Abstract:The hybrid process integrates two or more different processes, such as additive and subtractive manufacturing, which have gained appreciable consideration in recent years. The deformation of hybrid manufacturing is an essential factor affecting machining quality. The purpose of this paper is to study the effect of milling on stress release and surface deformation of additive manufacturing (AM) specimens in the process of additive and subtractive hybrid manufacturing (ASHM) of 316L stainless steel thin-walled p… Show more
“…Note, here, that the estimated values of the thermal expansion coefficient are close to 17.62 C, the value that was previously reported in [ 30 ].…”
Section: Stochastic Modeling and Optimizationsupporting
confidence: 87%
“…The material used for this study is AISI 316L stainless steel with Young’s modulus 138 GPa and Poisson’s ratio 0.29. The thermal expansion coefficient, being a stochastic variable, is chosen from a uniform distribution with lower limit equal to the one of conventional steel (10.5 C) [ 29 ] and upper limit the one corresponding to L-DED-processed AISI 316L stainless steel (17.62 C) [ 30 ]. The bottom layer (green) is constructed using the same elastic properties but very low thermal expansion coefficient, resulting in a ‘stiff’ (with respect to temperature changes) material.…”
Section: Stochastic Modeling and Optimizationmentioning
This article presents a novel approach for assessing the effects of residual stresses in laser-directed energy deposition (L-DED). The approach focuses on exploiting the potential of rapidly growing tools such as machine learning and polynomial chaos expansion for handling full-field data for measurements and predictions. In particular, the thermal expansion coefficient of thin-wall L-DED steel specimens is measured and then used to predict the displacement fields around the drilling hole in incremental hole-drilling tests. The incremental hole-drilling test is performed on cubic L-DED steel specimens and the displacement fields are visualized using a 3D micro-digital image correlation setup. A good agreement is achieved between predictions and experimental measurements.
“…Note, here, that the estimated values of the thermal expansion coefficient are close to 17.62 C, the value that was previously reported in [ 30 ].…”
Section: Stochastic Modeling and Optimizationsupporting
confidence: 87%
“…The material used for this study is AISI 316L stainless steel with Young’s modulus 138 GPa and Poisson’s ratio 0.29. The thermal expansion coefficient, being a stochastic variable, is chosen from a uniform distribution with lower limit equal to the one of conventional steel (10.5 C) [ 29 ] and upper limit the one corresponding to L-DED-processed AISI 316L stainless steel (17.62 C) [ 30 ]. The bottom layer (green) is constructed using the same elastic properties but very low thermal expansion coefficient, resulting in a ‘stiff’ (with respect to temperature changes) material.…”
Section: Stochastic Modeling and Optimizationmentioning
This article presents a novel approach for assessing the effects of residual stresses in laser-directed energy deposition (L-DED). The approach focuses on exploiting the potential of rapidly growing tools such as machine learning and polynomial chaos expansion for handling full-field data for measurements and predictions. In particular, the thermal expansion coefficient of thin-wall L-DED steel specimens is measured and then used to predict the displacement fields around the drilling hole in incremental hole-drilling tests. The incremental hole-drilling test is performed on cubic L-DED steel specimens and the displacement fields are visualized using a 3D micro-digital image correlation setup. A good agreement is achieved between predictions and experimental measurements.
“…The particle size ranges from 75 to 150 µm, and the powder has good flowability and thermal processing properties. The chemical composition of the powder is shown in Table 1 [16]. The substrate material used in the melt deposition forming experiment is a 316L stainless steel rolled steel plate with dimensions of 250 × 150 × 15 mm.…”
Section: Experimental Study On Additive and Subtractive Hybrid Manufa...mentioning
confidence: 99%
“…Additive/Subtractive Hybrid Manufacturing (ASHM) is an emerging manufacturing technology that combines the advantages of Additive Manufacturing (AM) and traditional Subtractive Manufacturing (SM) [14,15]. This technology relies on Laser Metal Deposition (LMD), which combines AM and SM and can produce parts with complex geometries, as well as good surface finish and dimensional accuracy [16]. In ASHM-based Selective Laser Melting (SLM), after the scanning laser beam forms multiple powder layers, the milling cutter machines the finished parts, and the next SLM process begins.…”
Additive manufacturing technology overcomes the limitations imposed by traditional manufacturing techniques, such as fixtures, tools, and molds, thereby enabling a high degree of design freedom for parts and attracting significant attention. Combined with subtractive manufacturing technology, additive and subtractive hybrid manufacturing (ASHM) has the potential to enhance surface quality and machining accuracy. This paper proposes a method for simulating the additive and subtractive manufacturing process, enabling accurate deformation prediction during processing. The relationship between stress distribution and thermal stress deformation of thin-walled 316L stainless steel parts prepared by Laser Metal Deposition (LMD) was investigated using linear scanning with a laser displacement sensor and finite element simulation. The changes in stress and deformation of these thin-walled parts after milling were also examined. Firstly, 316L stainless steel box-shaped thin-walled parts were fabricated using additive manufacturing, and the profile information was measured using a Micro Laser Displacement Sensor. Then, finite element software was employed to simulate the stress and deformation of the box-shaped thin-walled part during the additive manufacturing process. The experiments mentioned were conducted to validate the finite element model. Finally, based on the simulation of the box-shaped part, a simulation prediction was made for the box-shaped thin-walled parts produced by two-stage additive and subtractive manufacturing. The results show that the deformation tendency of outward twisting and expanding occurs in the additive process to the box-shaped thin-walled part, and the deformation increases gradually with the increase of the height. Meanwhile, the milling process is significant for improving the surface quality and dimensional accuracy of the additive parts. The research process and results of the thesis have laid the foundation for further research on the influence of subtractive process parameters on the surface quality of 316L stainless steel additive parts and subsequent additive and subtractive hybrid manufacturing of complex parts.
“…Thin walls and gaps are highly affected by thermal deformations due to a fixed size nozzle. 1,29 The definition of two threshold values G min and W min is recommended to ensure the machining of all gaps and walls in each layer. Commonly, G min and W min must be higher than four times the layer thickness in FDM.…”
Smart manufacturing involves the use of emergent technologies and requires dynamic feedback of customer’s demands. These concerns need a rapid Decision Support System (DSS) considering emergent manufacturing processes such as Additive (AM) and Hybrid (HM) Manufacturing and tracking the product changes. This paper proposes a DSS for process selection based on manufacturing complexity and cost. The complexity parameters, deduced from design for manufacturing (DFM), design for additive manufacturing (DFAM) and design for hybrid manufacturing (DFHM) rules, are automatically extracted from computer aided design (CAD) model to follow the product changes. Cost models are defined for each manufacturing process type. In design phase, the manufacturing cost estimation allows considering the cost as a selection factor. The combined complexity based on manufacturing difficulty and cost represents a new paradigm for process selection. The case studies show the reliability of the proposed DSS and its ability to respect the company resources and strategy.
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