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Abstract

The 3D printing of thermoplastic polymers (both virgin and reinforced with metal/ceramic particles) has been widely explored in recent past with fused deposition modelling (FDM) process. But hitherto very little has been reported on 3D printing of thermoplastics polymers with reinforcement of thermosetting polymers and ceramic particles. This article is an extension of work reported on thermo-mechanical investigations on waste thermosetting polymer bakelite and ceramic (silicon carbide and aluminium oxide) as reinforcement in recycled acrylonitrile butadiene styrene (ABS) thermoplastic matrix for sustainability. The study reports the experimental investigations on mechanical (tensile), morphological, surface hardness and thermal stability analysis of 3D printed functional prototype as tensile specimen (as per ASTM D 638). In the present case study, it has been ascertained that composition/proportion of thermoplastic matrix has a significant role in controlling the mechanical properties, whereas other input process parameters of FDM are insignificant. The results of the study suggest that thermosetting and ceramic-reinforced ABS thermoplastic-based 3D printed parts have mechanical properties at par with unreinforced ABS.

Keywords

3D printing thermoplastic bakelite fused deposition modelling ceramics reinforcement

Introduction

In modern manufacturing, number of engineering materials (metals, alloys, ceramics, non-metals, thermoplastics, etc.) are being used for 3D printing applications. In the category of thermoplastics, one of the most used materials is acrylonitrile butadiene styrene (ABS).^1
–3 The ABS is having properties of fast solidification, average strength and cheap in price but have inferior mechanical properties.^4

–8 The studies have reported the use of ABS printed parts on fused deposition modelling (FDM) in medical/surgical applications,^9
–11 anatomies models of animals and humans, and so on.^10

–13 As per the technology concern, some important factors responsible for variation of material properties in FDM printed parts are bed temperature, nozzle temperature, degree of crystallinity, molecular structure, neighbouring filament heat, filling speed, printing width and orientation angle in fabrication process.^14

–19 It has been reported that raster angle, addition of additives, layer thickness, complexity in structures, blending of thermoplastics, nozzle conditions, direction of printing, recovery temperature, deformation temperature, shape recovery and shape recovery rate greatly affect the mechanical, morphological, thermal, chemical, tribological and rheological behaviour of FDM printed functional prototypes.^20

–25 The high quality of FDM products in complex shapes can also be obtained by keeping accurate balance of process parameters.^26

–32 Good range of mechanical and wear properties of polymers and biomass alloys increased the area of utilization of FDM parts in electronic, industrial, automobile, architecture engineering, antenna covers, production tooling, electrical housing, aerospace industries, concrete printing, electronic button covers, tooling/fixtures, ecological monitoring vehicles, hydrodynamics and coral reef replica construction.^33,34 In addition, the use of advanced engineering particulates such as graphene, carbon black (CB) and carbon nanotubes provides high thermal and electrical conductivity for prospective use in electronics and electrical engineering fields.^34,35 It has been reported that ABS and CB mixture helps in making concave capacitive sensor with cost-effectiveness.³⁶

The literature review reveals the use of FDM for recycling of thermoplastics. Some of the studies outlined the use of nano- or micro-sized particulate for reduce/reuse/recycle (3R’s concept of waste management) prospective by FDM.^2,37 But hitherto very little has been reported on the recycling of thermosetting waste by using it as filler in thermoplastic matrix along with ceramic particulates. The present study is an extension of work reported by Singh et al.³⁷ in which the study was limited to the preparation of innovative feedstock filaments of thermosetting, thermoplastic and ceramics blends. In this study, 3D printed functional prototypes were subjected to mechanical, thermal, morphological and surface characterization for sustainability.

Materials and methods

In this study, primary recycled thermoplastic ABS with melt flow index value 8.76 g/10 min as per ASTM D 1238 has been selected as matrix material. For reinforcement, recycled bakelite (BAK) 10% (by weight) has been used (for recycling purpose) with different proportions of ceramic particles as reported by Singh et al.³⁷ Table 1 shows the composition/proportions of reinforcement in the ABS matrix.

Table 1.

