Effect of nose radius on surface roughness of diamond turned germaniumlenses
At a Glance
Section titled âAt a Glanceâ| Metadata | Details |
|---|---|
| Publication Date | 2023-01-01 |
| Authors | Adeniyi Adeleke, Peter Babatunde Odedeyi, Khaled Abou-El-Hossein |
| Institutions | University of North Carolina at Charlotte, Nelson Mandela University |
| Citations | 2 |
| Analysis | Full AI Review Included |
Executive Summary
Section titled âExecutive SummaryâThis research investigates the optimization of Single-Point Diamond Turning (SPDT) for achieving optical-grade surface finishes on single crystal germanium, a critical material for infrared (IR) lenses.
- Core Challenge: Achieving the required complex surface finish on brittle materials like germanium using SPDT without inducing cracks or excessive roughness.
- Primary Focus: Analyzing the effect of two distinct diamond cutting tool nose radii on the resulting surface roughness (Ra) under varying conditions (feed, speed, depth of cut).
- Methodology: A Box-Behnken design was employed to systematically generate optimal combinations of machining parameters for testing.
- Critical Finding: Increasing the cutting tool nose radius consistently resulted in a decrease in surface roughness (Ra) across most experimental runs, demonstrating improved ductile-regime machining efficiency.
- Equipment Used: Machining was performed on a Precitech Nanoform 250 ultra grind lathe, and surface quality was measured using a Taylor Hobson PGI Dimension XL Profilometer.
- Commercial Value: The study provides critical guidance on selecting the appropriate nose radius, which is essential for achieving desired lens quality at reduced production cost for specific IR applications.
- Future Direction: Proposed research includes integrating machine learning to develop predictive models for providing real-time accept/fail criteria based on machining parameters.
Technical Specifications
Section titled âTechnical Specificationsâ| Parameter | Value | Unit | Context |
|---|---|---|---|
| Workpiece Material | Single crystal germanium | N/A | Brittle material used for infrared lenses. |
| Machining Process | Single-Point Diamond Turning (SPDT) | N/A | Precision technique used for surface generation. |
| Key Input Variables | Two different nose radii | N/A | Parameters studied for their effect on surface roughness. |
| Controlled Parameters | Feed, speed, depth of cut | N/A | Variables combined using the DOE. |
| Experimental Design | Box-Behnken design | N/A | Adopted for optimal parameter combination creation. |
| Machining Equipment | Nanoform 250 ultra grind | Precitech | Precision diamond turning lathe. |
| Measurement Equipment | PGI Dimension XL | Taylor Hobson | Surface Profilometer used for roughness measurement. |
| Key Outcome Trend | Decreased Ra | N/A | Observed with an increase in tool nose radius. |
| Model Validation Metric | Mean Absolute Error (MAE) | N/A | Used to compare the accuracy of the two developed models. |
Key Methodologies
Section titled âKey MethodologiesâThe experimental procedure focused on systematically evaluating the relationship between tool geometry and surface finish during the precision machining of germanium.
- Material Selection: Single crystal germanium was chosen as the workpiece material due to its application in high-quality infrared lenses and its inherent brittleness, which complicates machining.
- Parameter Selection: The primary machining variablesâfeed, speed, and depth of cutâwere selected, alongside the critical variable of two distinct tool nose radii.
- Design of Experiment (DOE): The Box-Behnken design was implemented to generate a statistically optimal and efficient combination of the selected cutting parameters for the experimental runs.
- Machining Execution: All machining operations were carried out on a Precitech Nanoform 250 ultra grind precision diamond turning lathe, ensuring high precision and repeatability.
- Surface Metrology: After each run, the surface roughness was measured using a Taylor Hobson PGI Dimension XL surface Profilometer to quantify the resulting surface quality (Ra).
- Data Analysis: The resulting surface roughness data was analyzed to determine the effect of nose radius. Mean Absolute Error (MAE) was calculated to validate and compare the accuracy of the predictive models developed for the two different nose radius conditions.
Commercial Applications
Section titled âCommercial ApplicationsâThe findings from this research are directly applicable to industries requiring ultra-precision machining of brittle materials, particularly in optics and sensor technology.
- Infrared (IR) Systems: Manufacturing high-performance germanium lenses for thermal cameras, night vision equipment, and advanced sensor arrays used in defense and security.
- Precision Optics Fabrication: Optimizing the SPDT process for other brittle optical materials (e.g., silicon, ZnS, ZnSe) where achieving a sub-micron surface finish is mandatory.
- Aerospace and Defense: Production of specialized, high-reliability optical components that must maintain structural integrity and optical clarity under extreme operational conditions.
- Process Control and Automation: Utilizing the derived relationships between nose radius and roughness to establish optimal machining recipes, reducing trial-and-error and improving manufacturing yield.
- Future Predictive Modeling: The planned integration of machine learning models will allow manufacturers to rapidly determine the feasibility (accept/fail) of a given surface generation task based on input parameters, streamlining quality control.
View Original Abstract
Abstract: The desire for quality infrared lenses with better surface finish has brought about the usage of brittle materials like germanium to be machined via a single-point diamond turning machining process. However, achieving the required surface finish is complex if special machining techniques and approaches are not employed. In this paper, the effect of two different tool nose radius parameters on the surface roughness of single point diamond turned germanium workpiece were studied and analyzed. The machining parameters selected for this experiment were feed, speed, and depth of cut. Box-Behnken design was adopted to optimally create a combination of cutting parameters. The machining operations were carried out on a Precitech Nanoform 250 ultra grind precision diamond turning lathe. Measurement of surface roughness after each run in both experiments was achieved using a Taylor Hobson PGI Dimension XL surface Profilometer. The resulting outcomes show that at most experimental runs, the surface roughness value decreased with an increase in nose radius, leading to improved ductile efficiency. Mean absolute error was also used to compare the accuracy validation of the two models. The study examines the machining of single crystal germanium to optical excellence and highlights how the selection of appropriate nose radius is critical to achieving the desired lens quality at reduced production cost and meeting specific applications. Future research directions will be carried out to understand the complex interaction between cutting tool nose radius and other factors like tool wear and cutting force, the crack formation in surfaces of diamond-turned brittle materials, and the use of machine learning to develop models that will allow manufacturers to provide an accept/fail answer for a given set of parameters for surface generation.