Though buzzier than ever in 2019, 3D printing has existed for at least 35 years, beginning with the invention of stereolithography in 1984 and accelerating with the development of fused deposition modeling in 1988. More recently, however, additive manufacturing for series production, paired with an uptick in lower-cost metal 3D printers, has elevated the industrial 3D printing industry to previously unreachable heights.
What is Industry 4.0? This buzzword seems to have been thrown around for quite some time now. How is it affecting today’s NDT manufacturing processes compared to the past? What new technologies have risen from Industry 4.0 in the past few years to benefit NDT? To answer all these questions, it is important to look back and understand how it came to be.
On every trade show floor featuring additive manufacturing, there’s a growing selection of additive processes for making production parts that stand alongside machines more commonly associated with prototyping. An essential aspect of additive’s transition from prototyping to production is data collection.
Fabtech showcased new 3D printing technology as well as quality tools. In the smart manufacturing hub, companies such as Microsoft, Stratasys, SLM Solutions, Desktop Metal and Memex made the case for data-driven manufacturing and described how companies can get there. As Microsoft said, “AI is ready. Are you?”
Every day additively manufactured (AM) parts are being used in new applications as the industry rapidly matures. As additive parts become more economical for small productions runs and move beyond use solely in tooling and prototyping, the need to nondestructively inspect parts for quality increases as well.
As additive manufacturing gains traction as a means for making production parts, standards need to keep pace, ensuring quality and constancy across industries. Already, ASTM and other standards bodies are leading the effort.
For nearly 85 years, quality professionals have used SPC to monitor, control, and improve their manufacturing processes. Using statistical tools to detect variability before a sub-standard part can be produced—and thus, reduce scrap, downtime, and rework costs—began in the mid-1920s with handwritten control charts.