Research Articles (Industrial Engineering)

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Browsing Research Articles (Industrial Engineering) by Subject "Additive manufacturing"

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    Improving the R&D process efficiency of the selective laser sintering industry through numerical thermal modeling
    (2019) Olivier, Carlo Martin; Oosthuizen, Gert Adriaan; Sacks, Natasha
    ENGLISH ABSTRACT: The selective laser melting (SLS) industry is a relatively novel industry within the broad spectrum of available additive manufacturing (AM) technologies. As with most developing industries, the primary aim is to develop better quality components at reduced costs, often with a disregard towards efficiency. Resource efficiency is a key component of waste management and ties directly to sustainable manufacturing. In the SLS industry, large quantities of raw material are wasted during the machine calibration stage. Each time a new material is developed for SLS manufacturing a specific set of processing parameters need to be developed in order to ensure that high density, high strength components are produced. This paper investigates the possibility of replacing the current inefficient research and development (R&D) methods with numerical modeling. The fusion process can be simulated in a numerical thermal model using a combination of temperature dependent material properties and heat transfer principles.
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    Standard method for microCT-based additive manufacturing quality control 2 : density measurement
    (Elsevier, 2018) Du Plessis, Anton; Sperling, Philip; Beerlink, Andre; Tshabalala, Lerato; Hoosain, Shaik; Mathe, Ntombi; Le Roux, Stephan G.
    ENGLISH ABSTRACT: MicroCT is best known for its ability to detect and quantify porosity or defects, and to visualize its 3D distribution. However, it is also possible to obtain accurate volumetric measurements from parts – this can be used in combination with the part mass to provide a good measure of its average density. The advantage of this density-measurement method is the ability to combine the density measurement with visualization and other microCT analyses of the same sample. These other analyses may include detailed porosity or void analysis (size and distribution) and roughness assessment, obtainable with the same scan data. Simple imaging of the interior of the sample allows the detection of unconsolidated powder, open porosity to the surface or the presence of inclusions. The CT density method presented here makes use of a 10 mm cube sample and a simple data analysis workflow, facilitating standardization of the method. A laboratory microCT scanner is required at 15 μm voxel size, suitable software to allow sub-voxel precise edge determination of the scanned sample and hence an accurate total volume measurement, and a scale with accuracy to 3 digits.
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    System development for the five-axis extrusion of a photopolymer
    (Southern African Institute for Industrial Engineering, 2020-11-11) Kirkman, Daniel M.; Van Der Merwe, Andre F.; Campbell, Ian
    ENGLISH ABSTRACT: The development of a novel method of additive manufacturing involving the support-free extrusion of a photopolymer is discussed. The objectives of the paper are the presentation of mechanical and electrical hardware developments for a freeform additive manufacturing system, a discussion of the process developments that have taken place, and the presentation of the results from tests used to validate improvements to the system. Developments include the replacement of the UV light source with lasers, and the implementation of collision avoidance algorithms. The results include a significant increase in the speed of the extrusion process, and an improved toolpath viability achieved by the implementation of the collision avoidance algorithms.
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    Time-driven activity-based costing related to digital twinning in additive manufacturing
    (Southern African Institute for Industrial Engineering, 2021-05) Anderson, Anje Marjorie; Van der Merwe, Andre
    ENGLISH ABSTRACT: Many businesses in the additive manufacturing industry have limited equipment capacity. This method of using time-driven activity-based costing in collaboration with digital twinning will be advantageous to optimise their use of time and their capacity. Optimising the use of time is essential to ensure efficient process flow and to waste less time and money. To optimise, we need to analyse system dynamics and model system responses, to enable us to consider various scenarios iteratively. This paper first considers activity-based costing, driven by its most precious resource, time. Standard time is defined as the base parameter by which cost is calculated. Charge-out rates of elements are based on the actual cost of equipment apportioned to activities, based on the time spent using such equipment. The process chain is broken into elements, each of which incurs full cost when started. The value chain develops accordingly, enabling us to predict the actual cost of production. Second, the use of digital twinning to model standard time is considered. Stochastic variation is evident, but standard time can be allocated to each element in the process chain, given a certain confidence level. Together, a cause—effect prediction model can be developed. The model would predict the time that a process chain, consisting of known elements, would take. However, in the event of an occurrence out of the norm, the updated expected time can be predicted. Using the same rates, the new cost can be determined immediately. We propose that the digital twin can predict production cost, based on a statistically measurable stochastic variation of element duration and the time-varying charge-out rate.