A Comparative Study on the Structure and Quality of SLM and Cast AISI 316L Samples Subjected to WEDM Processing.

Additive manufacturing technologies are increasingly used in the production of semi-finished workpieces intended for further processing. This entails the need to investigate the machinability and final properties of such products. Comparative research on wire electrical discharge machining (WEDM) processes performed with two kinds of AISI 316L stainless steel workpieces is presented in this paper. The first workpiece was made by selective laser melting (SLM), while the second one was casting. Both working materials were cut with current values ranging from 8 to 72 amps. A comparison of roughness, structure and chemical composition of machined surfaces was performed between the two kinds of specimens. For the SLM sample, parameters of the cutting process that provide relatively low surface roughness (Ra ≤ 10 µm) with the simultaneous maximization of the process efficiency were determined. It was found that in the case of applying high current values (72 amp.), more favorable properties of the treated surface were obtained for the SLM sample than for the cast one.

Modelling and Analysis of the Effect of EDM-Drilling Parameters on the Machining Performance of Inconel 718 Using the RSM and ANNs Methods.

Electrical Discharge Machining (EDM) is one of the most efficient processes to produce high-ratio micro holes in difficult-to-cut materials in the Inconel 718 superalloy. It is important to apply a statistical technique that guarantees a high fit between the predicted values and those measured during analysis of test results. It was especially important to check which method gives a better fit of the calculated result values in case they were relatively small and/or close to each other. This study developed models with the use of the response surface methodology (RSM) and artificial neural networks (ANNs). The aim of the study was comparison between two models (RSM and ANNs) and to check which model gives a better data fit for relatively similar values in individual tests. In all cases, the neural network models provided a better value fit. This is due to the fact that neural networks use better fitted functions than in the case of the RSM method using quadratic fitting. This comparison included the aspect ratio hole and the thickness side gap data, the values of which for individual tests were very similar. The paper reports an analysis of the impact of parameter variables on the analyzed factors. Higher values of current amplitude, pulse time length, and rotational speed of the working electrode resulted in higher drilling speed (above 15 µm/s, lower linear tool wear (below 15%), higher aspect ratio hole (above 26), lower hole conicity (below 0.005), and lower side gap thickness at the hole inlet (below 100 µm).

Optimization of Cutting Data and Tool Inclination Angles During Hard Milling with CBN Tools, Based on Force Predictions and Surface Roughness Measurements.

This work deals with technological considerations required to optimize the cutting data and tool path pattern for finishing the milling of free-form surfaces made of steel in a hardened state. In terms of technological considerations, factors such as feed rate, workpiece geometry, tool inclination angles (lead and tilt angles) and surface roughness are taken into account. The proposed method is based on calculations of the cutting force components and surface roughness measurements. A case study presented in the paper is based on the AISI H13 steel, with hardness 50 HRC and milling with a cubic boron nitride (CBN) tool. The results of the research showed that by modifications of the feed value based on the currently machined cross-sectional area, it is possible to control the cutting force components and surface roughness. During the process optimization, the 9% and 15% increase in the machining process efficiency and the required surface roughness were obtained according to the tool inclination angle and feed rate optimization procedure, respectively.

Research and Optimization of Surface Roughness in Milling of SLM Semi-Finished Parts Manufactured by Using the Different Laser Scanning Speed.

The paper studies the potential to improve the surface roughness in parts manufactured in the Selective Laser Melting (SLM) process by using additional milling. The studied process was machining of samples made of the AlSi10Mg alloy powder. The simultaneous impacts of the laser scanning speed of the SLM process and the machining parameters of the milling process (such as the feed rate and milling width) on the surface roughness were analyzed. A mathematical model was created as a basis for optimizing the parameters of the studied processes and for selecting the sets of optimum solutions. As a result of the research, surface with low roughness (Ra = 0.14 µm, Rz = 1.1 µm) was obtained after the face milling. The performed milling allowed to reduce more than 20-fold the roughness of the SLM sample surfaces. The feed rate and the cutting width increase resulted in the surface roughness deterioration. Some milled surfaces were damaged by the chip adjoining to the rake face of the cutting tool back tooth.

Research and Method of Roughness Prediction of a Curvilinear Surface after Titanium Alloy Turning.

This paper deals with the optimization of process parameters (such as cutting speed and feed rate) to minimize surface roughness in the turning of a titanium alloy (Ti-6Al-4V) workpiece with spherical shape. In the first part of the article, based on the results analysis, a mathematical model is developed. It is shown that cutting speed has little effect on the surface roughness. The second part of the paper presents the application of the developed method to optimize cutting data such as feed rate in order to obtain the surface roughness parameters Ra and Rz of the curvilinear surface of the titanium alloy workpiece at acceptable and aligned, values regardless of the surface shape and its tilted angle. A case study verifies the correctness of the proposed method. The machining time was substantially shortened in comparison to the non-optimized cutting process.

Turning Research of Additive Laser Molten Stainless Steel 316L Obtained by 3D Printing.

This paper presents the characteristic of 316L steel turning obtained by 3D printing. The analysis of the influence of turning data on the components of the total cutting force, surface roughness and the maximum temperature values in the cutting zone are presented. The form of chips obtained in the machining process was also analyzed. Statistical analysis of the test results was developed using the Taguchi method.