Browsing by Author "Du Preez, Anli"

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    A decision support framework for machine learning applications
    (Stellenbosch : Stellenbosch University, 2020-20) Du Preez, Anli; Bekker, James; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: Data is currently one of the most critical and influential emerging technolo-gies. Organisations and employers around the globe strive to investigateand exploit the exponential data growth to discover hidden insights in aneffort to create value. Value creation from data is made possible throughdata analytics (DA) and machine learning (ML). The true potential of datais yet to be exploited since, currently, about 1% of generated data is everactually analysed for value creation. There is a data gap. Data is availableand easy to capture; however, the information therein remains untappedyet ready for digital explorers to discover the hidden value in the data. Onemain factor contributing to this gap is the lack of expert knowledge in thefield of DA and ML.In a survey of 437 companies, 76% indicated an interest to invest in DA andML technologies over the years of 2015 to 2017. However, in a survey of 400companies, 4% indicated that they have the right strategic intent, skilledpeople, resources and data to gain meaningful insights from their data andto act on them. Small, medium and micro enterprises (SMMEs) lack theavailability of DA and ML skills in their existing workforce, have limitedinfrastructure to realise ML and have limited funding to employ ML toolsand expertise. They need proper guidance as to how to employ ML in alow-cost, feasible and sustainable way.This study focused on addressing this data gap by providing a decision sup-port framework for ML algorithms. The goal of this study was therefore todevelop and validate adecision support frameworkwhich considers both thedata characteristicsand theapplication typeto enable SMMEs to choosethe appropriate ML algorithm for their unique data and application pur-pose. This study aimed to develop the framework for a semi-skilled analyst,with mathematics, statistics and programming education, who is familiar with the process of programming, yet has not specialised in the variety ofML algorithms which are available.This research project followed the Soft Systems Methodology and utilisedJabareen’s framework development methodology. Various literature studieswere performed on data, DA, application purposes, ML and the processof applying ML. The CRoss-Industry Standard Process for Data Mining(CRISP-DM) was followed to design and implement the experiments. Theresults were evaluated and summarised to create the decision support frame-work. The framework was validated by consulting subject matter experts(SMEs) and possible end-users (PEUs).
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    Machine learning in cutting processes as enabler for smart sustainable manufacturing
    (Elsevier, 2019) Du Preez, Anli; Oosthuizen, Gert Adriaan
    ENGLISH ABSTRACT: Machine learning is becoming an increasingly popular concept in the modern world since its most common goal is to optimize systems by allowing one to make smarter use of products and services. In the manufacturing industry machine learning can lead to cost savings, time savings, increased quality and waste reduction. At the same time, it enables systems to be designed for managing human behaviour. This research study used a systematic review to investigate the different machine learning algorithms within the sustainable manufacturing context. The study focuses specifically on cutting processes.