An approach to monitoring quality in manufacturing using supervised machine learning on product state data

Wuest, Thorsten; Irgens, Christopher; Thoben, Klaus‐Dieter

doi:10.1007/s10845-013-0761-y

Cited by 207 publications

(72 citation statements)

References 35 publications

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“…NNs; Gaussian) (Keerthi & Lin, 2003). SVM as a classification technique has its roots in SLT (Khemchandani & Chandra, 2009;Salahshoor, Kordestani, & Khoshro, 2010) and has shown promising empirical results in a number of practical manufacturing applications (Chinnam, 2002;Widodo & Yang, 2007) and works very well with high-dimensional data (Azadeh et al, 2013;Ben-hur & Weston, 2010;Salahshoor et al, 2010;Sun, Rahman, Wong, & Hong, 2004;Wu, 2010;Wuest, Irgens, & Thoben, 2014). Current literature suggests that the performance of SVM compared to other ML methods is still very competitive (Jurkovic, Cukor, Brezocnik, & Brajkovic, 2016).Another aspect of this approach is that it represents the decision boundary using a subset of the training examples, known as the support vectors.…”

Section: Supervised Machine Learning Algorithms In Manufacturing Applmentioning

confidence: 99%

Machine learning in manufacturing: advantages, challenges, and applications

Wuest

Weimer²,

Irgens

et al. 2016

Production & Manufacturing Research

Self Cite

817

438

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The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.

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Section: Supervised Machine Learning Algorithms In Manufacturing Applmentioning

confidence: 99%

Machine learning in manufacturing: advantages, challenges, and applications

Wuest

Weimer²,

Irgens

et al. 2016

Production & Manufacturing Research

Self Cite

817

438

View full text Add to dashboard Cite

show abstract

“…Some EHM applications are also unsupervised. At the other extreme, analytics can focus on determining correlations between "input" and "output" datasets [26,27]. As an example, PdM and VM determine relationships between equipment data (trace or processed, e.g., through FD) and maintenance and metrology measurement data, respectively.…”

Section: Dimensions Of Analytics Capabilitiesmentioning

confidence: 99%

Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing

2017

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Smart manufacturing (SM) is a term generally applied to the improvement in manufacturing operations through integration of systems, linking of physical and cyber capabilities, and taking advantage of information including leveraging the big data evolution. SM adoption has been occurring unevenly across industries, thus there is an opportunity to look to other industries to determine solution and roadmap paths for industries such as biochemistry or biology. The big data evolution affords an opportunity for managing significantly larger amounts of information and acting on it with analytics for improved diagnostics and prognostics. The analytics approaches can be defined in terms of dimensions to understand their requirements and capabilities, and to determine technology gaps. The semiconductor manufacturing industry has been taking advantage of the big data and analytics evolution by improving existing capabilities such as fault detection, and supporting new capabilities such as predictive maintenance. For most of these capabilities: (1) data quality is the most important big data factor in delivering high quality solutions; and (2) incorporating subject matter expertise in analytics is often required for realizing effective on-line manufacturing solutions. In the future, an improved big data environment incorporating smart manufacturing concepts such as digital twin will further enable analytics; however, it is anticipated that the need for incorporating subject matter expertise in solution design will remain.

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“…Machine learning algorithms have been previously used for diverse manufacturing problems, see e.g., (Sharma et al 2008;Wuest et al 2014;Benkedjouh et al 2013;Wang and Tseng 2013;Zhang et al 2013). The list of applications include bearings fault detection using vibration data (Li et al 2012(Li et al , 2013Kankar et al 2011a, b;Yang et al 2011;Sugumaran and Ramachandran 2011;Zhao et al 2011;Su et al 2005;Mendel et al 2008).…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of defective embedded bearings by sound analysis: a machine learning approach

Saucedo-Espinosa

Berrones

2014

J Intell Manuf

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This paper describes a machine learning solution for the detection of defective embedded bearings in home appliances by sound analysis. The bearings are installed deep into the home appliances at the beginning of the production process and cannot be physically accessed once they are fully assembled. Before a home appliance is put to sale, it is turned on and passed through a sound-based sensor that produces an acoustic signal. Home appliances with defective embedded bearings are detected by analyzing such signals. The approached task is very challenging, mainly because there is a small number of sample signals and the noise level in the measurements is quite high. In fact, it is showed that the signal-to-noise ratio is high enough to mask important components when applying traditional Fourier decomposition techniques. Hence, a different approach is needed. Experimental results are reported on both laboratory and production line signals. Despite the difficulty of the task, these results are encouraging. Several classification methods were evaluated and most of them achieved acceptable performance. An interesting finding is that, among the classifiers that showed better performance, some methods are highly intuitive and easy to implement. These methods are generally preferred in industry. The proposed solution is being implemented by the company which motivated this study.

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An approach to monitoring quality in manufacturing using supervised machine learning on product state data

Cited by 207 publications

References 35 publications

Machine learning in manufacturing: advantages, challenges, and applications

Machine learning in manufacturing: advantages, challenges, and applications

Big Data Analytics for Smart Manufacturing: Case Studies in Semiconductor Manufacturing

Detection of defective embedded bearings by sound analysis: a machine learning approach

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