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Processes
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Surface Analysis of Additive Manufacturing Processes
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Surface Analysis of Additive Manufacturing Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2003

Special Issue Editors

Dr. Cayetano Espejo Conesa

E-Mail Website
Guest Editor
School of Mechanical Engineering, University of Leeds, Leeds LS2 9JT, UK
Interests: surfaces; coatings; corrosion; microscopy; spectroscopy; tribology; tribochemistry; additive manufacturing
Dr. Haoqi Zhang

E-Mail Website
Guest Editor
Mechanical Engineering, National University of Singapore, Singapore 117561, Singapore
Interests: additive manufacturing; digital manufacturing, fibre reinforced composites; carbon fibre; finite element modelling

Special Issue Information

Dear Colleagues,

Additive manufacturing (AM) has emerged as a versatile, enabling technology, outperforming tradition manufacturing technologies in numerous applications. This processing technology has been adapted to an increasingly varied range of materials, including metals, thermoplastics, thermosets, ceramics, concrete, composites, etc., to fabricate complex structures with novel geometries. However, the surface quality of AM parts, which is critical to their functional performance, needs to be improved. Surface analysis of AM processes is therefore an important area of research with a focus on understanding the mechanisms that govern the formation of surface features and developing strategies to control and improve them.

This Special Issue, “Surface Analysis of Additive Manufacturing”, aims to compile the latest research on the surface analysis of AM processes, covering a broad range of topics and applications, including but not limiting to:

  • Characterization of surface topography, structures, texture and roughness in AM parts.
  • Surface modification techniques to enhance the properties of AM parts, such as laser polishing, chemical etching, coatings and other forms of surface engineering.
  • Development of in situ monitoring techniques for surface quality during AM processing.
  • Investigation of the impact of process parameters on surface quality, such as laser power, scan speed, material feed, temperature and powder, filament or liquid precursor characteristics.
  • Surface analysis of hybrid AM processes, such as multi-material and multi-process approaches.
  • Development of modeling and simulation approaches to predict and optimize surface quality in AM processes.

Dr. Cayetano Espejo Conesa
Dr. Haoqi Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • additive manufacturing
  • roughness
  • surface analysis
  • surface engineering

Published Papers (2 papers)

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Research

18 pages, 6523 KiB  
Article
Effect of Choice of Drilling Kinematic System on Cylindricity Deviation, Roundness Deviation, Diameter Error and Surface Roughness of Holes in Brass Alloy
by Mateusz Bronis, Bartlomiej Krawczyk and Stanislaw Legutko
Processes 2024, 12(1), 220; https://doi.org/10.3390/pr12010220 - 19 Jan 2024
Viewed by 627
Abstract
This article presents the results of an experimental study on the effect of the selection of kinematic system for the drilling process on the cylindricity deviation, roundness deviation, diameter error and surface roughness of holes in brass alloy. Three different kinematic systems based [...] Read more.
This article presents the results of an experimental study on the effect of the selection of kinematic system for the drilling process on the cylindricity deviation, roundness deviation, diameter error and surface roughness of holes in brass alloy. Three different kinematic systems based on the dependence of the direction of rotation of the workpiece and the drill bit were used. The drill bit was mounted in an axially driven holder that allowed it to be put into motion. Cutting tests were conducted at three different spindle speeds and three different feed rates per revolution (27 tests in total). A static ANOVA analysis was used to evaluate the effect of each input parameter on each output parameter. The results of this work have practical applications in machining. The following input parameters of the drilling process should be used to obtain the smallest values of each output parameter: for CYL, n = 4775 rpm, fn = 0.14 mm/rev and KIN III; for RON, n = 4775 rpm, fn = 0.1 or 0.12 mm/rev and KIN II; for DE, n = 3979 rpm, fn = 0.1 mm/rev and KIN I; and for Rz, n = 4775 rpm, fn = 0.1 mm/rev and KIN II. This research work also used Grey Relational Analysis with which input parameter optimization was derived. The optimal drilling parameters are spindle speeds of 4775 rpm, a feed per revolution of 0.1 mm/rev and the use of the first kinematic system. This paper also includes equations for predicting each parameter that describes the dimensional and shape accuracy and roughness of the hole surface. Using the first kinematic system reduced the roughness of the hole surface by as much as 58%. The correct selection of kinematic system improved its dimensional accuracy by 15%. On the other hand, the roundness deviation of the hole improved by 33% and the cylindricity deviation of the hole by 6%. Full article
(This article belongs to the Special Issue Surface Analysis of Additive Manufacturing Processes)
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24 pages, 11172 KiB  
Article
Optimization of the Effect of Laser Power Bed Fusion 3D Printing during the Milling Process Using Hybrid Artificial Neural Networks with Particle Swarm Optimization and Genetic Algorithms
by Husam Kaid, Abdulmajeed Dabwan, Khaled N. Alqahtani, Emad Hashiem Abualsauod, Saqib Anwar, Ali M. Al-Samhan and Abdullah Yahia AlFaify
Processes 2023, 11(10), 2892; https://doi.org/10.3390/pr11102892 - 30 Sep 2023
Cited by 1 | Viewed by 1045
Abstract
Additive manufacturing (AM) is gaining popularity as it can produce near-net geometries and work with difficult-to-manufacture materials, such as stainless steel 316L. However, due to the low surface quality of AM parts, machining and other finishing methods are required. Laser powder bed fusion [...] Read more.
Additive manufacturing (AM) is gaining popularity as it can produce near-net geometries and work with difficult-to-manufacture materials, such as stainless steel 316L. However, due to the low surface quality of AM parts, machining and other finishing methods are required. Laser powder bed fusion (LPBF) components can be difficult to finish as the surface roughness (Sa) can vary greatly depending on the part’s orientation, even when using the same machining parameters. This paper explored the effects of finishing (milling) SS 316L LPBF components in a variety of part orientations. The effect of layer thickness (LT) variation in LPBF-made components was also studied. LPBF parts of 30, 60, 80, and 100 μm layer thicknesses were created to analyze the effect of the LT on the final milling process. Additionally, the effect of cutting speed during the milling process on the surface roughness of the SS 316L LPBF component was investigated, along with the orientations and layer thicknesses of the LPBF components. The results revealed that the machined surface undergoes significant orientation and layer thickness changes. The investigations employed a factorial design, and analysis of variance (ANOVA) was used to analyze the results. In addition, an artificial neural network (ANN) model was combined with particle swarm optimization (denoted as ANN-PSO) and the genetic algorithm (denoted as ANN-GA) to determine the optimal process conditions for machining an SS 316L LPBF part. When milled along (Direction B) an orientation with a cutting speed of 80 m/min, the LPBF component produced, with a layer thickness of 60 μm, achieves the lowest surface roughness. For instance, the Sa of a milled LPBF part can be as low as 0.133 μm, compared to 7.54 μm for an as-fabricated LPBF part. The optimal surface roughness was 0.155 μm for ANN-GA and 0.137 μm for ANN-PSO, whereas the minimal surface roughness was experimentally determined to be 0.133 μm. Therefore, the surface quality of both hybrid algorithms has improved, making them more efficient. Full article
(This article belongs to the Special Issue Surface Analysis of Additive Manufacturing Processes)
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