Investigation of monitoring methods for ultrasonic metal welding

Lindamood, Lindsey R.; Mohr, Luke; Moghaddas, Amin; Kitt, Alex; Frech, Timothy

doi:10.1117/12.2583121

Cited by 3 publications

(1 citation statement)

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“…Current solutions to improving good part production rates typically use an iterative approach known as metamodeling, wherein (1) simulations are run in search of an optimal parameter setting, (2) a part is then produced based on the simulations results, and (3) the actual results are incorporated into optimal parameter searching for another round of simulations and actual runs [10][11][12] (see also Yang et al [13], for an additive manufacturing example). More recent work adopts neural networks in place of traditional simulations within the metamodeling process [14] (see also Zimmerling et al [15], for a textile forming example; for machine learning more generally within manufacturing, see [16,17]). The work most similar to what is presented here developed a Bayesian framework to predict variables within a full production line [18].…”

Section: Previous Workmentioning

confidence: 99%

Profit-Driven Methodology for Servo Press Motion Selection under Material Variability

Okuda¹,

Mohr²,

Kim³

et al. 2021

Applied Sciences

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Servo presses enable new types of forming motion profiles that can be used to stamp difficult materials, such as high strength steels. This paper presents an application of Bayesian statistics to intelligently select which motion profile maximizes the expected utility given the properties of the incoming material. Bayesian logistic regression was used in conjunction with expected utility to estimate manufacturing returns, which can be used to make informed process decisions. A use case is presented, which demonstrates that the Smart Forming Algorithm can increase expected returns by more than 20%.

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Section: Previous Workmentioning

confidence: 99%

Profit-Driven Methodology for Servo Press Motion Selection under Material Variability

Okuda¹,

Mohr²,

Kim³

et al. 2021

Applied Sciences

Self Cite

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Machine learning-based shear force quality prediction of ultrasonic wire bonds: utilizing process data and machine data without additional sensors

Buchner,

Seidler,

Huber

et al. 2024

Int J Adv Manuf Technol

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Ultrasonic wire bonding is a highly automated production process that is used billions of times a year in the electronics and electromobility industries. Due to the complexity of the process and the large number of influencing parameters, there are currently no automated methods that can be used without additional sensors to evaluate the shear force bond quality quantitatively and non-destructively with sufficiently high precision. For this reason, this paper presents a new methodology that uses machine learning to enable quantitative, non-destructive prediction of bond quality without additional sensors. For this purpose, a machine learning algorithm was developed that uses various machine data and process data from existing sensors to quantitatively predict the shear force of the bonded joint. In addition, features are extracted from process time series, such as current, power, and frequency of the ultrasonic generator as well as deformation during bonding. Of the total of 2,090 features considered, the number of features could be reduced to 26 by recursive feature elimination, while maintaining the prediction accuracy. By using optimized deep neural networks, on average, a prediction precision of the regression on the shear force of the source bond of over 89.6% R2-score and a mean absolute error of 241 cN can be achieved.

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Joint Quality Assessment of Ultrasonic Metal Welded Parts by Fracture Surface Evaluation

Müller,

Zhao,

Schiebahn

et al. 2024

Metals

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In ultrasonic metal welding, low specific resistances and large joining surface cross-sections require the use of mechanical testing to quantify the joint quality. In this study, different quality features of ultrasonically welded joints made of pure copper sheet are investigated during the successive phases of joint formation. Two test series with different workpiece geometries are examined. It is shown that mechanical quality features such as shear and peel forces behave differently over the formation of the joint and are not transferable. As an alternative to these, laser scanning microscopy is used to record images of the fracture surface that describe the growth of the joint area during formation. The study finds that shear tensile force growth and joint area growth are non-linear and comparable, with optimized welds achieving joint areas of 30 mm2 out of 64 mm2 and 6 mm2 out of 16 mm2. Although overall quality increases with increasing welding time, the material strength in the joint zone decreases. Depending on the original rolling condition, between 43% and 59% of the original material strength can be identified as the joint strength. The automatic analysis of fracture images is a suitable alternative to mechanical testing for similar joints.

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Investigation of monitoring methods for ultrasonic metal welding

Cited by 3 publications

References 0 publications

Profit-Driven Methodology for Servo Press Motion Selection under Material Variability

Profit-Driven Methodology for Servo Press Motion Selection under Material Variability

Machine learning-based shear force quality prediction of ultrasonic wire bonds: utilizing process data and machine data without additional sensors

Joint Quality Assessment of Ultrasonic Metal Welded Parts by Fracture Surface Evaluation

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