Improving Computational Accuracy during the Entire 3D Printing Process Research Progress

As a disruptive manufacturing technology, 3D printing has found widespread application in sectors such as aerospace and biomedicine. However, challenges persist throughout the entire process concerning the precision transfer and collaborative optimisation of model construction, slicing and layering, path planning, and quality inspection. This paper systematically reviews methods for enhancing computational accuracy in this field, analysing core technological advancements across key stages: NURBS and implicit surface modeling, multi-source data fusion, adaptive/multi-axis slicing, intelligent algorithm optimization routes. Research has found that there are problems with the existing methods for inspecting complex lumens, fusing multiple sources of data precisely, and performing real-time closed-loop control. In the future, it will need to deeply integrate smart algorithms and multi-sensor technology to make 3D printing more efficient, accurate, and intelligent, which can provide strong support for high-end manufacturing and personalized healthcare.