Abstract
Metal processing may benefit from innovative lean-and-green datacentric engineering techniques. Broad process improvement opportunities in the efficient usage of materials and energy are anticipated (United Nations Sustainable Development Goals #9, 12). A CO2 laser cutting method is investigated in this study in terms of product characteristics (surface roughness (SR)) and process characteristics (energy (EC) and gas consumption (GC) as well as cutting time (CT)). The examined laser cutter controlling factors were as follows: (1) the laser power (LP), (2) the cutting speed (CS), (3) the gas pressure (GP) and, (4) the laser focus length (F). The selected 10mm-thick carbon steel (EN10025 St37-2) workpiece was arranged to have various geometric configurations so as to simulate a variety of real industrial milling demands. Non-linear saturated screening/optimization trials were planned using the Taguchi-type L9(34) orthogonal array. The resulting multivariate dataset was treated using a combination of the Gibbs sampler and the Pareto frontier method in order to approximate the strength of the studied effects and to find a solution that comprises the minimization of all the tested process/product characteristics. The Pareto frontier optimal solution was (EC, GC, CT, SR) = (4.67 kWh, 20.35 Nm3, 21 s, 5.992 μm) for the synchronous screening/optimization of the four characteristics. The respective factorial settings were optimally adjusted at the four inputs (LP, CS, GP, F) located at (4 kW, 1.9 mm/min, 0.75 bar, +2.25 mm). The linear regression analysis was aided by the Gibbs sampler and promoted the laser power and the cutting speed on energy consumption to be stronger effects. Similarly, a strong effect was identified of the cutting speed and the gas pressure on gas consumption as well as a reciprocal effect of the cutting speed on the cutting time. Further industrial explorations may involve more intricate workpiece geometries, burr formation phenomena, and process economics.