Energy-efficient and quality-aware part placement in robotic additive manufacturing

The advancements in autonomous robots for additive manufacturing (AM) are opening new horizons in the manufacturing industry, especially in aerospace and construction applications. The use of multiple robots and collaborative work in AM has rapidly gained attention in the industry and research community. Addressing the process planning challenges for single-robotic AM is foundational in addressing more advanced challenges at the collaborative multi-robotic level for AM. Among these challenges include the part placement problem which explores the optimal positioning of the part within the robot’s reach volume. The majority of the existing part placement algorithms take into account the part accuracy and manufacturing time for decision-making, while neglecting the implications of such decisions on energy efficiency and environmental sustainability. To address this gap, this paper presents a methodology for energy-efficient, high-quality part placement (EEHQPP) in robotic additive manufacturing. An energy-quality map is formulated and established to characterize the energy and quality variations across the robot’s workspace to inform the decision-making process. Two case studies (a container and a spur gear) are considered, and the performance of the proposed approach compared to the benchmark (i.e., default part placement by the 3D printing software) are evaluated. The proposed algorithm reduces both the energy consumption and maximum deviation error of the container (6.5% and 19.4%, respectively) and spur gear (1.4% and 32.7%, respectively) geometries manufactured by the robotic additive manufacturing system

Modified Genetic Algorithm To Determine The Location Of The Distribution Power Supply Networks In The City

The problem of combinatorial optimization in relation to the choice of location of power supplies in solving the problem of development of urban power distribution networks is considered. Two methods of placing power supplies and securing consumers for them have been developed to solve this problem. The first developed method is to place power supplies of the same size, and the second - different sizes. The fundamental difference between the created methods and the existing ones is that the proposed methods take into account all the material of the task and have specialized ways to encode possible solutions, modified crossbreeding and selection operators. Proposed methods effectively address the problem of low inheritance, topological impracticability of the found solutions, as a result of which the execution time was significantly reduced and the accuracy of calculations increased. In the developed methods the absence of the account of restrictions on placement of new power supplies which has allowed to solve a problem of application of methods for a narrow range of tasks is realized. A comparative analysis of the results obtained by placing power supplies of the same size and known methods was performed, and it was found that the developed method works faster than known methods. It is shown that the proposed approach provides a stable convergence of the search process for an acceptable number of steps without artificially limiting the search space and the use of additional expert information on the feasibility of possible solutions. The obtained results allow to offer effective methods for improving the quality of decisions made on the choice of location of power supply facilities in the design of urban electricit