier transform (STFT) and continuous wavelet transform (CWT). The WPT analysis has been explored extensively for signal feature extractions, such as EEG (electroencephalogram) signals [34] and AE signals [35]. Specifically, this technique is also widely used in condition monitoring and fault diagnosis in rotary mechanical systems such as generators [36], gears [37], bearings [38] and diesel engines [39,40].…”

Section: Tribological Monitoring Features From Ae Signalsmentioning

confidence: 99%

An Investigation into the Acoustic Emissions of Internal Combustion Engines with Modelling and Wavelet Package Analysis for Monitoring Lubrication Conditions

Wei

et al. 2019

Energies

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Online monitoring of the lubrication and friction conditions in internal combustion engines can provide valuable information and thereby enables optimal maintenance actions to be undertaken to ensure safe and efficient operations. Acoustic emission (AE) has attracted significant attention in condition monitoring due to its high sensitivity to light defects on sliding surfaces. However, limited understanding of the AE mechanisms in fluid-lubricated conjunctions, such as piston rings and cylinder liners, confines the development of AE-based lubrication monitoring techniques. Therefore, this study focuses on developing new AE models and effective AE signal process methods in order to achieve accurate online lubrication monitoring. Based on the existing AE model for asperity–asperity collision (AAC), a new model for fluid–asperity shearing (FAS)-induced AE is proposed that will explain AE responses from the tribological conjunction of the piston ring and cylinder. These two AE models can then jointly demonstrate AE responses from the lubrication conjunction of engine ring–liner. In particular, FAS allows the observable AE responses in the middle of engine strokes to be characterised in association with engine speeds and lubricant viscosity. However, these AE components are relatively weak and noisy compared to others, with movements such as valve taring, fuel injection and combustions. To accurately extract these weaker AE’s for lubricant monitoring, an optimised wavelet packet transform (WPT) analysis is applied to the raw AE data from a running engine. This results in four distinctive narrow band indicators to describe the AE amplitude in the middle of an engine power stroke. Experimental evaluation shows the linear increasing trend of AE indicator with engine speeds allows a full separation of two baseline engine lubricants (CD-10W30 and CD-15W40), previously unused over a wide range of speeds. Moreover, the used oil can also be diagnosed by using the nonlinear and unstable behaviours of the indicator at various speeds. This model has demonstrated the high performance of using AE signals processed with the optimised WPT spectrum in monitoring the lubrication conditions between the ring and liner in IC engines.

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Wavelet Packet Decomposition to Characterize Injection Molding Tool Damage

Cited by 6 publications

References 24 publications

Sound Range AE as a Tool for Diagnostics of Large Technical and Natural Objects

Sound Range AE as a Tool for Diagnostics of Large Technical and Natural Objects

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

An Investigation into the Acoustic Emissions of Internal Combustion Engines with Modelling and Wavelet Package Analysis for Monitoring Lubrication Conditions

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