# The 3D Product Model Research Evolution and Future Trends: A Systematic Literature Review
## 4 Discussion
### The 3D Product Model Research Current State and Future Trends  
In discussions with industry experts, as we move through the 2020s into the 2030s and beyond, a number of trends are anticipated to play out in 3D product model research and industry applications. As organizations evolve and enterprise architecture interdisciplinary teams develop, the application of the 3D product model and the information they contain will become more broadly available beyond segregated departments such as information technology or operations. This will enable a federated approach in which having the 3D product model data will better enable teams to autonomously make decisions needed to reach the organization's key objectives. The standardization of data methods across industries will be a key enabler of this autonomy and accelerate the adoption of the 3D product model in areas such as analytics, machine learning, and the internet of things (IOT). The 3D product model will move from an analytic tool to becoming an integral part of the product lifecycle, where the evolution of the geometry and key characteristics of the product will also be able to be modeled in real time as the product moves through the production process.  
Knowing the evolution of the part geometry through the production process will enable marring the product digital twin with the process digital twin and create opportunities such as knowing what products are best to be ordered based on machine set-up, calibration, and dimensions for less changeover time via the sequencing of similar parts. The marrying of the product digital twin and process digital twin will also help mine out manufacturing patterns to generatively create designs and help determine the best manufacturing processes for a given 3D product model geometry. In addition, marring the product digital twin with the process digital twin can be coupled with the use of neural nets to reduce individual stock-keeping units (SKUs) through providing recommendations on 3D product model parts to combine based on both geometry and the manufacturing processes used to create them. As new parts are designed, the computer will also be able to provide design recommendations based on previously developed 3D product model parts clusters. This evolution combines product design with design for assembly and design for manufacturability.  
Over time, machine learning will be able to be used to associate manufacturing operations to the engineering 3D product model design. Any two parts that come together are joined somehow, and the computer will learn to determine how those geometries should be manufactured and assembled, including what tooling to use to do so. Machine learning of the 3D product model will also enable the development of numerical control (NC) program generation, and inspections plans will be automatically generated that are needed to manufacture, assemble, and inspect a part/assembly. Such developments will require parallel computation and quantum computing to account for the correlation and make such solutions possible.  
Eventually, the sustainability and repurposing of material will also become a cognizant component of the 3D product model design. The design, manufacturing processes, and operations will be done in such a way to allow that product to be easily upgraded and/or reused in different ways for subtle tasks.