2,326 research outputs found

    Experimental investigation of effect of tool path strategies and cutting parameters using acoustic signal in complex surface machining

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    High productive milling of complex sculptured surfaces is extremely important in many different industries. Determination of the appropriate tool path styles and milling parameters is crucial in ensuring precise surface machining, meeting the better surface integrities and lower tool deflection and forces using process monitoring methods. In this study, sound pressure as a monitoring method is presented for analyzing different tool path strategies and cutting parameters to assess their influence on surface errors, tool deflection, cutting forces, sound pressure level and instantaneous material removal rate on rough machining of complex surfaces with ball end mill. Design and analysis of experiments are performed using factorial design technique and variance analysis. Additionally, the significant parameters affecting the experimental results are introduced. B-rep based method with integrated CAM software is developed to calculate the cutter/workpiece engagement, effective cutting diameter and instantaneous material removal rate. Milling strategies employed include contour parallel, zigzag with two cut angle, and spiral. The milling conditions were feed rate and radial depth of cut. The conclusion is that 0° zigzag strategy provokes the lowest cutting forces, tool deflection, surface errors and sound pressure and spiral strategy signifies the worst surface errors and the highest cutting forces. With the increase of feed rate, instantaneous material removal rate increases parallel to rising of machining sound signal, milling forces, tool deflection and machining errors. It is observed that the step over value has less influence on the results. The sound pressure level which has a drastic reference to the material removal rate and removed volume values are detected and experimental results could be figured out with sound pressure

    Use of Energy Consumption during Milling to Fill a Measurement Gap in Hybrid Additive Manufacturing

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    Coupling additive manufacturing (AM) with interlayer peening introduces bulk anisotropic properties within a build across several centimeters. Current methods to map high resolution anisotropy and heterogeneity are either destructive or have a limited penetration depth using a nondestructive method. An alternative pseudo-nondestructive method to map high-resolution anisotropy and heterogeneity is through energy consumption during milling. Previous research has shown energy consumption during milling correlates with surface integrity. Since surface milling of additively manufactured parts is often required for post-processing to improve dimensional accuracy, an opportunity is available to use surface milling as an alternative method to measure mechanical properties and build quality. The variation of energy consumption during the machining of additive parts, as well as hybrid AM parts, is poorly understood. In this study, the use of net cutting specific energy was proposed as a suitable metric for measuring mechanical properties after interlayer ultrasonic peening of 316 stainless steel. Energy consumption was mapped throughout half of a cuboidal build volume. Results indicated the variation of net cutting specific energy increased farther away from the surface and was higher for hybrid AM compared to as-printed and wrought. The average lateral and layer variation of the net cutting specific energy for printed samples was 81% higher than the control, which indicated a significantly higher degree of heterogeneity. Further, it was found that energy consumption was an effective process signature exhibiting strong correlations with microhardness. Anisotropy based on residual strains were measured using net cutting specific energy and validated by hole drilling. The proposed technique contributes to filling part of the measure gap in hybrid additive manufacturing and capitalizes on the preexisting need for machining of AM parts to achieve both goals of surface finish and quality assessment in one milling operation

    A model-based sustainable productivity concept for the best decision-making in rough milling operations

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    [EN]There is a need in manufacturing as in machining of being more productive. However, at the same time, workshops are also urged for lesser energy waste in cutting operations. Specially, rough milling of impellers and bladed integrated disks of aircraft engines need an efficient use of energy due to the long cycle times. Indeed, to avoid dramatic tool failures and idle times, cutting conditions and operations tend to be very conservative. This is a multivariable problem, where process engineers need to handle several aspects such as milling operation type, toolpath strategies, cutting conditions, or clamping systems. There is no criterion embracing productivity and power consumption. In this sense, this work proposes a methodology that meets productivity and sustainability by using a specific cutting energy or sustainable productivity gain (SPG) factor. Three rough milling operations-slot, plunge nad trochoidal milling-were modelled and verified. A bottom-up approach based on data from developed mechanistic force models evaluated and compared different alternatives for making a slot, which is a common operation in that king of workpieces. Experimental data confirmed that serrated end milling with the highest SPG value of 1 is the best milling operation in terms of power consumption and mass removal rate (MRR). In the case of plunge milling technique achieve an SPG < 0.51 while trochoidal milling produces a very low SPG value.The authors acknowledge the support from the Spanish Government (JANO, CIEN Project, 2019.0760) and Basque Government (ELKARTEK19/46, KK-2019/00004). This research was funded by Tecnologico de Monterrey through the Research Group of Nanotechnology for Devices Design, and by the Consejo Nacional de Ciencia y Tecnologia de Mexico (Conacyt), Project Number 296176, and National Lab in Additive Manufacturing, 3D Digitizing and Computed Tomography (MADiT) LN299129. The authors also acknowledge the support from Garikoitz Goikoetxea and fruitful discussions with Mr. Jon Mendez (Guhring (c)) and Endika Monge (Hoffmann Group (c))

    IN-SITU CHARACTERIZATION OF SURFACE QUALITY IN Îł-TiAl AEROSPACE ALLOY MACHINING

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    The functional performance of critical aerospace components such as low-pressure turbine blades is highly dependent on both the material property and machining induced surface integrity. Many resources have been invested in developing novel metallic, ceramic, and composite materials, such as gamma-titanium aluminide (Îł-TiAl), capable of improved product and process performance. However, while Îł-TiAl is known for its excellent performance in high-temperature operating environments, it lacks the manufacturing science necessary to process them efficiently under manufacturing-specific thermomechanical regimes. Current finish machining efforts have resulted in poor surface integrity of the machined component with defects such as surface cracks, deformed lamellae, and strain hardening. This study adopted a novel in-situ high-speed characterization testbed to investigate the finish machining of titanium aluminide alloys under a dry cutting condition to address these challenges. The research findings provided insight into material response, good cutting parameter boundaries, process physics, crack initiation, and crack propagation mechanism. The workpiece sub-surface deformations were observed using a high-speed camera and optical microscope setup, providing insights into chip formation and surface morphology. Post-mortem analysis of the surface cracking modes and fracture depths estimation were recorded with the use of an upright microscope and scanning white light interferometry, In addition, a non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN) was developed to achieve a real-time total volume crack monitoring capability. This approach showed good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, thereby establishing the significant potential for real-time quality monitoring in manufacturing processes. The findings from this present study set the tone for creating a digital process twin (DPT) framework capable of obtaining more aggressive yet reliable manufacturing parameters and monitoring techniques for processing turbine alloys and improving industry manufacturing performance and energy efficiency

    Manufacturing of high precision mechanical components

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    The main goal of the thesis is to analyze key aspects of Precision Manufacturing, aiming at optimizing critical manufacturing processes: innovative experimental methodologies and advanced modelling techniques will be applied to cases study of industrial interest which have been successfully optimized

    Tool wear monitoring for milling by tracking cutting force model coefficients

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    This study establishes a way to monitor tool wear in end milling using a tangential force model coefficient method. An experimental investigation of the characteristics of tangential force model coefficients, KTC and KTE, under different cutting conditions is presented. Experimental results indicate that the coefficients are relatively insensitive to the cutting conditions and quite sensitive to tool wear. Several tool wear experiments were performed on AISI 1018 steel. The tool wear was examined using an optical measurement inspection system. The results indicate that KTE increases proportionally with tool flank wear while K TC stays relatively constant until close to the end of tool life. Other possible wear indicators were studied, specifically the radial coefficients (KRC & KRE) and vibration signals. It was found that KRC & KRE are proportional to KTC & KTE, and the vibration signal magnitude is related to flank wear
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