Examination committee:

1. Chairman: Prof. Chandra Mouleeswara Rao Volla, Chemistry
2. External Examiner: Prof. Shrikrishna N. Joshi, IIT Guwahati
3. Internal Examiner: Prof. Ramesh Singh, Mechanical Engineering
4. Supervisors: Prof. Deepak Marla, Prof. Deepoo Kumar (MEMS), & Prof. Makarand Kulkarni

Abstract:

Laser Color Marking (LCM) is a versatile and environmentally sustainable technique for generating vibrant colors on metal surfaces through controlled oxide layer formation using pulsed lasers. Despite its advantages, LCM is highly sensitive to process parameters, leading to inconsistent color outcomes and limiting industrial adoption. This research aims to enhance the understanding and control of LCM for reliable and precise color generation on stainless steel.

A comprehensive experimental study was conducted using a nanosecond pulsed fiber laser on AISI 304 stainless steel by systematically varying process parameters, including power, pulse width, scanning speed, and repetition rate. Characterization techniques such as optical microscopy, scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), and X-ray photoelectron spectroscopy (XPS) were used to analyze oxide layer formation, thickness, and chemical composition. Results indicated that oxide thickness ranged from 160 to 850 nm, with chromium oxides producing greenish and bluish colors, while iron oxides resulted in brownish and reddish hues. Microhardness measurements showed increased hardness for chromium-rich surfaces. For the first time, the colors generated in the LCM process were erased by suitably identifying the process parameters. Besides, the color spectrum generated using LCM was expanded by investigating the effect of multiple scans.

An analytical thermal model was developed to correlate surface temperature and oxidation kinetics with color formation. It was observed that lower thermal conditions, characterized by lower surface temperatures and shorter heating durations favored the formation of chromium oxides, resulting in greenish and bluish hues. Conversely, higher thermal conditions promoted the formation of iron oxides, leading to reddish and greyish colors. These insights provide a deeper understanding of how thermal conditions influence the resulting color spectrum in LCM.

In addition to experimental and analytical efforts, predictive models were developed using machine learning algorithms, specifically Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN). These models were designed to enhance control and precision in the LCM process. The ANN model demonstrated strong correlations with experimental data in predicting RGB color values based on input process parameters, achieving correlation coefficients of 0.987, 0.992, and 0.994 for the R, G, and B color space values, respectively. The CNN model effectively predicted key laser parameters, with strong correlations of 0.896, 0.916, and 0.90 for power, pulse width, and frequency, respectively. These results highlight the effectiveness of ANN and CNN models in simulating the LCM process and predicting both color outcomes and process parameters with high precision.

In conclusion, the findings of this research significantly advance the understanding of laser color marking on stainless steel, providing a robust framework for optimizing the process through the integration of experimental analysis, analytical modeling, and machine learning techniques. By leveraging the developed predictive models, the precision and range of colors achievable through LCM can be significantly enhanced, offering new possibilities for industrial applications where color accuracy and durability are critical. This work lays the foundation for more reliable and consistent LCM processes, with the potential to transform its application across various industries.