The combination of Atomic Diffusion Additive Manufacturing (ADAM) and traditional CNC machining allows manufacturers to leverage the advantages of both technologies in the production of functional metal parts. This study presents the methodological development of hybrid manufacturing for solid copper parts, initially produced using ADAM technology and subsequently machined using a 5-axis CNC system. The ADAM technology was dimensionally characterized by adapting and manufacturing the seven types of test artifacts standardized by ISO/ASTM 52902:2019. The results showed that slender geometries suffered warpage and detachment during sintering despite complying with the design guidelines. ADAM technology undersizes cylinders and oversizes circular holes and linear lengths. In terms of roughness, the lowest results were obtained for horizontal flat surfaces, while 15° inclined surfaces exhibited the highest roughness due to the stair-stepping effect. The dimensional deviation results for each type of geometry were used to determine the specific and global oversize factors necessary to compensate for major dimensional defects. This also involved generating appropriate over-thicknesses for subsequent CNC machining. The experimental validation of this process, conducted on a validation part, demonstrated final deviations lower than 0.5% with respect to the desired final part, affirming the feasibility of achieving copper parts with a high degree of dimensional accuracy through the hybridization of ADAM and CNC machining technologies.
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http://dx.doi.org/10.3390/ma17061437 | DOI Listing |
Heliyon
January 2025
Institute of Energy Engineering, Dhaka University of Engineering & Technology, Gazipur, Bangladesh.
This study investigates the optimization of cutting conditions for machining titanium alloy (Ti-6Al-4V) using Response Surface Methodology (RSM), with the goal of minimizing tool-chip interface temperature and surface roughness. The research focuses on key cutting parameters to investigate the most effective combinations for enhancing surface finish and reducing thermal impact during machining. The present study deals with the dry turning of Ti-6Al-4V alloy with carbide alloy inserts in a way to utilize the Analysis of Variance (ANOVA) to develop predictive models for minimum surface roughness and optimum temperature.
View Article and Find Full Text PDFACS Appl Mater Interfaces
January 2025
Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey.
Cold isostatic pressing, gel casting, and protein coagulation are the most common techniques to produce green bodies prior to computer numerical control (CNC)-based machining for the near-net-scale shaping of ceramics. These methods typically involve various additives and entail several steps to create a green body that is capable of withstanding machining forces. Here, utilizing a single additive, we first introduced a facile benchtop method to generate self-standing, malleable doughs of alumina in under 2 min.
View Article and Find Full Text PDFSci Rep
January 2025
College of Intelligent systems Science and Engineering, Harbin Engineering University, Harbin, 150006, China.
Most of toolpaths for machining is composed of series of short linear segments (G01 command), which limits the feedrate and machining quality. To generate a smooth machining path, a new optimization strategy is proposed to optimize the toolpath at the curvature level. First, the three essential components of optimization are introduced, and the local corner smoothness is converted into an optimization problem.
View Article and Find Full Text PDFSci Rep
December 2024
School of Mechanical Engineering, Liaoning Engineering Vocational College, Tieling, 112008, Liaoning, People's Republic of China.
The paper proposes a multi-rigid-body system state identification method based on self-healing model in order to improve the accuracy and reliability of CNC machine tools. Firstly, considering the influence of the joint surface, the Lagrange method is used to establish the mechanical model of the multi-rigid-body system. We input acceleration information and use the second-order modulation function to complete the online real-time identification of the joint surface parameters, thereby establishing the self-healing mechanical model of the multi-rigid-body system.
View Article and Find Full Text PDFSensors (Basel)
November 2024
McMaster Manufacturing Research Institute (MMRI), Department of Mechanical Engineering, McMaster University, 230 Longwood Rd S, Hamilton, ON L8P0A6, Canada.
The implementation of Machine Vision (MV) systems for Tool Condition Monitoring (TCM) plays a critical role in reducing the total cost of operation in manufacturing while expediting tool wear testing in research settings. However, conventional MV-TCM edge detection strategies process each image independently to infer edge positions, rendering them susceptible to inaccuracies when tool edges are compromised by material adhesion or chipping, resulting in imprecise wear measurements. In this study, an MV system is developed alongside an automated, feature-based image registration strategy to spatially align tool wear images, enabling a more consistent and accurate detection of tool edge position.
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