Selected composition/proportion of reinforcement.³⁷

S. no.	Composition/proportion
1	65%ABS-10%BAK-12.5%Al₂O₃-12.5%SiC
2	70%ABS-10%BAK-10%Al₂O₃-10%SiC
3	75%ABS-10%BAK-7.5%Al₂O₃-7.5%SiC
4	80%ABS-10%BAK-5%Al₂O₃-5%SiC
5	85%ABS-10%BAK-2.5%Al₂O₃-2.5%SiC
6	90%ABS-10%BAK-0%Al₂O₃-0%SiC

ABS: acrylonitrile butadiene styrene; BAK: bakelite; Al₂O₃: aluminium oxide; SiC: silicon carbide.

Figure 1 shows the flow chart for the present experimental study. The recycled ABS and BAK have been procured from local market (Batra Polymers, Ludhiana, Punjab, India) and reinforcement from (aluminium oxide (Al₂O₃) and silicon carbide (SiC)) from Shiva Chemicals, Ludhiana, Punjab, India.

Figure 1.

Flow chart for experimentation.

Experimentation

Since the present work is an extension of the study reported by Singh et al., initially a pilot study was conducted for possible 3D printing of functional prototypes.³⁷ Three compositions/proportions: C1(90%ABS-10%BAK-0%Al₂O₃-0%SiC), C2(80%ABS-10%BAK-5%Al₂O₃-5%SiC) and C3(70%ABS-10%BAK-10%Al₂O₃-10%SiC) were judicially selected to explore the effect of thermosetting polymer (BAK) and ceramic (Al₂O₃/SiC) as reinforcement. It has been reported that infill ratio/percentage and infill speed while printing with FDM affect the mechanical properties of the thermoplastic.^20

–23 So, based upon pilot study and review of the literature,^{20

–29} three levels of composition/proportion (C1, C2 and C3), infill ratio/percentage (60, 80 and 100) and infill speed (50, 60 and 70 mm s⁻¹) have been selected. Table 2 shows input parameters and their levels selected for processing on FDM.

Table 2.

Input parameters and their levels.

Level	Composition	Infill ratio	Infill speed (mm s⁻¹)
1	C1	60	50
2	C2	80	60
3	C3	100	70

Table 3 shows design of experiment (DOE) selected based upon Taguchi L9 orthogonal array. The other input FDM parameters were kept fixed based upon the literature.^6
–8 It should be noted that with proposed composition/proportion no printing difficulties were encountered as proportion of reinforcement was relatively less.

Table 3.

DOE for final experimentation.

Experiment number	Composition	Infill ratio	Infill speed (mm s⁻¹)
1	C1	60	50
2	C1	80	60
3	C1	100	70
4	C2	60	60
5	C2	80	70
6	C2	100	50
7	C3	60	70
8	C3	80	50
9	C3	100	60

DOE: design of experiment.

Further based upon Table 3, 27 (9 × 3 repetitions) tensile samples as per ASTM D 638 type IV were printed on FDM setup (Figure 2).

Figure 2.

3D printed specimens for tensile testing.

The 3D printed specimens were put on tensile testing at universal tensile testing setup at 50 mm s⁻¹ speed. Figure 3 shows FDM printed samples after tensile testing.

Figure 3.

3D printed specimens after testing on UTT.

Now by applying DOE (as per Table 3), Table 4 shows observations for various mechanical/morphological properties. Further, Figures 4 and 5, respectively, show plots for load versus displacement and stress versus strain.

Figure 4.

Load versus displacement curve (as per Table 4).

Figure 5.

Stress versus strain curve (as per Table 4).

Table 4.

