Natasha Botha

During the development of an aerial robotic platform, it is necessary to characterise the flight controller to ensure flight stability. Even though this can be done through open-source software like Betaflight, flight testing can result in potential crashes. To prevent this, this paper aims to design and manufacture a test rig to test and characterise the flight controller reliably. A 4-degree-of-freedom (DOF) test rig was developed to allow for vertical motion and characterise the yaw, pitch, and roll angles. It was also entirely constructed utilising additive manufacturing (AM). An iterative design approach was considered to improve the design after practical testing with a micro aerial robotic platform. This approach significantly enhanced the design of the ball joint and linear shaft to assist in better performance of the micro aerial robotic platform when using the test rig.

There is an increased interest in using aerial robotic platforms for indoor applications in industrial and manufacturing environments. One such example is stocktaking in warehouses, where the use of other mobile robotic systems is not efficient as they aren’t able to reach higher shelves or operate at the same speed as an aerial robotic platform. In this paper we discuss a prototype design for an aerial robotic platform that will be able to operate in GPS-denied areas, such as indoor warehouses. Suitable choices for the hardware and avionics are proposed based on the system and user requirements. The final design weighs 194 g, costs R 7 354.3, and has an estimated flight time of 5.5 min which is within the system requirements.

Statistical design of experiments (DoE) aims to develop a near efficient design while minimising the number of experiments required. This is an optimal approach especially when there is a need to investigate multiple variables. DoE is a powerful methodology for a wide range of applications, from the efficient design of manufacturing processes to the accurate evaluation of global optima in numerical studies. The contribution of this paper is to provide a general introduction to statistical design of experiments for a non-expert audience, with the aim of broadening exposure in the applied mechanics community. We focus on response surface methodology (RSM) designs — Taguchi Design, Central Composite Design, Box-Behnken Design and D-optimal Design. These different RSM designs are compared in the context of a case study from the field of polymer composites. The results demonstrate that an exact D-optimal design is generally considered to be a good design when compared to the global D-optimal design. That is, it requires fewer experiments while retaining acceptable efficiency measures for all three response surface models considered. This paper illustrates the benefits of DoE, demonstrates the importance of evaluating different designs, and provides an approach to choose the design best suited for the problem of interest.

This paper analyses a set of previously obtained experimental results on various clay fillers added to high density polyethylene (HDPE). The composite material was compounded using an extrusion process and manufactured into tensile test samples by means of hot pressing. Various manufacturing parameters (number of extrusions, press time, sample cooling method), material (polymer grade, clay type, clay weight loading) and testing parameters (strain rate) were investigated to determine their influence on the mechanical properties of the composite system. Exploratory data analysis was first employed by graphically representing the data using scatter plots to identify any main characteristics, patterns or anomalies. The statistical analysis was used to quantify the effects of the ultimate tensile strength by first conducting a one way ANOVA analysis before developing a linear model for the response variable analysis. For the percentage elongation to failure, the observations was first grouped into three groups, Brittle, Intermediate or Ductile. A linear discriminant analysis was performed to classify the groups considering a training set of 80% randomly selected observations and testing on the remaining 20%.