Study on Parametric Optimization of Fused Deposition Modelling (FDM) Process

Rapid prototyping (RP) is a generic term for a number of technologies that enable fabrication of physical objects directly from CAD data sources. In contrast to classical methods of manufacturing such as milling and forging which are based on subtractive and formative principles espectively, these processes are based on additive principle for part fabrication. The biggest advantage of RP processes is that an entire 3-D (three-dimensional) consolidated assembly can be fabricated in a single setup without any tooling or human intervention; further, the part fabrication methodology is independent of the mplexity of the part geometry. Due to several advantages, RP has attracted the considerable attention of manufacturing industries to meet the customer demands for incorporating continuous and rapid changes in manufacturing in shortest possible time and gain edge over competitors. Out of all commercially available RP processes, fused deposition modelling (FDM) uses heated thermoplastic filament which are extruded from the tip of nozzle in a prescribed manner in a temperature controlled environment for building the part through a layer by layer deposition method. Simplicity of operation together with the ability to fabricate parts with locally controlled properties resulted in its wide spread application not only for prototyping but also for making functional parts. However, FDM process has its own demerits related with accuracy, surface finish, strength etc. Hence, it is absolutely necessary to understand the shortcomings of the process and identify the controllable factors for improvement of part quality. In this direction, present study focuses on the improvement of part build methodology by properly controlling the process parameters. The thesis deals with various part quality measures such as improvement in dimensional accuracy, minimization of surface roughness, and improvement in mechanical properties measured in terms of tensile, compressive, flexural, impact strength and sliding wear. The understanding generated in this work not only explain the complex build mechanism but also present in detail the influence of processing parameters such as layer thickness, orientation, raster angle, raster width and air gap on studied responses with the help of statistically validated models, microphotographs and non-traditional optimization methods. For improving dimensional accuracy of the part, Taguchi‟s experimental design is adopted and it is found that measured dimension is oversized along the thickness direction and undersized along the length, width and diameter of the hole. It is observed that different factors and interactions control the part dimensions along different directions. Shrinkage of semi molten material extruding out from deposition nozzle is the major cause of part dimension reduction. The oversized dimension is attributed to uneven layer surfaces generation and slicing constraints. For recommending optimal factor setting for improving overall dimension of the part, grey Taguchi method is used. Prediction models based on artificial neural network and fuzzy inference principle are also proposed and compared with Taguchi predictive model. The model based on fuzzy inference system shows better prediction capability in comparison to artificial neural network model. In order to minimize the surface roughness, a process improvement strategy through effective control of process parameters based on central composite design (CCD) is employed. Empirical models relating response and process parameters are developed. The validity of the models is established using analysis of variance (ANOVA) and residual analysis. Experimental results indicate that process parameters and their interactions are different for minimization of roughness in different surfaces. The surface roughness responses along three surfaces are combined into a single response known as multi-response performance index (MPI) using principal component analysis. Bacterial foraging optimisation algorithm (BFOA), a latest evolutionary approach, has been adopted to find out best process parameter setting which maximizes MPI. Assessment of process parameters on mechanical properties viz. tensile, flexural, impact and compressive strength of part fabricated using FDM technology is done using CCD. The effect of each process parameter on mechanical property is analyzed. The major reason for weak strength is attributed to distortion within or between the layers. In actual practice, the parts are subjected to various types of loadings and it is necessary that the fabricated part must withhold more than one type of loading simultaneously.To address this issue, all the studied strengths are combined into a single response known as composite desirability and then optimum parameter setting which will maximize composite desirability is determined using quantum behaved particle swarm optimization (QPSO). Resistance to wear is an important consideration for enhancing service life of functional parts. Hence, present work also focuses on extensive study to understand the effect of process parameters on the sliding wear of test specimen. The study not only provides insight into complex dependency of wear on process parameters but also develop a statistically validated predictive equation. The equation can be used by the process planner for accurate wear prediction in practice. Finally, comparative evaluation of two swarm based optimization methods such as QPSO and BFOA are also presented. It is shown that BFOA, because of its biologically motivated structure, has better exploration and exploitation ability but require more time for convergence as compared to QPSO. The methodology adopted in this study is quite general and can be used for other related or allied processes, especially in multi input, multi output systems. The proposed study can be used by industries like aerospace, automobile and medical for identifying the process capability and further improvement in FDM process or developing new processes based on similar principle

