Multipass Turning Operation Process Optimization Using Hybrid Genetic Simulated Annealing Algorithm

<div>The mean <svg height="11.439pt" id="M156" style="vertical-align:-2.15067pt" version="1.1" viewbox="-0.0498162 -9.28833 22.5826 11.439" width="22.5826pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M449 634C442 637 425 643 405 650C376 660 341 666 307 666C181 666 98 590 98 485C98 400 170 343 215 310L246 288C307 243 343 204 343 147C343 67 291 18 219 18C104 18 61 124 51 202L23 199C28 124 27 71 27 47C47 22 122 -16 204 -16C324 -16 428 60 428 174C428 256 379 309 307 360L276 382C223 419 179 455 179 516C179 576 221 632 293 632C379 632 410 564 418 487L448 490C446 536 446 592 449 634Z" id="g113-84"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="472" vert-adv-y="472" xpluscharwidth="6.136"></glyph.data></g><g transform="matrix(.013,0,0,-0.013,6.136,0)"><path d="M368 703H309L44 -163H104L368 703Z" id="g113-48"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="412" vert-adv-y="412" xpluscharwidth="11.492"></glyph.data></g><g transform="matrix(.013,0,0,-0.013,11.497,0)"><path d="M822 650H589L583 622C660 617 677 607 674 561C672 534 664 481 647 390L600 137H596L273 650H126L120 622C176 620 194 615 207 594C221 571 225 557 214 504L161 257C141 166 129 112 121 85C108 42 83 30 29 28L23 0H260L266 28C193 33 173 42 176 89C178 122 186 172 202 255L256 527H259L583 -8H612L690 390C708 481 720 535 728 558C744 603 756 619 816 622L822 650Z" id="g113-79"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="839" vert-adv-y="839" xpluscharwidth="22.404"></glyph.data></g></svg> plot for each level of the HSAGA factors.</div>

Modelling and Simulation in Engineering

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Figure 2

Figure 2: Multipass Turning Operation Process Optimization Using Hybrid Genetic Simulated Annealing Algorithm 