Effect of process parameters on parts quality and process efficiency of fused deposition modeling

Highlights

• A new methodology to optimize FDM process efficiency and quality is presented.

• Twenty responses are selected to analyse process efficiency and part quality.

• Design of Experiments (DoE) is used to quantify the effects of process parameters.

• Mathematical models are built and optimized in different scenarios to meet goals.

• Optimal parameters settings are validated in each identified scenario.

Abstract

Fused Deposition Modeling (FDM) is an additive manufacturing technique for fabricating parts directly from computer-aided design data by melting, extruding, and resolidifying a thermoplastic filament. This paper presents a methodology for optimizing both process efficiency, i.e., time and energy consumption, and part quality, i.e., surface roughness and dimensional accuracy, of Polylactic Acid (PLA) components produced by FDM. In this work, a Design of Experiments (DoE) approach is adopted to quantify the effects of deposition parameters on process efficiency and part quality outputs. Specifically, the investigated input parameters are layer height, fill density, extruder temperature, part orientation, number of shells, print speed and retraction speed. The mathematical models relating the significant process parameters to the output responses are developed and the responses are optimized considering different scenarios. An experimental validation is performed to test the adequacy of such optimizations. These experimental results showed that, according to the context, different parameter settings pursue different goals in terms of part quality and process efficiency. The proposed approach may effectively help designers determine process parameters’ settings to optimize both part quality and process efficiency and can be applied to either prototype or part production.

Introduction

Additive Manufacturing (AM) is worldwide recognized as “the process of joining materials to make parts from 3D model data, usually layer upon layer, as opposed to subtractive manufacturing technologies and formative manufacturing methodologies”. Additive technologies have experienced significant growth over the past 30 years in terms of the number of machines sold and parts produced. By researchers and practitioners, AM is considered one of the most dynamic and promising recent industrial innovations (Nyaluke et al., 1995, Wohlers, 2018). AM processes offer a novel approach for prototyping and manufacturing parts compared to traditional casting and metal-cutting processes. These technologies integrate computer-aided design (CAD) for creating a computer model of the final part with its manufacturing by adding layers of materials with dedicated equipment. Accordingly, it is possible to create spatially sophisticated and lightweight lattice components that would be impossible to obtain with traditional manufacturing techniques (Verna, Genta, Galetto, & Franceschini, 2020). The rapid growth and improvements in AM technologies have enabled many industrial sectors to reap the advantages (Chergui et al., 2018, Lan and Ding, 2007). Typically, AM applications are found in the aerospace, energy, automotive, medical and dental, tooling and jewelry industries (Amini and Chang, 2018, Galetto et al., 2020, Gardan, 2016, Majeed et al., 2020, Verna et al., 2019). One of the main solid freeform fabrication (SFF) processes recognized as an AM technology is Fused Deposition Modeling (FDM). This process is one of the most widely used, particularly for non-commercial use, due to its versatility in producing functional parts with complex geometry in reasonable production time (Rayegani & Onwubolu, 2014). In the FDM process, a thermoplastic filament is melted and extruded through a circular nozzle. The molten plastic is deposited onto a print bed through a nozzle movement, controlled through a 3-axis system. Thermoplastics are the most widely used feedstock materials, although different materials, including cement and composites, are also compatible with the FDM process (Abid et al., 2018, Li et al., 2018, Liu et al., 2019, Stoof and Pickering, 2018). Due to the versatility of materials and shapes, FDM's main advantage is to produce polymeric complex-shaped components in one step. The major applications are functional prototypes for commercial and non-commercial use, rapid tooling patterns, and concept models. Despite FDM benefits, many technical challenges continue to hamper widespread adoption and achieve its full potential. One major barrier is the variation in the part quality and mechanical properties, due to inadequate dimensional tolerance, presence of defects, surface roughness, and residual stress, which is not sufficient yet to meet the industrial sectors' stringent requirements (Dong et al., 2018, Perez, 2002, Wu et al., 2018). Achieving high levels of surface roughness and dimensional accuracy of FDM parts is an extremely challenging task due to many factors, such as the high complexity of the underlying physical phenomena and transformations that take place during part production. Nowadays, there is no unique standard method to model and improve the surface quality and dimensional accuracy due to the complex nature of the process and the different properties of the material used. Indeed, several approaches have been adopted in the literature to this purpose, such as Design of Experiments (DoE), simulations and optimizations, involving different process parameters and their interactions. In particular, several attempts have been made to model the surface roughness of FDM parts. Ahn, Kweon, Kwon, Song, and Lee (2009) proposed a theoretical model to express surface roughness distribution according to changes in surface angle by considering the main factors that crucially affect surface quality. In the study of Anitha, Arunachalam, and Radhakrishnan (2001), parameters' influence on prototype quality characteristics using Taguchi technique was assessed. Pandey, Reddy, and Dhande (2003) described a methodology and software implementation that provide the designer with a computer graphics-based visualization of surface roughness. In the study of Durgun and Ertan (2014), the effect of five different raster angles for three orientations was tested on surface roughness, tensile strength, and flexural strength (Durgun & Ertan, 2014). Furthermore, several studies have been published in the literature on modeling the dimensional accuracy of FDM parts. Sood, Ohdar, and Mahapatra (2009) investigated the effect of different process parameters, such as layer height, part orientation, raster angle, air gap and raster width, on the dimensional accuracy of FDM processed ABSP400 (acrylonitrile-butadine-styrene) parts. Also in the paper of Nancharaiah, Raju, and Raju (2010), the effect of layer height, road width, raster angle and air gap on the surface finish and dimensional accuracy was investigated. Sahu, Mahapatra, and Sood (2013) presented experimental data and a fuzzy decision-making logic combined with Taguchi method for improving the dimensional accuracy of FDM processed ABSP 400 parts. Garg, Bhattacharya, and Batish (2016) investigated the effect of part deposition orientation on surface finish and dimensional accuracy of FDM parts. Previous research has also investigated the effect of several process parameters on the FDM process's efficiency in terms of printing time and energy consumption (Frank et al., 2015, Griffiths et al., 2016). It is evident from the literature that several input parameters can be controlled and varied in order to optimize the selected output parameters, involving efficiency and quality of FDM parts (Pandey, Thrimurthulu, & Reddy, 2004). In this work, a methodology for choosing the input parameters is proposed and implemented. When a combination of different input variables and their interactions affect selected responses, the Design of Experiments (DoE) is an effective statistical approach for optimizing the process (Mason et al., 2003, Verna et al., 2020).