An investigation on dimensional accuracy of fused deposition modeling (FDM) processed parts using fuzzy logic

on dimensional accuracy of FDM processed ABSP 400 (Acrylonitrile-butadine-styrene) part which can be expressed as change in length, width and thickness. This study presents experimental data and fuzzy decision making logic in integration with the Taguchi method for improving the dimensional accuracy of FDM built parts. It is observed that length and width decreases but thickness shows positive deviation from desired value of the built part. Optimum parameters setting to minimize change in length, width and thickness of standard test specimen have been found out using Taguchi’s parameter design. Experimental results indicate that optimal factor settings for each response are different. Therefore, all the three responses are expressed in a single response index through fuzzy logic approach. The process parameters are optimized with consideration of all the performance characteristics simultaneously. An inference engine is developed to perform the inference operations on the rules for fuzzy logic based on Mamdani method. This study also presents two prediction models- one based on Taguchi approach and the other on ANN approach for assessment of dimensional accuracy of FDM built parts subjected to different operating conditions. The predicted values obtained from Taguchi’s additive model and ANN model are in good agreement with the values from the experimental data with mean absolute percentage error of 3.16 and 0.15 respectively. It was found that ANN model is able to predict overall performance characteristic at all operating condition to a higher degree of accuracy. Finally, experimental results are provided to confirm the effectiveness of the proposed fuzzy approach

Experimental investigation and statistical analysis of additively manufactured onyx-carbon fiber reinforced composites

This is the peer reviewed version of the following article published in final form at https://doi.org/10.1002/app.50338. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.Availability of additive manufacturing (AM) has influenced the scientific community to improve on production and versatility of the components created with several associated technologies. Adding multiple substances through superimposing levels is considered as a part of three-dimensional (3D) printing innovations to produce required products. These technologies are experiencing an increase in development nowadays. It requires frequently adding substance and has capacity to fabricate extremely complex geometrical shapes. However, the fundamental issues with this advancement include alteration of capacity to create special products with usefulness and properties at an economically viable price. In this study, significant procedural parameters: layer designs/ patterns (hexagonal, rectangular and triangular) and infill densities (30, 40 and 50%) were considered to investigate into their effects on mechanical behaviors of fused deposition modeling (FDM) or 3D-printed onyx-carbon fiber reinforced composite specimens, using a high-end 3D printing machine. Mechanical (tensile and impact) properties of the printed specimens were conclusively analyzed. From the results obtained, it was observed that better qualities were achieved with an increased infill density, and rectangular-shaped design exhibited an optimum or maximum tensile strength and energy absorption rate, when compared with other counterparts. The measurable relapse conditions were viably evolved to anticipate the real mechanical qualities with an accuracy of 96.4%. In comparison with other patterns, this was more closely predicted in the rectangular design, using regression models. The modeled linear regression helps to define the association of two dependent variables linked with properties of the dissimilar composite material natures. The models can further predict response of the quantities before and also guide practical applications.Peer reviewedFinal Accepted Versio

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

Additive manufacturing has gained significant popularity from a manufacturing perspective due to its potential for improving production efficiency. However, ensuring consistent product quality within predetermined equipment, cost, and time constraints remains a persistent challenge. Surface roughness, a crucial quality parameter, presents difficulties in meeting the required standards, posing significant challenges in industries such as automotive, aerospace, medical devices, energy, optics, and electronics manufacturing, where surface quality directly impacts performance and functionality. As a result, researchers have given great attention to improving the quality of manufactured parts, particularly by predicting surface roughness using different parameters related to the manufactured parts. Artificial intelligence (AI) is one of the methods used by researchers to predict the surface quality of additively fabricated parts. Numerous research studies have developed models utilizing AI methods, including recent deep learning and machine learning approaches, which are effective in cost reduction and saving time, and are emerging as a promising technique. This paper presents the recent advancements in machine learning and AI deep learning techniques employed by researchers. Additionally, the paper discusses the limitations, challenges, and future directions for applying AI in surface roughness prediction for additively manufactured components. Through this review paper, it becomes evident that integrating AI methodologies holds great potential to improve the productivity and competitiveness of the additive manufacturing process. This integration minimizes the need for re-processing machined components and ensures compliance with technical specifications. By leveraging AI, the industry can enhance efficiency and overcome the challenges associated with achieving consistent product quality in additive manufacturing.publishedVersio