Observations for mechanical and morphological properties.^a

Experiment number	Peak load (N)	Strength at peak (MPa)	Strength at break (MPa)	Peak elongation (mm)	Break elongation (mm)	Percentage (%) porosity at cross-section	Shore D hardness
1	178.4 ± 2.1	7.43 ± 0.9	6.69 ± 0.8	3.04 ± 0.02	3.80 ± 0.03	16.13 ± 2.2	58.50 ± 2.0
2	373.6 ± 3.7	15.57 ± 1.1	14.01 ± 1.0	4.18 ± 0.09	4.75 ± 0.08	16.95 ± 2.3	68 ± 1.5
3	290.2 ± 4.1	12.09 ± 2.1	10.88 ± 1.8	2.85 ± 0.1	3.42 ± 0.11	13.49 ± 1.8	65 ± 2.0
4	307.4 ± 3.2	12.81 ± 1.6	11.53 ± 1.4	3.23 ± 0.11	3.61 ± 0.12	13.80 ± 1.6	71.50 ± 1.5
5	273.1 ± 2.8	11.38 ± 1.3	10.24 ± 1.2	2.47 ± 0.10	2.66 ± 0.11	13.54 ± 1.6	66.50 ± 1.5
6	221.1 ± 2.2	9.21 ± 0.9	8.29 ± 0.8	3.23 ± 0.11	3.8 ± 0.10	17.23 ± 2.8	72 ± 2.0
7	361.8 ± 3.1	15.08 ± 1.2	13.57 ± 1.1	4.37 ± 0.13	4.56 ± 0.12	16.8 ± 2.7	66.50 ± 1.5
8	523.2 ± 4.1	21.80 ± 2.4	19.62 ± 2.2	4.75 ± 0.12	5.7 ± 0.11	9.43 ± 1.3	73 ± 1.5
9	418.7 ± 4.3	17.45 ± 2.1	15.7 ± 1.9	3.04 ± 0.11	3.99 ± 0.10	17.69 ± 2.7	71.50 ± 1.5

^aThe percentage porosity has been calculated as per ASTM E 2015-04 (2014) standard using image processing software at ×100 magnification at the cross-section.

Based upon Table 4 and Figure 5, Table 5 shows observations for modulus of toughness. As observed from Tables 4 and 5, samples prepared in experiment number 8 have maximum strength at peak, strength at break, peak load, peak elongation, break elongation, Shore D hardness, modulus of toughness and lowest percentage porosity. Hence from mechanical, surface properties viewpoint (based upon Table 4) and for crash applications (based upon Table 5), sample prepared in experiment number 8 is recommended.

Table 5.

Observations for modulus of toughness.

Experiment number	Modulus of toughness (MPa) (1/2 × strength at break × strain)
1	0.363171
2	0.950679
3	0.531566
4	0.594619
5	0.38912
6	0.450029
7	0.883989
8	1.597629
9	0.8949

Results and discussion

Now based upon Table 4, Table 6 shows signal-to-noise (SN) ratio for maximum the better type case for strength at peak using the following formula

Table 6.

SN ratios for strength at peak.

Experiment number	SN ratio for strength at peak (dB)
1	17.41
2	23.84
3	21.64
4	22.15
5	21.12
6	19.28
7	23.56
8	26.76
9	24.83

SN: signal-to-noise.

η = - 10 log [\frac{1}{n} \sum_{k = 1}^{n} \frac{1}{y^{2}}]

where $η$ is SN ratio, n is the number of experiment and y is the material properties at experiment number k.

Based upon Tables 4 and 6, analysis of variance and ranking of input parameters have been presented in Tables 7 and 8, respectively. Further, Figure 6 shows mean effect plot for SN ratios. As observed from Figure 6, the best settings for input parameters are composition (C3), infill ratio (80%) and infill speed (60 mm s⁻¹).

Figure 6.

Main effect plot for SN ratios for strength at peak.

Table 7.

ANOVA for strength at peak.

Source	Degree of freedom	Sequential sum of squares	Adjusted sum of squares	Adjusted mean of squares	Fisher’s value	Probability	Percentage contribution
Composition	2	34.391	34.391	17.196	3.98	0.201	52.79
Infill ratio	2	12.942	12.942	6.471	1.50	0.400	19.86
Infill speed	2	9.172	9.172	4.586	1.06	0.485	14.08
Residual error	2	8.633	8.633	4.317			13.25
Total	8	65.139

ANOVA: analysis of variance.

Table 8.

Ranking table for input parameters.

Level	Composition	Infill ratio	Infill speed
1	20.97	21.05	21.16
2	20.85	23.91	23.61
3	25.06	21.92	22.11
Delta	4.20	2.87	2.45
Rank	1	2	3

As observed from Table 7, the composition has maximum contribution (52.79%) for strength at peak followed by infill ratio (19.86%) and infill speed (14.08 mm s⁻¹). As observed from Figure 6, with increase in ceramic composition/proportion (5% Al₂O₃/SiC) there was initial dip in peak strength and with further increase up to 10% (Al₂O₃/SiC) a steep rise in peak strength was observed. This may be due to the fact that 5% Al₂O₃/SiC proportion in ABS-BAK matrix have non-uniform dispersion (mainly because of processing conditions) resulting into agglomeration. The dispersion of reinforcements may have become better with higher proportion of Al₂O₃/SiC and processing conditions.

