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The development of machines explores the development of a manufacturing system. This perspective offers challenges adjacent to the materials, the product that wants to be manufactured, the technical considerations that must be covered during implementation and the manner of delivering the products to the user. This article addresses the design and development of machines, on the one hand the redesign of a multifunctional platform, and on the other hand a 3D food printer. The second is emphasised, since its main objective is to help the food problems that exist today. The design phases, aspects considered for its implementation such as food safety, the choice of the substrate, the functionality of the food, transportation and the pre- and post-processes required. In this manner, the selection and innovation of materials constitutes a primary source of innovation and research in the integral development of a 3D food printer. The main source of value of these both cases lie in customisation. The experiences of a multidisciplinary research team in the development processes are compiled and the design and creation process are shown, in order to contribute to deal with design and development to tackle fundamental problems of society. [ABSTRACT FROM AUTHOR]

This study presents the development of an event-driven hybrid control for position and force tracking applied on a mobile robotic manipulator for metal recycling tasks. The suggested controller operates in a sequenced strategy starting from a fixed spot, moving the mobile device towards a targeted zone ($\Omega _i^r$ Ω i r ) from where the i-th piece-to-be-recycled is attainable (considering the arm manipulation). Once the event of entering the zone is completed, the mobile robot is fixed at a position, and the end-effector of the robotic arm is enforced towards the piece-to-be-recycled. When the end-effector touches the piece in a given spot ($\Omega _i^e$ Ω i e ), the hybrid control changes to the force tracking intending to carry the piece towards the spot ($\Omega _i^p$ Ω i p ) where it ill be processed. Each piece location is identified based on a vision-based system that applies deep learning tools using convolutional neural networks. A multi-physics numerical simulation illustrated the application of the developed controller in a realistic scenario, showing all the elements of the event-driven operation. To validate the suggested controller, the comparison with a robust control that works on a wide range of carrying mass confirms the operational improvement of the event-driven hybrid position and force design. [ABSTRACT FROM AUTHOR]

Vehicle safety relies on the capacity of vehicle systems to decrease the likelihood of biomechanical injuries to both vehicle occupants and pedestrians. This can take the form of active or passive measures applied to the materials and their geometry in the event of an impact. Nevertheless, due to the nonlinear behaviour of materials and deformation, a distinct collapse process emerges that requires analysis; this analysis can be virtual or through experimental tests. While observed performance can be ascertained through experimental designs, executing such a design for every parameter combination can be time-consuming and costly. This study employs a Long Short-Term Memory (LSTM) to predict the energy absorbed and crushing force of thin-walled aluminium 6063 T5 tubes subject to different collapse triggers and heat treatments. The LSTM model is constructed using experimental data derived from experiments that consider trigger shape, area, trigger position, furnace duration, cooling temperature, and heat treatment soaking method. The LSTM model achieved a Root Mean Square Error (RSME) of 0.56 and 0.0025 for crushing force and energy absorption, respectively. LSTM proves to be a valuable tool for predicting results in nonlinear analysis, particularly in the context of crush behaviour. Also, a comparison of the LSTM and finite element analysis (FEA) predictive performance is presented. [ABSTRACT FROM AUTHOR]

Summary: In non‐cooperative multi‐player games, an average sub‐gradient (ASG) strategy for finding an ϵ$$ \epsilon $$‐Nash equilibrium. For convex (but not necessarily strictly convex) individual cost functions, the function convergence of the multi‐player game is demonstrated. Applying Tanaka's formula that characterizes the Nash equilibrium leads to finding the strategy for each player. A min‐max formulation defines the corresponding solution for each player. The main result is proving the reachability of the desired regime, obtaining an explicit upper bound for Tanaka's function decrement. Detailed analyses for two‐player and multi‐player games are developed. For a class of two‐player games with a convex performance function, a set of numerical studies shows the applicability of the proposed method. So, the tracking of the arm in 3‐links robot is considered as an illustrative example. [ABSTRACT FROM AUTHOR]