Improving the Surface Quality Through a Laser Scan and Machining Strategy Combining Powder Bed Fusion and Machining Processes

This paper focuses on the unconventional laser powder bed fusion (LPBF) technique in which the LPBF and machining processes were executed alternately to fabricate higher quality parts compared to those obtained using subtractive machining processes. The additional machining process changed the stress distribution inside the built part, resulting in the deformation of the surface morphology in the final part. The phenomenon pertaining to the combined LPBF and machining process based fabrication was investigated, and the influence of the process parameters on the formation of the surplus part and deformation of the machined surface was evaluated. In addition, a laser scan and machining strategy was formulated to improve the surface quality of the built part. The surplus buildup at the edge of the fabricated part occurred owing to the difference in the thermal properties between the solidified part and deposited metal powder. The laser-irradiated position at the first layer buildup and energy density were the principal factors affecting the formation of the surplus part, and the surplus buildup could be reduced using the laser scan strategy, in which the laser-irradiated position was shifted inward. The peripheral face of the built part formed periodical steps, owing to the deformation induced by the change in the thermal distribution inside the built part. These steps could be reduced using the machining strategy combining the rough machining process with a finishing allowance and stepwise finishing process.

Multiple performance characteristics optimization in end milling of thin-walled parts using desirability function

With the development of high-performance CNC machine tools, milling has been established as one of the main means of machining thin-walled parts. Thus, the selection of process parameters for milling operations is an important issue in end milling of thin-walled parts to assure product quality and increase productivity. The current study explores three machining parameters, namely wall thickness, feed, and machining strategies, that influence dimensional and form errors, surface roughness, and machining time milling of 7075-T6 aluminum alloy thin-walled parts. The effects of machining parameters on each of the response variables were analyzed using graphs of the main effects and three-dimensional surface plots. Analysis of the results show that the most influential factor for wall thickness deviation, dimensions deviation, perpendicularity deviation, flatness deviation, surface roughness of inner walls, surface roughness of outer walls, and surface roughness of reference plane was machining strategy, while feed is the most influential parameter affecting the machined time, followed by the machining strategy. The desirability concept has been used for simultaneous optimization in terms of machining parameters of the thin-walled parts machining process. Finally, a confirmation test with the optimal parameter settings was carried out to validate the results.

Improvement of Machining Processes: A Case Study

In material removal processes, determination of optimal machining strategy is a key factor to increase productivity. This situation is gaining more importance when machining components with complex geometry. The current practice in the determination of machining strategy mostly depends on the experience of the machine operator. However, poorly designed machining processes lead to time-consuming and costly solutions. Therefore, the improvement of machining processes plays a vital role in terms of machining costs. In this study, the machining process of a boom-body connector (GGG40) of a backhoe loader was improved. Improvements of toolpaths and cutting conditions of 22 different material removal processes were checked through a CAM software. According to the simulation results, the process plan was rearranged. Besides, some enhancements in casting model were conducted to decrease in the number of machining operations. When compared to current practice, a reduction of 55% in machining time was achieved.

Development of Direction-Parallel Strategy for Shorting A Tool Path in The Triangular Pocket Machining

The machining strategy is one of the parameters which practically influences the time of the different manufacturing geometric forms. The machining time directly relates to the machining efficiency of the tool paths. In area milling machining, there are two main types of tool path strategies: a direction-parallel milling and contour-parallel milling. Then direction-parallel milling is simple compared with a contour-parallel strategy. This paper proposes a new model of the direction-parallel machining strategy for triangular pockets to reduce the tool path length. The authors develop an analytical model by appending additional the tool path segments to the basis tool path for cutting un-machined area or scallops, which remained along the boundary. To validate its results, the researchers have compared them to the existing model found in the literature. For illustrating the computation of this model, the study includes two numerical examples. The results show that the proposed analytic direction-parallel model can reduce the total length of machining. Thus, it can take a shorter time for milling machining.

Optimal Machining Strategy Selection in Ball-End Milling of Hardened Steels for Injection Molds

In the present study, the groups of cutting conditions that minimize surface roughness and its variability are determined, in ball-end milling operations. Design of experiments is used to define experimental tests performed. Semi-cylindrical specimens are employed in order to study surfaces with different slopes. Roughness was measured at different slopes, corresponding to inclination angles of 15°, 45°, 75°, 90°, 105°, 135° and 165° for both climb and conventional milling. By means of regression analysis, second order models are obtained for average roughness Ra and total height of profile Rt for both climb and conventional milling. Considered variables were axial depth of cut ap, radial depth of cut ae, feed per tooth fz, cutting speed vc, and inclination angle Ang. The parameter ae was the most significant parameter for both Ra and Rt in regression models. Artificial neural networks (ANN) are used to obtain models for both Ra and Rt as a function of the same variables. ANN models provided high correlation values. Finally, the optimal machining strategy is selected from the experimental results of both average and standard deviation of roughness. As a general trend, climb milling is recommended in descendant trajectories and conventional milling is recommended in ascendant trajectories. This study will allow the selection of appropriate cutting conditions and machining strategies in the ball-end milling process.