How machine learning support additive manufacturing in 3d printing?4 answersMachine learning (ML) plays a crucial role in enhancing additive manufacturing (AM) processes in 3D printing by enabling accurate predictions and control mechanisms. ML algorithms, particularly neural networks, are utilized to optimize processing parameters, monitor quality, predict properties, and design models in AM. These algorithms aid in understanding complex patterns, improving microstructure design, and ensuring the quality and mechanical strength of printed parts. By integrating open-loop and closed-loop ML models, the effects of processing parameters on printed part properties can be monitored and optimized, leading to better quality outcomes in AM. ML also accelerates learning by creating physics-informed neural networks that accurately estimate and predict temperature distributions during the additive manufacturing process.
How do these analytical models compare with experimental data in predicting the behavior of metalic additive manufacturing processes?5 answersAnalytical models in metal additive manufacturing (AM) aim to predict process behavior and defects, offering insights to optimize parameters. These models, like Flash Heating (FH) and Sequential Flash Heating (SFH), provide reliable results quickly by lumping parts into meta-layers. However, the complexity of AM requires coupling with experimental tests and numerical simulations for accurate predictions. Data-driven methods, such as machine learning algorithms, are emerging to enhance microstructure design by efficiently predicting 3D microstructures based on process parameters. Analytical descriptions of heat accumulation effects in AM have been validated against temperature measurements, aiding in understanding and optimizing temperature distributions during printing. These models, along with physics-informed artificial intelligence approaches, show promise in predicting temperature histories accurately, especially in scenarios with limited data, potentially enabling real-time control of AM processes.
What are the steps in the additive manufacturing methodology?5 answersThe additive manufacturing methodology typically involves several key steps. Firstly, material deposition on a support structure is carried out. Subsequently, scanning of the deposited material to acquire topographical data is performed. This data is then processed to detect and geolocate any defects, ensuring quality control throughout the manufacturing process. Additionally, optimization calculations are conducted on contour scanning line energy density and position parameters based on the material properties to enhance part quality. Furthermore, in the additive manufacturing process, the formation of layers through selective melting and solidification is repeated to build the final part. Finally, bonding of filaments and energy-absorbing layers using an energy beam is employed to create a solid article.
How to apply ML model for optimizate parameter for additive manufacturing?5 answersMachine learning (ML) models can be applied for parameter optimization in additive manufacturing. These models can help in predicting and optimizing multiple properties of printed metal parts. One approach is to use a single neural network to optimize multiple part properties by identifying the optimal process parameter values. Another approach is to use a reinforcement learning (RL) methodology transformed into an optimization problem, where an off-policy RL framework based on Q-learning is used to find optimal parameter combinations. Additionally, open-loop and closed-loop ML models can be integrated to monitor the effects of processing parameters on the quality of printed parts. These models can provide a model-free approach to learning and optimization without any prior knowledge. By using experimental data, ML models can be trained to predict and optimize the desired properties of printed parts.
What works modelled screen filtering in openfoam?5 answersThe utility model disclosed a filtering screen for a filter that is simple in structure and convenient to process, with good filtering effect. Another utility model presented an automatic filtering screen molding machine that is high in machining efficiency and reduces production cost. A completely filtering screen pipe was designed with a reasonable structure, increased filtering area, high precision, and high integral strength, resulting in better suitability for the environment, convenient connection and assembly, high working efficiency, and reduced operation cost. An air filtering screen was provided with an increased dedusting area and high dust accommodating capacity, fulfilling the goals of large dedusting area and high dust absorbing capacity. Lastly, a filtering screen with a dust vibrating protection piece was introduced, which effectively avoids irreparable damage to the filtering screen body and prolongs its service life.
How to simulate heat transfer by spray cooling with openfoam?3 answersSpray cooling heat transfer can be simulated using OpenFOAM. The three-dimensional simulation model of intermittent spray cooling was developed to study its heat transfer characteristics. The wall superheat-wall heat flux relationship in spray cooling of a flat rectangular target can be derived using empirical time-temperature histories. A new solver called CHT-2FM, implemented in OpenFOAM, allows for the simulation of subcooled and saturated boiling flow with multi-domain thermal coupling. Forced convective heat transfer in a silicon microchannel heat sink can also be simulated using OpenFOAM. The numerical results obtained from the simulation show good convergence and the possibility of using OpenFOAM for simulating physical processes in channel radiators.