Evaluation of manufacturability for the effective decomposition of product when layered build

The possibility of evaluating the manufacturability of product on the basis of a statistical analysis of the elementary volumes distribution of original 3D model is considered. The proposed indicator allows for quantitative evaluation of the efficiency of applying structural reversible decomposition of a product in order to rationally place it in the workspace of layered build of additive technology installation. The definition of manufacturability index is carried out according to the proposed algorithm for analyzing the distribution of product material in workspace. The algorithm is performed by using voxel 3D-model of product. Approbation of the proposed evaluation algorithm is performed on the example of test models of industrial products. The estimated data for determining the manufacturability level is presented depending on division parts number of workspace with the product. The results show sufficiently high degree of confidence and informative for development of design and technological preparation of additive manufacturing of complex products

Спосіб пошарової побудови виробів на базі структурної декомпозиції вихідної тріангуляційної 3D моделі

Спосіб пошарової побудови виробів на базі структурної декомпозиції вихідної тріангуляційної 3D моделі включає попереднє розбиття виробів на частини, розміщення частин виробу на платформі установки, періодичне опускання платформи на задану величину кроку побудови, подальше формування шарів матеріалу і складання в готовий виріб. Визначають кути повороту виробу для орієнтації та варіанти декомпозиції в процесі розміщення на платформі з забезпеченням рівномірного розподілу елементарних об'ємів виробу на платформі

Optimal Packing of Irregular 3D Objects For Transportation and Disposal

This research developed algorithms, platforms, and workflows that can optimize the packing of 3D irregular objects while guaranteeing an acceptable processing time for real-life problems, including but not limited to nuclear waste packing optimization. Many nuclear power plants (NPPs) are approaching their end of intended design life, and approximately half of existing NPPs will be shut down in the next two decades. Since decommissioning and demolition of these NPPs will lead to a significant increase in waste inventory, there is an escalating demand for technologies and processes that can efficiently manage the decommissioning and demolition (D&D) activities, especially optimal packing of NPP waste. To minimize the packing volume of NPP waste, the objective is to arrange irregular-shaped waste objects into one or a set of containers such that container volume utilization is maximized, or container size is minimized. Constraints also include weight and radiation limits per container imposed by transportation requirements and the waste acceptance requirements of storage facilities and repositories. This problem falls under the theoretical realm of cutting and packing problems, precisely, the 3D irregular packing problem. Despite its broad applications and substantial potential, research on 3D irregular cutting and packing problems is still nascent, and largely absent in construction and civil engineering. Finding good solutions for real-life problems, such as the one mentioned above, through current approaches is computationally expensive and time-consuming. New algorithms and technologies, and processes are required. This research adopted 3D scanning as a means of geometry acquisition of as-is 3D irregular objects (e.g., nuclear waste generated from decommissioning and demolition of nuclear power plants), and a metaheuristics-based packing algorithm is implemented to find good packing configurations. Given the inefficiency of fully autonomous packing algorithms, a virtual reality (VR) interactive platform allowing human intervention in the packing process was developed to decrease the time and computation power required, while potentially achieving better outcomes. The VR platform was created using the Unity® game engine and its physics engine to mimic real-world physics (e.g., gravity and collision). Validation in terms of feasibility, efficiency, and rationality of the presented algorithms and the VR platform is achieved through functional demonstration with case studies. Different optimal packing workflows were simulated and evaluated in the VR platform. Together, these algorithms, the VR platform, and workflows form a rational and systematic framework to tackle the optimal packing of 3D irregular objects in civil engineering and construction. The overall framework presented in this research has been demonstrated to effectively provide packing configurations with higher packing efficiency in an adequate amount of time compared to conventional methods. The findings from this research can be applied to numerous construction and manufacturing activities, such as optimal packing of prefabricated construction assemblies, facility waste management, and 3D printing

Investigation of production planning for environmental sustainability improvement in polymer LPBF

