On the surface quality of additive manufactured parts

Barari, Ahmad; Kishawy, Hossam A.; Kaji, Farzaneh; Elbestawi, M.A.

doi:10.1007/s00170-016-9215-y

Cited by 70 publications

(24 citation statements)

References 15 publications

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“…Thus, microstructure [6,[18][19][20], fracture behavior [19,20], mechanical properties [18] fatigue performance [7,21], and residual stress [20,22], were investigated and reported in the literature. However, surface characteristics or enhancing approaches of surface characteristics have not extensively investigated as yet; although, the functionality of AM parts is highly dependent on the geometric and dimensional accuracy, as well as the surface integrity [23]. As hybrid method includes both SLM and finish machining, and finish machining is being used to improve the surface property, it is inecessary to investigate the effects of the finishing process on the surface and subsurface characteristics of additively manufactured parts.…”

Section: Introductionmentioning

confidence: 99%

Porosity, Surface Quality, Microhardness and Microstructure of Selective Laser Melted 316L Stainless Steel Resulting from Finish Machining

Kaynak

Kıtay

2018

JMMP

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Among additive manufacturing (AM) techniques, Selective Laser Melting (SLM) is widely used to fabricate metal components, including biocompatible bone implants made of 316L stainless steel. However, an issue with the components manufactured using this technique is the surface quality, which is generally beyond the acceptable range. Thus, hybrid manufacturing, including AM and finish machining processes, are being developed and implemented in the industry. Machining processes, particularly finish machining, are needed to improve surface quality of additively manufactured components and performance. This study focuses on the finish machining process of additively manufactured 316L stainless steel parts. Finish machining tests were carried out under dry conditions for various cutting speeds and feed rates. The experimental study reveals that finish machining resulted in up to 88% lower surface roughness of SLMed 316L stainless steel; it also had a substantial effect on microstructure and microhardness of the additively manufactured components by creating smaller grains and strain-hardened layer on the surface and subsurface of the SLMed part. The finish machining process also significantly decreased the density of porosity on the surface and subsurface, compared to an as-built sample. The created strain harden layer with less porosity is expected to increases wear and fatigue resistance of these parts.

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Section: Introductionmentioning

confidence: 99%

Porosity, Surface Quality, Microhardness and Microstructure of Selective Laser Melted 316L Stainless Steel Resulting from Finish Machining

Kaynak

Kıtay

2018

JMMP

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“…The measurements were repeated thrice and the average was considered as final value; afterwards, percentage change was calculated using Eqs. (13,14). Table 5 displays different levels of variable parameters considered for present experimentation.…”

Section: Confirmatory Experimentsmentioning

confidence: 99%

“…Based on polynomial interpolation of the massive data, the predicted and experimental values were found in agreement. The average surface roughness model was developed in terms of layer thickness and orientation and angle [13]. Raju et al [14] implemented a hybrid particle swarm (PSO) and bacterial foraging optimization evolutionary (BFO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Parametric optimization of fused deposition modeling using learning enthusiasm enabled teaching learning based algorithm

2020

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“…A successful design process needs to be conducted by considering all of the manufacturing and inspection requirements [8]. Although additive manufacturing processes are still not able to produce high surface qualities [9], [10], they are highly flexible in producing the complex geometries produced by topology optimization [11], [12]. Also, the advances in coordinate metrology algorithms allow relatively fast inspection of the complex topologies resulting by the topologically optimized designs [13].…”

Section: Implementation Of Overhang Constraintmentioning

confidence: 99%

Constrained Topology Optimization For Additive Manufacturing Of Structural Components In Ansys®

Jankovics¹,

Gohari²,

Barari³

2018

Progress in Canadian Mechanical Engineering

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Topology Optimization is currently the main technique to optimize an objects structural design. This method commonly produces parts that have exceedingly complex geometry. Additive manufacturing (AM) is the main manufacturing process to produce these optimized designs due to the flexibility and speed it offers. However, results of topology optimization without considering manufacturing process limits, even AM ones, may result in designs that are expensive and difficult to build. This paper presents a topology optimization filter that minimizes the effect of overhang structures. These structures are very difficult to manufacture using conventional AM techniques. In order to constrain the gradient compliances with respect to densities and converge the results towards a structure with the least amount of overhang structures, sensitivities are modified using the proposed filter. To implement the proposed filter and the base topology optimization methods ESO and SIMP, ANSYS Parametric Design Language (APDL) is employed within the ANSYS ® Workbench™ environment. The results of a case study using the different topology optimization methods are investigated. Finally, an implementation of the proposed AM filter is used to solve an MBB-beam problem. The result is a structure that needs the least amount of support structure.

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On the surface quality of additive manufactured parts

Cited by 70 publications

References 15 publications

Porosity, Surface Quality, Microhardness and Microstructure of Selective Laser Melted 316L Stainless Steel Resulting from Finish Machining

Porosity, Surface Quality, Microhardness and Microstructure of Selective Laser Melted 316L Stainless Steel Resulting from Finish Machining

Parametric optimization of fused deposition modeling using learning enthusiasm enabled teaching learning based algorithm

Constrained Topology Optimization For Additive Manufacturing Of Structural Components In Ansys®

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