Cyborgization yesterday, today and tomorrow: Selected perspectives and educational contexts

In this article we attempt to show that men have aimed at cyborgizing themselves from the dawn of history. We will present selected current perspectives on the process of cyborgization that we symbolically call the restoring and extending ones. We will show selected exemplifications of cyborgizing activities and visions on the cyborgization of tomorrow. The analysis of these concepts is crucial for teachers, as they show the dichotomous relation between education and cyborgization. The dichotomy is related to the fact that on the one hand futurologists claim cyborgization is a technology of a tremendous educational potential, and on the other hand they conjecture about the concept of a world of cyborgs that exists without education

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