Multi-Objective Optimisation Of Fiber Laser Cutting Of Stainless Steel Plates Using Taguchi-Based Grey Relational Analysis (Part 2)
Yusuf Alptekin Turkkan, Muhammed Aslan, Alper Tarkan, Özgür Aslan, Celalettin Yuce and Nurettin Yavuz of Bursa Uludag University and NUKON Laser Machine Metal Industry continue their study on various methods of cutting stainless steel using laser.
In this study, the data used in GRA were normalised. Due to the desired smaller roughness and kerf width, Equation (2) was used for normalisation. In this equation, minxi0(k) represents the minimum value of xi0(k), maxxi0(k) represents maximum xi0(k), and yi(k) represents the normalized value. x0 also represents the best optimized value.
After normalisation, the grey relational coefficient (GRC) is defined with Equations (3) and (4) to discover the relation between the real and ideal sequences. The weight factor determinations for each response are crucial in the GRA method. However, the effect of surface roughness and kerf width on the quality of the cutting surface is greater than other quality characteristics. Therefore, the weights of the responses should not be chosen without a reasonable quantitative basis to increase or decrease its importance.
In most of the studies, the weights of the responses are generally determined to be equal. ξ represents the coefficient of identification as 0 . . . 1, and is usually accepted as 0.5. Δmax represents the maximum and Dmin represents the Δminimum value of Δ0i. yi0(k) represents the comparable sequence and yi(k) represents the reference sequence. Δ0i also states the difference between y0(k) and yi(k).
In the last step, the grey relational grade (GRG) is obtained with the help of a normalized weight factor. The grey relational grade represents the correlation level between the comparable sequences and reference sequences. The best optimum level of cutting process parameters is defined by the highest grey relational grade.
Read more here —> https://shorturl.at/abgru
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