Additive manufactured sandwich composite/ABS parts for unmanned aerial vehicle applications

Fused deposition modelling (FDM) is one of most popular 3D printing techniques of thermoplastic polymers. Nonetheless, the poor mechanical strength of FDM parts restricts the use of this technology in functional parts of many applications such as unmanned aerial vehicles (UAVs) where lightweight, high strength, and stiffness are required. In the present paper, the fabrication process of low-density acrylonitrile butadiene styrenecarbon (ABS) with carbon fibre reinforced polymer (CFRP) sandwich layers for UAV structure is proposed to improve the poor mechanical strength and elastic modulus of printed ABS. The composite sandwich structures retains FDM advantages for rapid making of complex geometries, while only requires simple post-processing steps to improve the mechanical properties. Artificial neural network (ANN) was used to investigate the influence of the core density and number of CFRP layers on the mechanical properties. The results showed an improvement of specific strength and elastic modulus with increasing the number of CFRP. The specific strength of the samples improved from 20 to 145 KN·m/kg while the Young’s modulus increased from 0.63 to 10.1 GPa when laminating the samples with CFRP layers. On the other hand, the core density had no significant effect on both specific strength and elastic modulus. A case study was undertaken by applying the CFRP/ABS/CFRP sandwich structure using the proposed method to manufacture improved dual-tilting clamps of a quadcopter UAV

A Study On Parametric Appraisal of Fused Deposition Modelling (FDM) Process

The manufacturing industries are contemplating to develop new technologies for production of complex end use parts possessing high strength and low product development cycle in order to meet the global competition. Rapid prototyping (RP) is one of the proficient processes having the ability to build complex geometry parts in reasonably less time and material waste. Fused deposition modelling (FDM) is one of the RP processes that can manufacture 3D complex geometry accurately with good mechanical strength and durability. Normally, the FDM process is a parametric dependant process due to its layer-by-layer build mechanism. As FDM build parts are used as end use parts, it is prudent to study the effect of process parameters on the mechanical strength under both static and dynamic loading conditions and wear (sliding) behaviour. In order to investigate the behaviour of build parts in a systematic manner with less number of experimental runs, design of experiment (DOE) approach has been used to save cost and time of experimentation. As the selection of input process parameters influence on build mechanism, the mechanical properties and wear behaviour of FDM build parts change with process parameters. Notably, the raster fill pattern during part building causes FDM build parts to exhibit anisotropic behaviour when subject to loading (static or dynamic). In this research work, an attempt has been made to minimise the anisotropic behaviour through controlling the raster fill pattern during part building by adequate selection of process parameters. Statistical significance of the process parameters is analysed using analysis of variance (ANOVA). Influence of process parameters on performance characteristics like mechanical strength, fatigue life and wear of build part is analysed with the help of surface plots. Internal structure of rasters, failure of rasters, formation of pits and crack are evaluated using scanning electron machine (SEM) micro-graphs. Empirical models have been proposed to relate the performance characteristics with process parameters. Optimal parameter setting has been suggested using a nature inspired metaheuristic firefly algorithm to improve the mechanical strength. Finally, genetic programming (GP) and least square support vector machine (LS-SVM) are adopted to develop predictive models for various performance characteristic

DESIGN STRATEGIES AND ADDITIVE MANUFACTURING OF 3D CUSTOMIZED SCAFFOLDS WITH OPTIMIZED PROPERTIES FOR CRANIOFACIAL TISSUE ENGINEERING

3D customized scaffolds for craniofacial tissue engineering were designed using advanced strategies and technologies. Specifically, reverse engineering, additive manufacturing, material selection, experimental and theoretical analyses were properly integrated. The focus was on: i) design strategies of 3D customized nanocomposite scaffolds for hard tissue regeneration; ii) an engineered design of 3D additive manufactured nanocomposite scaffolds with optimized properties; iii) an approach toward the design of 3D customized scaffolds for large cranial defects

