Metal additive manufacturing (MAM), also known as “metal 3D printing,” has been around for over 30 years. In the past decade, however, there has been a surge of interest in the technology as it moves from prototype to low-rate and high-rate production for increasingly critical applications for more industries. With this shift comes the challenge of determining design properties for the first time in many years. Not only is it necessary to determine basic material properties, but it is also necessary to accommodate new geometries and design concepts as well. While some of the methods and approaches are common to other product forms, others are unique to MAM.
MAM is a process that uses a laser or electron beam to melt metal powder and create complex, three-dimensional parts directly from a computer-aided design (CAD) model. The process offers several advantages over traditional manufacturing methods, including the ability to create complex geometries with less waste, shorter lead times, and lower tooling costs. However, as the technology has matured and gained wider acceptance, the need to determine design properties has become increasingly important.
One of the main challenges in determining design properties for MAM is the lack of standardized testing methods. While traditional manufacturing methods such as casting, forging, and machining have established testing methods, MAM is still in the process of developing these methods. The lack of standards can make it difficult to compare results between different MAM processes and materials.
Another challenge is the need to understand the microstructure of MAM parts. The microstructure refers to the arrangement of the atoms in the metal and can have a significant impact on the properties of the part. The microstructure of MAM parts is often different from that of parts made using traditional methods, which can make it difficult to predict the properties of the part.
To overcome these challenges, product teams must take a methodical approach to determining design properties for MAM parts. This involves understanding the process parameters, material properties, and part geometry, and using this information to develop testing methods that can accurately predict the performance of the part.
One approach to understanding the process parameters is to use a design of experiments (DOE) approach. DOE involves systematically varying the process parameters and measuring the resulting properties of the part. This can help identify the optimal process parameters for a given material and part geometry.
Another approach is to develop a process map for the MAM process. A process map is a graphical representation of the process parameters and their impact on the part properties. This can help identify the key process parameters that have the most significant impact on the part properties.
Understanding the material properties is also critical in determining design properties for MAM parts. This involves characterizing the mechanical, thermal, and chemical properties of the material. Traditional testing methods such as tensile testing, hardness testing, and impact testing can be used to determine these properties.
In addition to the traditional testing methods, there are also some unique testing methods that are specific to MAM. One such method is the use of computed tomography (CT) scanning to analyze the internal structure of the part. This can help identify defects such as voids, cracks, and inclusions that can affect the part properties.
Another unique testing method is the use of digital image correlation (DIC) to analyze the deformation of the part under load. DIC involves analyzing images of the part before and after loading to determine the displacement and strain of the part. This can help identify areas of the part that are experiencing high stress and may be prone to failure.
Once the process parameters and material properties have been characterized, the next step is to determine the part geometry. This involves analyzing the CAD model and identifying areas of the part that may be prone to failure. Finite element analysis (FEA) is a common tool used to simulate the behavior of the part under different loads and boundary conditions. This can help identify areas of the part that are experiencing high stress and may be prone to failure.
FEA can also be used to optimize the part geometry for the MAM process. This involves modifying the CAD model to minimize distortion, reduce residual stress, and improve the part properties. One approach to this is topology optimization, which involves using algorithms to generate an optimal shape for the part based on a set of design constraints.
Once the testing methods have been developed and the part geometry has been optimized, the next step is to validate the design properties. This involves testing the part under real-world conditions to confirm that it meets the design requirements. This can include testing the part under different loads, temperatures, and environmental conditions.
One example of MAM in the mobility industry is the use of the technology to produce lightweight, complex parts for aerospace applications. MAM has been used to produce parts such as brackets, hinges, and latches that are up to 60% lighter than their traditionally manufactured counterparts. These parts offer significant weight savings, which can lead to improved fuel efficiency and reduced emissions.
To ensure that these parts meet the stringent safety requirements of the aerospace industry, product teams have had to develop new testing methods and standards. For example, the Federal Aviation Administration (FAA) has developed a set of guidelines for qualifying MAM parts for use in aircraft. These guidelines include requirements for material properties, process parameters, and testing methods.
Looking to the future, there are several areas where further research is needed to fully realize the potential of MAM in the mobility industry. One area is the development of new materials that are specifically designed for the MAM process. These materials could offer improved properties over traditional materials and enable the production of parts with even greater complexity.
Another area is the development of in-process monitoring and control systems for the MAM process. These systems could help identify defects and deviations in real-time, allowing for immediate corrective action. This could help improve the quality and consistency of MAM parts and reduce the need for post-processing.
In conclusion, determining design properties for metal additive manufacturing in the mobility industry is a complex and challenging task. However, with the right approach and testing methods, it is possible to develop parts that meet the stringent requirements of the industry. As MAM continues to mature and gain wider acceptance, it will become increasingly important for product teams to understand the unique challenges and opportunities presented by this technology. By doing so, they can unlock the full potential of MAM to produce lightweight, complex parts that offer significant benefits in terms of cost, lead time, and performance.