Additive Manufacturing (AM), also known as 3D printing, refers to a family of manufacturing technologies that use a layer-by-layer approach to converting digital models into physical components. The adoption of AM has offered significant sustainability benefits such as improved resource efficiency, extended product life, and reconfigured value chains. However, despite these prospective benefits, the full potential of the sustainable aspects of AM has not been explored, due to a lack of knowledge regarding environmental sustainability improvement in AM. This thesis documents work on investigating the environmental sustainability improvement in polymer Laser Powder Bed Fusion (LPBF) from a production planning perspective. Three studies were performed to understand how to improve the environmental sustainability of AM: modelling, optimisation, and network effects investigation. The modelling study revealed environmental sustainability elements in polymer LPBF and their share in the environmental impacts of polymer LPBF. To do this, a layer-based environmental sustainability model was established. In this model, the build time, energy consumption, embedded energy, material consumption, and risk of build failure were considered. It was shown that embedded energy dominated the total energy consumption (approximately 40 to 60%). Meanwhile, the energy relevant to risk of build failure contributed to approximately one third of expected total energy consumption at full capacity utilization. The study of optimisation demonstrated that integrated optimisation plays a significant role in improving energy efficiency during the additive process. In this study, an exploratory simulation was used to investigate integrated optimisation through the system (or computational tool) development. Building on this, a new framework of integrated optimisation was established. Build volume packing and scheduling were jointly optimized. Specifically, a bottom-left heuristic, capacity aggregation algorithm and exhaustive search were used to support integrated optimisation. Specific energy consumption was regarded as the optimisation objective. It was found that integrated optimisation approach had a significant effect on improving energy efficiency of polymer LPBF at higher demand profiles. The developed system allowed a lower specific energy consumption during the additive process than the results in extant literature. The study of network effects revealed the extraordinary potential for environmental sustainability improvement in polymer LPBF by investigating the environmental network effects in the AM platform. Environmental network effects reflect the mutual impact regarding quantity and benefits (i.e., energy efficiency and lead time) between customers and machine operators (or manufacturers) in AM platform. Specifically, machine operators are assumed to care about energy efficiency (i.e., specific energy consumption) and customers are assumed to concern lead time (i.e., schedule attainment). Another computational tool was developed to support this investigation. A build volume-based capacity aggregation algorithm was developed in this system. Specific energy consumption and schedule attainment were considered as the metrics to uncover environmental network effects in the AM platform. It was shown that there were indirect network effects embedded in the AM platform. These powerful effects are likely to help manufacturers improve energy efficiency and help customers reduce waiting time. Based on integrated optimisation, using network effects in the AM platform shows greater performance in improving the environmental sustainability of AM

Investigation of production planning for environmental sustainability improvement in polymer LPBF

Additive Manufacturing (AM), also known as 3D printing, refers to a family of manufacturing technologies that use a layer-by-layer approach to converting digital models into physical components. The adoption of AM has offered significant sustainability benefits such as improved resource efficiency, extended product life, and reconfigured value chains. However, despite these prospective benefits, the full potential of the sustainable aspects of AM has not been explored, due to a lack of knowledge regarding environmental sustainability improvement in AM. This thesis documents work on investigating the environmental sustainability improvement in polymer Laser Powder Bed Fusion (LPBF) from a production planning perspective. Three studies were performed to understand how to improve the environmental sustainability of AM: modelling, optimisation, and network effects investigation. The modelling study revealed environmental sustainability elements in polymer LPBF and their share in the environmental impacts of polymer LPBF. To do this, a layer-based environmental sustainability model was established. In this model, the build time, energy consumption, embedded energy, material consumption, and risk of build failure were considered. It was shown that embedded energy dominated the total energy consumption (approximately 40 to 60%). Meanwhile, the energy relevant to risk of build failure contributed to approximately one third of expected total energy consumption at full capacity utilization. The study of optimisation demonstrated that integrated optimisation plays a significant role in improving energy efficiency during the additive process. In this study, an exploratory simulation was used to investigate integrated optimisation through the system (or computational tool) development. Building on this, a new framework of integrated optimisation was established. Build volume packing and scheduling were jointly optimized. Specifically, a bottom-left heuristic, capacity aggregation algorithm and exhaustive search were used to support integrated optimisation. Specific energy consumption was regarded as the optimisation objective. It was found that integrated optimisation approach had a significant effect on improving energy efficiency of polymer LPBF at higher demand profiles. The developed system allowed a lower specific energy consumption during the additive process than the results in extant literature. The study of network effects revealed the extraordinary potential for environmental sustainability improvement in polymer LPBF by investigating the environmental network effects in the AM platform. Environmental network effects reflect the mutual impact regarding quantity and benefits (i.e., energy efficiency and lead time) between customers and machine operators (or manufacturers) in AM platform. Specifically, machine operators are assumed to care about energy efficiency (i.e., specific energy consumption) and customers are assumed to concern lead time (i.e., schedule attainment). Another computational tool was developed to support this investigation. A build volume-based capacity aggregation algorithm was developed in this system. Specific energy consumption and schedule attainment were considered as the metrics to uncover environmental network effects in the AM platform. It was shown that there were indirect network effects embedded in the AM platform. These powerful effects are likely to help manufacturers improve energy efficiency and help customers reduce waiting time. Based on integrated optimisation, using network effects in the AM platform shows greater performance in improving the environmental sustainability of AM