Development of an empirical regression model for predicting worm gear efficiency

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Honkalas R., Deshmukh B., Pawar P., Chandarki I., SALUNKHE S. S., Cepova L.

Frontiers in Mechanical Engineering, cilt.12, 2026 (ESCI, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12
  • Basım Tarihi: 2026
  • Doi Numarası: 10.3389/fmech.2026.1973503
  • Dergi Adı: Frontiers in Mechanical Engineering
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Applied Science & Technology Source, Compendex, INSPEC, Directory of Open Access Journals
  • Anahtar Kelimeler: design of experiments, efficiency improvement, module, regression analysis, worm gear, worm wheel teeth
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Gazi Üniversitesi Adresli: Evet

Özet

Worm gearboxes are widely used in industrial applications requiring high torque transmission and large speed reduction; however, their efficiency is strongly influenced by sliding friction, lubrication, heat generation and geometry. This study develops a constrained empirical modelling and optimisation methodology for an industrial single-start worm gearbox used in a continuous-duty soot-blower application. The input speed (342 rpm), centre distance and operating requirements are fixed, while the worm module and worm-wheel tooth count are varied over a manufacturable design space. Thirty-five design combinations are evaluated analytically, followed by local regression modelling and multi-criteria selection. To avoid kinematic redundancy, the revised regression uses module and worm-wheel tooth count as predictors because output speed is deterministically related to tooth count under fixed input speed. The selected configuration, m = 2.75 mm and Z2 = 43, gives a calculated efficiency of 74.6%, compared with 68.8% for the existing m = 2.54 mm, Z2 = 45 design. This corresponds to an absolute gain of 5.8 percentage points and a relative improvement of 8.43%, corresponding to approximately to 8.45%. The revised local regression gives R 2 = 0.9950, RMSE = 0.520 percentage points and MAPE = 0.574% within the investigated design domain. The methodology is intended as a transparent design-screening tool; experimental validation under controlled temperature, lubricant, surface and dynamic conditions remains necessary before production implementation.