Masters Degrees (Industrial Engineering)

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    Developing a compressed air benchmark approach to be used as a metric to identify ventilation shortfalls
    (Stellenbosch : Stellenbosch University, 2023-11) van Gruting, Ulrich; Schutte, Cornelius Stephanus Lodewyk; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Engineering Management (MEM).
    ENGLISH ABSTRACT: Platinum mining contributes significantly to the economy of South Africa. However, deep-level platinum mines in South Africa are facing numerous challenges which is placing strain on the profitability of these mines. One challenge that stood out is the rising electricity cost which is detracting from the price of Platinum Group Metals. This challenge has forced platinum mines to investigate improving the efficiency of electricity consumers. Two utilities that were specifically highlighted are compressed air and ventilation. Compressed air is a critical component in the mining operation and accounts for a large portion of electricity use. It has been estimated that 75% of produced compressed air is wasted because of mismanagement and misappropriation. The wastage stems from leakages, as a result of poor maintenance, mismanagement and misappropriation. To address the low efficiencies in deep-level mine compressed air systems, previous studies have investigated several demand-side management initiatives to reduce the wastage of compressed air. Ventilation, the second utility, promotes an optimised mining cycle and is critical to ensuring that the health and safety standards of mine personnel are adhered to. Mining companies prioritise production considerations over ventilation requirements and as a result, ventilation networks are often inadequate. This often causes compressed air to be misappropriated as an interim solution for cooling working areas underground. Existing studies on underground compressed air wastage and ventilation shortfalls in deep-level mines are limited. Additionally, the effect of ventilation shortfalls on compressed air misappropriation has not been evaluated. Hence, a need exists to determine the relationship between compressed air wastage and ventilation shortfalls. Current methods for addressing compressed air wastage and ventilation inefficiencies, such as benchmarking models, simulations, leak management, and conventional audits, do not specifically target ventilation shortfalls as a root cause for compressed air wastage. Additionally, these studies make use of complicated and limited methods to address compressed air wastage and ventilation inefficiencies. To address the problem identified, the main study objective of this thesis was the development of a new methodology, utilising compressed air wastage as a metric, to identify ventilation shortfalls in a less resources and time-intensive way. A new method was developed that benchmarks compressed air systems in deep-level underground mines to identify and prioritise levels based on the highest compressed air wastage. This newly developed method was further tailored towards ventilation shortfalls, utilising a newly developed Baseload Intensity indicator, to identify the level with the highest possibility of a ventilation shortfall. By localising ventilation shortfalls to specific crosscuts using the crosscut baseload method (in conjunction with the Baseload Intensity indicator), the methodology reduces the resources and time required to identify ventilation shortfalls. The newly developed methodology, with its sub-methods, was applied to two deep-level platinum mines in the North West province of South Africa. The application of the newly developed methodology successfully identified ventilation shortfalls using less resources and time (when compared with conventional audits) on both case studies.
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    Development of a framework of factors essential to the optimal implementation of the coding and robotics subject in South African schools
    (Stellenbosch : Stellenbosch University, 2023-11) Heyns, Jana; Van Eeden, J; Van Rooyen, GJ; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Engineering Management (MEM).
    ENGLISH ABSTRACT: The development of the Fourth Industrial Revolution (4IR) introduced technological innovation, which led to digital transformation in the workplace. This transformation demands that employees be equipped with 21st-century skills to keep up with the fast-paced development. The Department of Basic Education South Africa plans to prepare students for these demands by introducing the subject of Coding and Robotics from grades R to 9 to the national school curriculum. However, there is a lack of research available on the practical aspects that a school should consider in the unique South African school context. This study contributed to the research by developing a framework of factors that a school should consider when implementing the subject of Coding and Robotics in SA primary schools. The context of the study was provided by investigating the benefits of Coding and Robotics as part of STEM education in the context of the 4IR. The framework was developed by first reviewing relevant literature and policy statements from countries that already teach the subject in their schools. This information was used to identify draft factors that impact the successful implementation of the subject. The draft factors were used as input in the data collection process. Eleven Subject Matter Experts (SMEs) were curated to participate in the study. The SMEs include teachers with Coding and Robotics teaching experience, teaching experts related to the development of computational thinking skills and owners or employees of private robotics education providers. Following a snowball method, interviews were conducted with the SMEs to provide practical insights into the considerations of the implementation in the SA context. Their suggestions and experiences were analysed to populate the framework of factors. The six factors included in the framework are the teacher, infrastructure, artefacts, curriculum, support network, and budget. Each factor was expanded into attributes that provided more detailed considerations and suggestions. A synthesis of the interviews revealed the existence of specific challenges faced by the participants in the SA context. The possibility of certain relationships existing among the factors was discovered. These hierarchies and influences were explored, although conclusive results could not be drawn due to the limited dataset. Nonetheless, the limited observations suggested that the teacher factor is considered the most influential in the process of successfully implementing the subject of Coding and Robotics, which could suggest prioritising it during the implementation process. The research was carefully evaluated to ensure the soundness of the findings. The continuous verification and validation strategy confirmed the reliability of the developed framework. The expanded factors present a practical overview of the considerations that will influence the success of the implementation of Coding and Robotics in SA primary schools.
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    Development of a human digital twin for human-centric dispatching for an assembly process
    (Stellenbosch : Stellenbosch University, 2023-12) Kneissl, Anja; Jooste, Dr. J. L.; Bitsch, Prof. Dr. Günter; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Engineering Management (MEM).