Design and Development of Cellular Structure for Additive Manufacturing

The demand for shorter product development time has resulted in the introduction of a new paradigm called Additive Manufacturing (AM). Due to its significant advantages in terms of cost effective, lesser build time, elimination of expensive tooling, design flexibility AM is finding applications in many diverse fields of the industry today. One of the recent applications of this technology is for fabrication of cellular structures. Cellular structures are designed to have material where it is needed for specific applications. Compared to solid materials, these structures can provide high strength-to-weight ratio, good energy absorption characteristics and good thermal and acoustic insulation properties to aerospace, medical and engineering products. However, due to inclusion of too many design variables, the design process of these structures is a challenge task. Furthermore, polymer additive manufacturing techniques, such as fused deposition modeling (FDM) process which shows the great capability to fabricate these structures, are still facing certain process limitations in terms of support structure requirement for certain category of cellular structures. Therefore, in this research, a computer-aided design (CAD) based method is proposed to design and develop hexagonal honeycomb structure (self-supporting periodic cellular structure) for FDM process. This novel methodology is found to have potential to create honeycomb cellular structures with different volume fractions successfully without any part distortion. Once designing process is complete, mechanical and microstructure properties of these structures are characterized to investigate effect of volume fraction on compressive strength of the part. Volume fraction can be defined as the volume percentage of the solid material inside the cellular structure and it is varied in this thesis by changing the cell size and wall thickness of honeycombs. Compression strength of the honeycomb structure is observed to increase with the increase in the volume fraction and this behavior is compared with an existing Wierzbicki expression, developed for predicting compression properties. Some differences are noticed in between experimentally tested and Wierzbicki model estimated compressive strength. These differences may be attributed to layer by layer deposition strategy and the residual stress inherent to the FDM-manufacturing process. Finally, as a design case study, resin transfer molding (RTM) mold internally filled with honeycomb is designed and tested instead of the regular FDM mold. Results show that our proposed methodology has the ability to generate honeycomb structures efficiently while reducing the expensive build material (Mold) consumption to near about 50%. However, due to complex geometry of the honeycomb pattern the build time increased about 65% compare to solid FDM mould. In this regard, FDM tool-path can be optimized in future, so that overall product cost will be minimized. As per the author’s knowledge, this design methodology will have a greatest contribution towards creating sustainable and green product development. Using this, in future, expensive build material and production time can also be minimized for some hydroforming and injection molding applications

Eklemeli İmalat ile Üretilen PLA Esaslı Malzemenin Çekme Dayanımının Makine Öğrenmesi Algoritmaları Kullanarak Tahmini

Endüstri 4.0'ın önemli bileşenlerinden olan eklemeli imalat ve yapay zekâ tekniklikleri günümüzde birçok alanda sıklıkla kullanılmaktadır. Eklemeli imalat yöntemleri kendi içerisinde yedi sınıfa ayrılmaktadır. Eriyik yığma modelleme eklemeli imalat yönteminin sıklıkla tercih edilen yöntemlerinden birisidir. Eriyik yığma modelleme imalat tablası üzerinde kullanılan filament malzemenin katman katman birleşimi ile parça üretimi gerçekleştirilir. Çalışmada eriyik yığma modelleme yönteminde farklı işleme parametreleri kullanılarak çekme numuneleri üretilmiştir. Çekme numuneleri ASTM standartlarına göre çekme deneyi yapılarak, çekme dayanımına ait değerler ile veri seti oluşturulmuştur. Malzeme üretim sürecinde toplanan sıcaklık, hız, katman özelliklerine dair veri seti kullanılarak üretilen malzemenin çekme dayanımı değerleri üç farklı makine öğrenmesi modeli kullanılarak tahmin edilmiştir. Sonuçlar, makine öğrenmesi algoritmaları kullanılarak çekme dayanımını Kısmi En Küçük Kareler algoritması ile %98,3 doğrulukla tahminlediğini göstermiştir