article · Materials
Stainless steel 304 possesses favourable physical and mechanical qualities alongside biocompatibility, making it suitable for varied uses. Wire electrical discharge machining can process this alloy in a controllable manner. An experimental study evaluated how five machining parameters affect surface roughness and material removal rates during slotting operations. The investigated variables comprised applied voltage, traverse feed, pulse-on time, pulse-off time, and current intensity. Slot geometries were analysed using edge-detection image processing to determine removal rates, and side-wall roughness was measured across the trial conditions. Findings indicate that traverse feed, current tension, and voltage predominantly govern material removal rates, whereas current tension, pulse-on time, and pulse-off time most heavily influence surface roughness. The resulting regression models capture the operational trade-offs, showing that multi-objective optimisation is necessary to achieve high removal rates alongside superior surface quality.
Stainless steel 304 is widely used in technical and biomedical components due to its strength and biocompatibility. Precision machining techniques like wire electrical discharge machining often force a compromise between production speed and surface quality. Identifying the exact influence of electrical and feed settings allows manufacturers to predict cut quality accurately and avoid costly trial-and-error adjustments during fabrication.
This research is at an applied and tested stage, producing predictive regression tools relevant to precision manufacturers and machine shops working with stainless steel 304. The models could be adopted by process engineers seeking to select operating parameters for wire electrical discharge machining. Practical industrial deployment would likely require incorporating these predictive equations into multi-objective optimisation software or computerised machine controllers to balance cutting speed against surface quality.
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Its unexcelled mechanical and physical properties, in addition to its biocompatibility, have made stainless steel 304 a prime candidate for a wide range of applications. Among different manufacturing techniques, electrical discharge machining (EDM) has shown high potential in processing stainless steel 304 in a controllable manner. This paper reports the results of an experimental investigation into the effect of the process parameters on the obtainable surface roughness and material removal rate of stainless steel 304, when slotted using wire EDM. A full factorial design of the experiment was followed when conducting experimental trials in which the effects of the different levels of the five process parameters; applied voltage, traverse feed, pulse-on time, pulse-off time, and current intensity were investigated. The geometry of the cut slots was characterized using the MATLAB image processing toolbox to detect the edge and precise width of the cut slot along its entire length to determine the material removal rate. In addition, the surface roughness of the side walls of the slots were characterized, and the roughness average was evaluated for the range of the process parameters being examined. The effect of the five process parameters on both responses were studied, and the results revealed that the material removal rate is significantly influenced by feed (p-value = 9.72 × 10−29), followed by current tension (p-value = 6.02 × 10−7), and voltage (p-value = 3.77 × 10−5), while the most significant parameters affecting the surface roughness are current tension (p-value = 1.89 × 10−7), followed by pulse-on time (1.602 × 10−5), and pulse-off time (0.0204). The developed regression models and associated prediction plots offer a reliable tool to predict the effect of the process parameters, and thus enable the optimizing of their effects on both responses; surface roughness and material removal rate. The results also reveal the trade-off between the effect of significant process parameters on the material removal rate and surface roughness. This points out the need for a robust multi-objective optimization technique to identify the process window for obtaining high quality surfaces while keeping the material removal rate as high as possible.
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DOI: 10.3390/ma16031022
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