This paper aims at investigating FDM processed PLA (Polylactic Acid) parts through statistically designed experiments to determine the significance of the process parameters affecting parts quality (surface roughness and dimensional accuracy) and process efficiency (printing time and energy consumption). To date, few detailed studies have proposed a combined analysis of the quality and efficiency of parts produced by the FDM process. This investigation attempts to provide a reference procedure to determine the combination of process parameters that optimize both part quality and process efficiency, with the minimum number of tests. To this end, a fractional factorial design is performed. The Analysis Of Variance (ANOVA) is used to estimate the statistical significance of parameters' effects on the observed differences in the selected responses. The adequacy of the obtained models is demonstrated by using the coefficients of determination, and the residual plots are analyzed to verify the basic assumptions to perform the ANOVA. The mathematical models relating the significant process parameters to the output responses are derived and optimized, considering different scenarios. Finally, new samples are produced to test the models' adequacy and the optimizations performed in each scenario. The findings should make an important contribution to the field of FDM production by supporting designers in the improvement of process efficiency and parts quality, according to their objectives.

It is worth remarking that, in the scientific literature, it has been shown that some parameters may have a quadratic effect on responses (Lužanin et al., 2014, Sanatgar et al., 2017). To this end, four central points are added to the experiment, which allowed evaluating the model's curvature. However, since the effects of each quadratic term of the model cannot be estimated without performing further tests, i.e., by adding axial points, these models may not be used for forecasting purposes (Myers & Montgomery, 1995). Further tests will aim at estimating the quadratic effects, thus improving the predictions and optimizations obtained in this paper. Despite this limitation, the proposed methodology can represent an effective and efficient approach to support researchers and practitioners.

The remainder of this paper is organized into five sections, including one appendix. Section 2 presents the FDM process and the produced PLA samples. Section 3 contains the description of the design of experiments and the related experimental details. Section 4 contains the performed analysis with the related experimental results and discussion. Section 5 summarizes the results obtained, specifying the practical implications and insights for future research. The Appendix section provides further information to support the proposed analysis.