    ENGLISH ABSTRACT: Assembly workers experience higher stress levels than other positions. Reducing the stress of assembly workers is important for workers and companies as it has an impact on long-term health, employee commitment to the company and job satisfaction. The research presented in this thesis develops a new approach to strain reduction using an assembly process. Considering workers' preferences increases their autonomy and control over their work, which is expected to reduce strain. This is implemented in worker dispatching for an assembly process. The approach presented in this thesis considers the individual worker and their preferences regarding the type of task in the dispatching procedure. In addition, machine learning is used to predict the worker's strain in a particular task to evaluate the worker with the least expected strain in that task from a group of workers. By incorporating preferences, worker autonomy can be increased. Combined with strain prediction using machine learning, which incorporates individual worker strain based on various factors into the dispatching system, worker strain can be reduced. A human-centric dispatching system that includes a human digital twin as a digital representation of the individual worker is developed. The human digital twin consists of three elements: data storage, strain prediction and strain monitoring. The worker attributes included in the data storage are availability, age, assembly competence, preference regarding the type of task, and worker strain using heart rate as an objective measure. The worker's strain is predicted using task-specific training data from three scooter assembly workstations at the "Werk 150" logistics learning factory at the University of Reutlingen, Germany. The worker attributes included in the training data for strain prediction are task type, age, time of day, preference, and assembly skill. A Random Forest regressor trained on the augmented dataset 1 is used to predict the median heart rate. The mean average error is 5,64 beats per minute and the deviation between predicted and test values is 39,66 %. The developed dispatching procedure considering preferences and including strain prediction is evaluated in Werk 150. The field experiment indicated that using the developed human-centric dispatching system including the worker's human digital twin leads to a decrease in strain. Using the NASA TLX as a subjective strain measurement, the average worker strain decreases by 27 % measured with the NASA RTLX and by 33 % measured with the weighted NASA TLX across all three assembly tasks and subscales. Using heart rate as an objective strain measurement, the strain decreases by 42,27 % when measuring mean heart rate and by 41,08 % for median heart rate. Compared to random task assignment, the developed human-centric dispatching system, therefore, reduces worker strain on average by 35,84 %.In conclusion, considering the preferences of the workers in dispatching and combining this with strain prediction leads to strain reduction. This is a starting point for increased human-centricity in production and a step forward in the implementation of the Industry 5.0 concept.
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    An investigation into computer vision methods to identify material other than grapes in harvested red wine grape loads.
    (Stellenbosch : Stellenbosch University, 2023-12) Kleyn, Riaan; Von Leipzig, Konrad; Palm, Daniel; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Engineering Management (MEM).
    ENGLISH ABSTRACT: Mass wine production companies across the globe are provided with grapes from winegrowers that predominantly utilise mechanical harvesting machines to harvest wine grapes. Mechanical harvesting accelerates the rate at which grapes are harvested, allowing grapes to be delivered faster to meet the demands of wine cellars. The disadvantage of the mechanical harvesting method is the inclusion of material-other-than-grapes (MOG) in the harvested wine grape loads arriving at the cellar. The MOG degrades the quality of wine that can be produced while it is also costly to transport and discard and it can cause machine downtime. This paper seeks to find an optimal computer vision method capable of detecting the amount of MOG within a wine grape load. A MOG detection method will encourage winegrowers to deliver MOG-free wine grape loads to avoid penalties which will indirectly enhance the quality of the wine to be produced. Traditional image segmentation methods were compared to deep learning segmentation methods based on images of wine grape loads that were captured at a wine cellar. The Mask R-CNN model with a ResNet-50 convolutional neural network backbone emerged as the optimal method for this study to determine the amount of MOG in an image of a wine grape load. Furthermore, a statistical analysis was conducted to determine how the MOG on the surface of a grape load relates to the mass of MOG within the corresponding grape load.
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    An investigation of whether an adaptable serious game for multiple stakeholder perspectives can achieve stakeholder objectives
    (Stellenbosch : Stellenbosch University, 2023-12) Krüger, Monique; Von Leipzig, Konrad; Hummel, Vera; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Engineering Management (MEM).
    ENGLISH ABSTRACT: The world is becoming increasingly digitalized. People have become accustomed to learning and interacting with their environment through technology. The Covid-19 pandemic has accelerated the adoption of digital learning methods, particularly for the younger generation entering the education system and the workforce. Consideration of digital tools and learning approaches is essential for future learning. The growing demand for online learning necessitates the incorporation of digital learning elements such as serious gaming into education and training systems. Among the education and training systems that can benefit from the integration of digital learning extensions are learning factories. Digital capabilities such as digital twins and models allow for further investigation into using digital serious games as an extension of learning factories. Learning factories are intended for a range of different learning, training, and research purposes. Multiple stakeholders are involved in these activities. In this context, serious games should be adaptable across various stakeholder perspectives to maximize the value gained from the cost and time required for their development. Research into the creation of adaptable serious games for many stakeholder viewpoints must first assess if such development can achieve the desired objectives for the various stakeholder perspectives. This study investigates the development of adaptable serious games for multiple stakeholder perspectives, focusing on the practical development of a digital adaptable serious game for stakeholder perspectives. This thesis presents the design and development of a prototype serious game for multiple stakeholder perspectives. The serious game is developed using the Unity game engine and a digital model of the Fischertechnik Training Factory 4.0. Conceptual guidelines are presented that explore design considerations for how one serious game can be made adaptable across multiple stakeholder perspectives. Game versions are developed for multiple stakeholders to explore the feasibility of using a base game with shared functionalities as well as stakeholderspecific adaptation of functionalities to satisfy different sets of desired objectives.