Artificial neural network-based prediction assessment of wire electric discharge machining parameters for smart manufacturing

Artificial intelligence (AI), robotics, cybersecurity, the Industrial Internet of Things, and blockchain are some of the technologies and solutions that are combined to produce “smart manufacturing,” which is used to optimize manufacturing processes by creating and/or accepting data. In manufacturing, spark erosion technique such as wire electric discharge machining (WEDM) is a process that machines different hard-to-cut alloys. It is regarded as the solution for cutting intricate parts and materials that are resistant to conventional machining techniques or are required by design. In the present study, holes of different radii, i.e. 1, 3, and 5mm, have been cut on Nickelvac-HX. Tapering in WEDM is a delicate process to avoid disadvantages such as wire break, wire bend, wire friction, guide wear, and insufficient flushing. Taper angles viz. 0°, 15°, and 30° were obtained from a unique fixture to get holes at different angles. The study also shows the influence of taper angles on the part geometry and area of the holes. Next, the artificial neural network (ANN) technique is implemented for the parametric result prediction. The findings were in good agreement with the experimental data, supporting the viability of the ANN approach for the evaluation of the manufacturing process. The findings in this research provide as a reference to the potential of AI-based assessment in smart manufacturing processes and as a design tool in many manufacturingrelated fields

Effects on microstructure and material properties for TiNiCo shape memory alloy

Abstract: Wire spark discharge machining of TiNiCo alloys for bone staple applications is presented. Vacuum arc melting furnace was used to developed TiNiCo alloys with varied cobalt content (1, 5 and 10 at. %). Investigation has been performed to study the material removal rate (MRR) and surface quality (Ra) as per the experimental design planned for L33 array. Input process parameters are Pulse on/off duration servo voltage, wire speed and servo feed. From the responses on MRR and Ra optimal process condition were identified using multi objective grey analysis. It is understood that the results obtained for different combination of materials are insignificant due to the property of element weight percentage. Surface of bone staple (Non-implanted) is characterized with respect to surface morphology, surface topography, recast layer thickness, micro hardness and residual stresses. The smoother machined surface and compressive residual stresses have been noticed which are favourable in improving the fatigue life of machined bone staples. Keywords: Shape memory alloys, Wire Spark Discharge Machining

Investigation on dry machining of stainless steel 316 using textured tungsten carbide tools

Abstract: In this research, austenitic stainless steel SS316 material has been machined using textured carbide cutting tools under dry conditions. Micro-textures were made on tool rake face using wire spark erosion machining technology. Effects of three important machining process parameters i.e. cutting speed, depth of cut and feed rate on machinability (MRR, average roughness, and tool wear) of SS316 have been investigated. Taguchi L27 orthogonal array based twenty seven experiments have been carried out by varying machining parameters at three levels. Feed rate has been identified as the most important parameter. Machining parameters have been optimized by grey entropy method to enhance the machinability. Optimal combination of machining parameters i.e. 170 m min−1 cutting speed, 0.5 mm/rev feed rate and 1.5 mm depth of cut produced the best machinability with 3.436 μm average roughness, 105187 mm3 min−1 . MRR, and tool wear 234.63 μm. Lastly, a tool wear and chip morphology study have been done where textured tools have been found outperformed plain (Non-textured) tools

Enhanced process parameters using TOPSIS method during Wire electro discharge machining of TiNiCo shape memory alloy

Abstract: Shape memory alloys (SMAs) are most usable material in the field of biomedical, aerospace and robotics applications due to their unique class of properties. Super elasticity, shape memory effect and two phase transformation behaviour of these alloys make unique material. Machining of such kind of material is quite difficult through conventional machining process hence non-conventional machining of these materials is suitable. Ti50Ni40Co10 shape memory alloy was developed through Vacuum arc melting furnace for the present study. Wire electro discharge machining (A Non-conventional Machining) was used for machining of Ti50Ni40Co10 shape memory alloy. Moreover Topsis method implemented for the parametric optimization of wire electro discharge machining (WEDM). Pulse on time, Pulse off time, Servo voltage, wire speed and servo feed were considered as process parameters and productivity rate and surface quality (average roughness) were consider as output process parameters of WEDM. To see the effect of machining on the machined component EDX analysis has been carried out of optimal process parameters setting. 4.72 mm3 /min productivity rate and 1.26 µm average roughness was found at optimal combination of process parameters of WEDM. Moreover, during the EDX analysis Cu, Zn, O and C were noticed on the machined surface including the parent material

Study on temperature and hardness behaviors of Al-6060 alloy during magnetic abrasive finishing process using artificial neural networks

Magnetic Abrasive Finishing (MAF) revolutionizes surface finishing by utilizing magnetic fields to apply pressure, ensuring exceptionally high-quality surfaces. Its potential for ultra-precise finishing with fine magnetic abrasives is particularly noteworthy. This study analyzes subsurface temperature dynamics during the MAF process on Al-6060, recognizing temperature's pivotal role in shaping surface texture and mechanical-chemical properties. Unbonded magnetic abrasives containing SiC were used without lubrication or coolants in experimentation. Experimental parameters, including machining gap, abrasive weight, voltage, and rotational speed, were systematically varied using a Box-Behnken Design of Experiments. An artificial neural network facilitated comprehensive data modelling, enabling a detailed parametric study. Post-processing surface characterization provided crucial insights into the impact of surface temperature rise on surface finish and hardness. This holistic approach enhances understanding of temperature's influence on MAF efficacy and its outcomes on Al-6060 surfaces. Experimental findings demonstrate MAF's effectiveness in achieving low-temperature finishing of Al-6060, with a maximum surface temperature of 36 °C. ANOVA analysis quantitatively determined that voltage has greatest influence (55.56%), followed by abrasive weight (20.00%) and machining gap (13.89%), with rotational speed having the least impact (2.22%). Qualitatively, the developed ANN model accurately predicted changes in surface roughness (ΔRa), temperature (ΔT), and hardness (ΔH), with maximum errors of 4.107%, 5.588%, and 6.680%, respectively. XRD analysis provided quantitative evidence of SiC diffusion into the surface, resulting in a hardness increase from 2.7 HV to 5.6 HV. Additionally, chemical substrates deposition, such as silicon carbide, kaolinite, iron silicide, and cristobalite, elucidated chemical-mechanical interactions during the process at such low temperatures

Surface characterization of SAE 304 after WED cutting: an experimental investigation and optimization

Stainless steel 304 is an iron-chromium-nickel-based alloy developed for structural applications. This alloy exhibits good mechanical strength along with excellent resistance to various atmospheric conditions. Due to the increasing demand for complex, precise, and high-quality structural components, the wire-electrical-discharge-cutting (WEDC) process is recommended as a powerful technique instead of conventional machine tools. The experimental layout is designed based on L27 Taguchi orthogonal array where pulse-on-time, pulse-off-time, servo voltage, and wire feed are selected as control variables. Post experimentation, roughness profile, topography, morphology, recast surface, and subsurface microhardness of the cut section of SAE 304 alloy were evaluated. Thereafter, the desirability function approach is used to find the optimum cutting conditions. The experimental investigation reveals lower profile roughness, smoother topography, no micro-cracks, least recast layer, and minimum hardness alteration under a trim-cut strategy which is found to be useful for safe, durable, and high–strength complex structural components