Investigation of the Single-Point Diamond Turning of Optics-Grade Germanium Substrate

Öz

This study investigates the influence of cutting parameters on surface roughness (Sa) in diamond turning of optical-grade single-crystal germanium lenses. Specimens (Ø40 mm, 2 mm thick) were machined on an ultraprecision single-point diamond turning (SPDT) lathe; surfaces were characterized using Fizeau and white-light interferometers, accompanied by surface topography maps. Experiments followed a 3×2×2 factorial design (V: 1000–3000 rpm; d: 3–6 μm; fr: 2–5 mm/min) with constant tool geometry (rake γ = −25°, clearance α = 10°). The best Sa achieved was 1.065 nm, well below the accepted 4.640 nm threshold. From the measurements, two predictive models—linear and nonlinear—were established to relate Sa to the process parameters. The design enabled rapid exploration of settings and interactions (V–d–fr). The models provide practical tuning windows and sensitivity guidance to reach the 1–3 nm target band. Overall, the study offers a deployable framework for in-process optimization.

Anahtar Kelimeler

• Surface roughness
• optical metrology
• factorial design
• germanium substrate

Optik Kalite Germanyum Alttaşın Tek Nokta Kristal Elmas ile Tornalanmasının İncelenmesi

Öz

Bu çalışma, optik kalite tek kristal germanyum lenslerin tornalanmasında kesme parametrelerinin yüzey pürüzlülüğü (Sa) üzerindeki etkisini incelemektedir. 40 mm çap, 2 mm kalınlıktaki numuneler ultra hassas tek nokta elmas tornalamada işlenmiş, yüzeyler Fizeau ve beyaz ışık interferometreleriyle karakterize edilmiştir. Sa değerlerine eşlik eden topografik yükseklik haritaları türetilmiştir. Deneyler, 3×2×2 faktöriyel tasarım altında yürütülmüştür (V: 1000–3000 rpm; d: 3–6 µm; fr: 2–5 mm/dk). Takım geometrisi sabit tutulmuştur (talaş açısı γ=−25°, boşluk açısı α=10°). En iyi Sa değeri 1.065 nm olarak elde edilmiş ve bu değer kabul edilen 4.640 nm sınırının belirgin biçimde altındadır. Ölçümlerden elde edilen veri setiyle, Sa’yı parametrelerle ilişkilendiren lineer ve lineer olmayan iki tahmin modeli kurulmuştur. Tasarım, üretim ayarlarının hızlı taranmasına ve etkileşimlerin (V–d–fr) anlaşılmasına olanak vermiştir. Modeller, 1–3 nm hedef aralığına inmek için pratik ayar pencereleri ve duyarlılık bilgisi sağlamaktadır. Çalışma, yerinde proses optimizasyonu için uygulanabilir bir çerçeve sunar.

Anahtar Kelimeler

• Yüzey pürüzlülüğü
• optik metroloji
• faktöriyel tasarım
• germanyum alttaş

Kaynakça

1. K. P. Bouzoukis, G. Moraitis, V. Kostopoulos, and V. Lappas, “An Overview of CubeSat Missions and Applications,” Jun. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI).
2. E. F. J. Ring and K. Ammer, “Infrared thermal imaging in medicine,” 2012, IOP Publishing Ltd.
3. C. T. Dang, T. Balaz, and M. Kyselak, “Research on the Design of a 4-Lens Athermalized Refractive Optical System in the LWIR Spectral Region,” in 2025 10th International Conference on Military Technologies, ICMT 2025 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2025.
4. J. Wang, J. Frantz, E. Chumbley, A. Samad, and H. Hu, “An Explorative Study on Using Carbon Nanotube-Based Superhydrophobic Self-Heating Coatings for UAV Icing Protection,” Molecules, vol. 30, no. 17, Sep. 2025.
5. J. Zheng, Z. Liu, and X. Yu, “Thermal UAV image super-resolution via bimodal image feature fusion,” Int J Remote Sens, vol. 46, no. 1, pp. 205–225, 2025.
6. M. Khan, M. M. Su’ud, M. M. Alam, S. Karimullah, F. Shaik, and F. Subhan, “Enhancing Breast Cancer Detection Through Optimized Thermal Image Analysis Using PRMS-Net Deep Learning Approach,” J Imaging Inform Med, 2025.
7. M. Sudhi, D. K. Shetty, J. M. Balakrishnan, D. R. Prabhu, R. Kamath, and S. Girisha, “Thermal Imaging Applications in Shock Detection: Technological Advancements and Clinical Implications,” Feb. 01, 2025, Engineered Science Publisher.
8. Q. Liu et al., “Infrared thermography in clinical practice: a literature review,” Jan. 16, 2025.

9. P. Anu, P. Sharma, H. Kumar, N. Sharma, P. Rani, and K. Immanuvel Arokia James, “Machine learning in multi-spectral thermal imaging for enhanced detection of neurological disorders through thermoplasmonics,” J Therm Biol, vol. 129, Apr. 2025.
10. L. He et al., “Compact Infrared Imaging System Based on Small-Diameter Chalcogenide Glass Fiber Bundles,” J. Lightwave Technol, vol. 43, no. 14, pp. 6887–6893, 2025.
11. J. Q. Li, X. L. Xia, Z. W. Zheng, and X. Chen, “Inverse analysis on non-uniform temperature and emissivity fields based on multispectral infrared thermal image data,” Appl Therm Eng, vol. 267, May 2025.
12. M. Tunesi, N. E. Sizemore, M. A. Davies, and D. A. Lucca, “Effect of cutting speed in single point diamond turning of (100)Ge,” Manuf Lett, vol. 38, pp. 15–18, Nov. 2023, doi: 10.1016/j.mfglet.2023.08.144.
13. G. Zhang, J. Han, Y. Chen, J. Wang, and H. Wang, “Brittle-ductile transition and nano-surface generation in diamond turning of single-crystal germanium,” J Manuf Process, vol. 82, pp. 628–645, Oct. 2022.
14. G. Zhang, Z. Huo, J. Han, W. Zhang, and J. Zheng, “Cutting depth-oriented surface morphology control in diamond-turning brittle single-crystal germanium,” J Mater Process Technol, vol. 319, Oct. 2023.
15. K. K. Prasad, M. P. Singh, V. S. Negi, V. Mishra, S. Jha, and G. S. Khan, “Ductile machining of single-crystal germanium freeform optics via ultra-precision diamond turning for high-performance infrared imaging systems,” Precis Eng, vol. 96, pp. 380–397, Oct. 2025.
16. K. E. Tang, Y. C. Huang, W. T. Lin, Y. C. Cheng, and C. W. Liu, “Optimization of single-point diamond turning processes for single crystal calcium fluoride: A surrogate model for surface roughness prediction,” Int. J. Adv. Manuf. Technol, vol. 136, no. 2, pp. 775–787, Jan. 2025.
17. R. H. Myers, D. C. Montgomery and C. M. Anderson-Cook, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, 4th ed., Wiley, New York, 2016.
18. M. Hasegawa, S. Serega and R. Lindberg, “Surface roughness model for turning,” Tribol. Int., vol. 9, no. 6, pp. 285–289, 1976.
19. P. Benardos and G. Vosniakos, “Predicting surface roughness in machining: a review,” Int. J. Mach. Tools Manuf., vol. 43, no. 8, pp. 833–844, 2003.
20. T. Özel and Y. Karpat, “Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks,” Int. J. Mach. Tools Manuf., vol. 45, no. 4–5, pp. 467–479, 2005.