دانلود کتاب کاربردهای مدل‌سازی و یادگیری ماشین در تولید افزایشی

Additive manufacturing (AM), often referred to as 3D printing, is a preferred technique for producing components that are challenging to manufacture through conventional methods. This approach facilitates the direct fabrication of complex parts in a single step from a 3D digital model. Today, AM components are widely utilized across various sectors, including healthcare, aerospace, the automotive industry, energy, the marine sector, and consumer products [1]. Examples of such components include custom medical implants tailored to individual patients, aero-engine parts, intricate geometries with internal channels, lattice structures, and materials designed with location-specific chemical compositions, microstructures, and properties [2]. The materials used include metals, polymers, ceramics, and composites. Among these, metal and alloy printing is advancing most rapidly due to its growing applications, high demand, and ability to produce specialized, functional parts. Various AM techniques are employed based on the material, geometry, and complexity of the desired component [3]. For instance, powder bed fusion (PBF) and directed energy deposition (DED) are commonly utilized for metallic parts. These processes involve melting fine layers of powder or wire feedstocks using high-energy sources like lasers, electron beams, or electric arcs, followed by solidification to form the final part. Similarly, different processes are used in the industry for printing polymers, ceramics, and composite materials. In AM, a reduction in defects, maintaining geometric consistency, and control of microstructure and mechanical properties cannot be achieved by time-consuming and expensive experimental trials because of the involvement of many variables with a large parameter window. Physics-based computational models are often used as an alternative. Figure 1 provides a few examples of using such models in AM. However, the evolution of microstructures, properties, and defects depends on many complex physical processes, and the mechanistic understanding of many of these processes is not fully developed. The use of data science techniques such as machine learning can automate several steps, including process monitoring, defect detection, sensing, and process control, and can help in the selection of appropriate processing conditions to improve structure and properties. This would minimize the need for human intervention and significantly improve the process efficiency, productivity, and part quality, and reduce materials and energy waste and cost. Topics in this Special Issue include applications of modeling and machine learning for the novel design of additively manufactured products, additive manufacturing processes, alloy design, tailoring microstructures, customized mechanical and chemical properties, improved creep resistance, fatigue life, and serviceability, reducing defects and residual stresses and distortion. The scope of this Special Issue also includes all AM processes for alloys, ceramics, and polymers.