Micro engraving on 316L stainless steel orthopedic implant using fiber laser

Abstract

It has been seen that there is a growing demand for the production of micron size grooves with a uniform flat surface, minimal heat-affected zone (HAZ) thickness, debris deposition, microcrack, microcavity, and recast layer thickness on orthopedic implants. In the present study, a multi-response optimization approach is adopted for fiber laser-based micro engraving to enhance groove characteristics and implant functionality during fixation. Grey relational analysis (GRA) is applied to analyze the experimental data sets and to decide key input factors' (pulse frequency, scanning speed, number of passes, laser power) level setting. These optimized data sets maintain groove quality (groove width, HAZ, debris deposition, recast layer thickness). Laser power and pulse frequency have been identified as the most significant factors for controlling groove quality. A neural network model has been developed and trained through experimental data sets. During analyzing the model, it has been recognized that the regression analysis score is very nearer to 1, and model performance is 2.45e-18. It represents its adaptability for determining the response quality characteristics when factor level settings are out of defined boundary. Improvements in groove quality are noticed in terms of minimal HAZ thickness, debris deposition, recast layer thickness, microvoids, and microcavity under optimum engraving conditions.