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Inverse Analysis of Inconel 718 Laser-Assisted Milling to Achieve Machined Surface Roughness

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Abstract

This manuscript proposes an inverse analysis method for the machined surface roughness in laser-assisted milling on Inconel 718. The method solves the forward problem considering the tool profile and the elastic recovery of machined surface and applies the variance-based recursive method to guide the updating mechanism of process parameters to match the measurements. Subsequently, the inverse analysis identifies four process parameters of feed per tooth, tool tip radius, minimum cutting thickness, and tool tip angle, and finds the optimal solution for target performance, the surface roughness. The measurements are collected under the single beam coaxial laser-assisted milling spindle. The proposed modified Kalman filter algorithm introduces the gain coefficient G when updating the process parameters to improve robustness and accuracy. The inverse analysis is conducted on all measurements, and the average error of target performance is 0.460% when the laser is on and 0.394% when the laser is off. The average difference of process parameters is less than 5%, and the selection process is done in 50 loops within a minute. Therefore, the proposed inverse analysis model is robust, adaptive to different initial guesses and measurements, highly accurate, and saves computation time.

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Abbreviations

r t :

tool tip radius

α e :

tool tip angle

f z :

feed per tooth

f :

rotation angle

i :

number of revolution

R t :

tool radius

t min :

minimum cutting thickness

t c :

actual cutting thickness

Ra :

arithmetic average surface roughness

X i :

length of each gap between two revolutions in feed direction

\({\bar z_i}\) :

the arithmetical mean deviation between 0 and Zmax in each section

X n :

process parameters after nth loop

Ra exp R :

surface roughness measured from experiments

G :

gain coefficient

K n :

Kalman gain matrix

P n :

simulation covariance matrix

R :

error covariance matrix

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Authors and Affiliations

  1. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Yixuan Feng & Steven Y. Liang

  2. Department of Mechanical Engineering, Cheng Shiu University, Kaohsiung, Taiwan

    Tsung-Pin Hung

  3. Metal Industries Research and Development Centre (MIRDC), Kaohsiung, Taiwan

    Yu-Ting Lu, Yu-Fu Lin, Fu-Chuan Hsu, Chiu-Feng Lin & Ying-Cheng Lu

  4. Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian, Liaoning, China

    Xiaohong Lu

Authors
  1. Yixuan Feng
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  2. Tsung-Pin Hung
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  4. Yu-Fu Lin
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  5. Fu-Chuan Hsu
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Correspondence to Yixuan Feng.

Additional information

Yixuan Feng Ph.D. candidate in the George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology. His research interest is laser-assisted milling.

Tsung-Pin Hung Assistant Professor in the Department of Mechanical Engineering, Cheng Shiu University. His research interest is laser-assisted machining.

Yu-Ting Lu Senior researcher in Metal Industries Research and Development Centre. His research interest is precision machining.

Yu-Fu Lin Senior researcher in Metal Industries Research and Development Centre. His research interest is precision machining and multi-axis milling.

Fu-Chuan Hsu Senior research director in Metal Industries Research and Development Centre. His research interest is electrical discharge machining.

Chiu-Feng Lin CEO of Metal Industries Research and Development Centre. His research interest is precision machining.

Ying-Cheng Lu CTO of Metal Industries Research and Development Centre. His research interest is precision machining.

Xiaohong Lu Associate Professor in the School of Mechanical Engineering, Dalian University of Technology. Her research interest is micro-milling Inconel 718.

Steven Y. Liang Professor in the George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology. His research interest is modeling, monitoring, and control of advanced manufacturing processes and equipment.

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Feng, Y., Hung, TP., Lu, YT. et al. Inverse Analysis of Inconel 718 Laser-Assisted Milling to Achieve Machined Surface Roughness. Int. J. Precis. Eng. Manuf. 19, 1611–1618 (2018). https://doi.org/10.1007/s12541-018-0188-7

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  • DOI: https://doi.org/10.1007/s12541-018-0188-7

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