A D V A N C E D M A T E R I A L S & P R O C E S S E S | J A N U A R Y / F E B R U A R Y 2 0 2 3 2 9 Fig. 4 — Model-basedmaterial, process, and component definitions for advanced digital manufacturing. finished machined and in-situ sensing provides data that can be used for quality assurance. This brings us back to the discussion of physical atoms and digital bits. If this fabrication sequence is automated and interoperation transfer is simple and low-cost, the whole process for making the part becomes very akin to software, where processes like additive manufacturing are mere subroutines in a larger program, executed to make a part. This represents a paradigm of manufacturing for design. When the part is created, the data flow is then used to qualify this part, ideally through model-based certification. In addition, this data (along with physical test results) from the fabricated component can also be used to improve the certification models as well as the control algorithms. This is the role of machine learning. Achieving these goals will require traversing a long and challenging path, but one that our new Engineering Research Center is committed to follow. This represents a paradigm of manufacturing for design where process and product design are simultaneously optimized within constraints. The elements of this problem are noted in Fig. 3. Perspectives on Advanced Manufacturing Dr. David Furrer Pratt & Whitney Advanced manufacturing is changing the way we think about design and new product creation. There are several enabling technologies and capabilities that are making advanced manufacturing possible, including: rapid and accurate computational material and process modeling, advanced sensors, manufacturing informatics, and model guided and assisted qualification and certification. These foundational technologies are enabling new product capabilities and are radically changing how products are produced. The well-known industrial revolutions have each provided for new capabilities thathavechangedhowmaterials and components are manufactured. Industry 3.0 has largely been focused on and has delivered computerization and advanced automation. Industry 4.0 is currently focused on advancing simulation, digital data, adaptive controls, and continuous learning through artificial intelligence and machine learning methods. The current stage of the industrial revolution is manifested by the rapid development of advanced manufacturing technologies that provide a framework for digital engineering, manufacturing, communication, and optimization, including validation and certification. It is well known that materials and subsequent component properties are developed through specific manufacturing process paths. Local component properties can be understood as being controlled by location-specific processing conditions, including thermal, mechanical, and time dependent parameters (e.g., strain, strain rate, cooling rate). Computational models have matured to the point where they are utilized for virtually all advanced manufacturing processes. Similarly, materials models are becoming available with the capability to accurately predict the microstructure evolution and associated mechanical properties based on specific manufacturing processing paths. The ability to describe and define materials and manufacturing processes by means of model-based definitions enables true holistic model-based component definitions, which in turn provides for a number of critical capabilities now afforded through the application of digital engineering and advanced manufacturing (Fig. 4). The application of advanced manufacturing methods provides for greater control of properties on a component location-specific basis. Enhanced understanding of spatial distribution of mechanical properties and ability to control local mechanical properties provide a means for design engineers to further optimize component designs and product applications. Knowledge of systematic, physics-based spatial variation in material properties can provide for the development and utilization of smart testing, which targets critical locations that provide greatest insight into component capabilities and underlying manufacturing process control. Smart testing is a means of evaluating products and validating models and predicted local properties. This approach can readily support rapid qualification and certification of new components and manufacturing processes. Additive manufacturing is leading the charge in many areas of advanced manufacturing, including computational modeling, process design optimization, advanced sensors, and smart testing. Examples of integrated computational modeling in the design, manufacturing, and testing of AM components are becoming more prevalent. The culmination of the enabling technology elements will continue to drive advanced manufacturing methods
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