Prediction for peak strength at proposed settings

Following formula has been used for prediction of peak strength at proposed setting:

H_{opt} = X + (X_{A 3} - X) + (X_{B 2} - X) + (X_{C 2} - X)

where H_opt is the SN value at optimum settings, X is the mean of SN values for peak strength at 09 different settings (as per Table 5) = 22.29 dB, X_A3 is the SN ratio of composition at level 3, X_B2 is the SN ratio of infill ratio at level 2 and X_C2 is the SN ratio of infill speed at level 2.

After putting the values of X, X_A3, X_B2 and X_C2 (from Table 8), H_opt = 28.

The optimum value of peak strength can be obtained by using the following formula

{(Y_{opt})}^{2} = {(10)}^{(H_{opt} / 10)}

Putting the value of H_opt in the above equation, (Y_opt))² = (10)^2.8, Y_opt = 25.12 MPa.

The confirmatory experiment was conducted at proposed settings and observed value for peak strength was 23.21 MPa. It should be noted that the peak strength of 3D printed parts are observed at par with the feedstock filament³⁷ and with unreinforced ABS (which was 22.4 MPa). Further as observed from Table 7, the residual error was high (13.25%) and no parameter was significant at 95% confidence level. So to ascertain the interaction among input parameters for observed values of strength at peak (Table 4), historical data approach (under response surface methodology) has been used. It has been observed that two-factor interaction, quadratic or higher degree model showed aliased results and therefore discarded and finally linear model has been recommended (see Table 9). The model Fisher’s value of 6.61 implies the model is significant. Further, probability values less than 0.0500 indicate model terms are significant. In this case, composition is a significant model term. Based upon Table 9, Figure 7(a) to (c) shows the 3D model graphs.

Figure 7.

3D model graph (based upon Table 9) for (a) C1, (b) C2 and (c) C3.

Table 9.

Linear model based upon historical data approach.

Source	Sum of squares	Degree of freedom	Mean square	Fisher’s value	Probability value	Remarks
Model	276.25	4	69.06	6.61	0.0012	Significant
Infill ratio	5.88	1	5.88	0.5631	0.4610
Infill speed	0.0060	1	0.0060	0.0006	0.9810
Composition	270.37	2	135.18	12.94	0.0002
Residual	229.81	22	10.45
Pure error	46.20	18	2.57

As observed from Figure 7, better results for strength at peak are observed for C3, whereas other two parameters, that is, infill ratio and infill speed, are having no effect. Further based upon Table 3, Figure 8 shows photomicrographs of samples at cross-section (at 30×) after tensile testing. It has been observed that sample number 8 with best mechanical properties has relatively less porosity and highest surface hardness (see Table 4). On the other hand, for sample number 1 with poor mechanical properties, the porosity value is relatively high with low value of surface hardness. For better understanding of failure mechanism, the scanning electron microscopy (SEM)-based images were captured at different magnifications (Figures 9 and 10) for sample number 8 (with better mechanical properties) and sample number 1(with poor mechanical properties). In sample 8, it has been observed (through the rendered SEM images and surface roughness (Ra) profile) that more uniform grains with less Ra value are major reason for better mechanical properties in comparison to sample number 1.

Figure 8.

Photomicrographs ((a) to (i)) for 3D printed samples after tensile testing at cross-section (at ×30).

Figure 9.

SEM images for sample number 8: (a) SEM image at ×100, (b) SEM image at ×200, (c) SEM image at ×500, (d) rendered 3D SEM image (×100) and (e) surface roughness profile.

Figure 10.

SEM images for sample number 1: (a) SEM image at ×100, (b) SEM image at ×200, (c) SEM image at ×500, (d) rendered 3D SEM image (×100) and (e) surface roughness profile.

Based upon observations in Figures 9 and 10 to ascertain the composition/proportion of reinforcement in ABS matrix, energy-dispersive X-ray spectroscopy (EDS) was conducted for sample numbers 8 and 1 (see Figures 11 and 12), which clearly indicate high proportion of ceramic reinforcement in sample number 8.

Figure 11.

EDS analysis for sample number 8.

Figure 12.

EDS analysis for sample number 1.

Finally for thermal stability analysis, all three compositions/proportions (C1, C2 and C3) were subjected to differential scanning calorimetry test conducted under nitrogen inert atmosphere with a flow rate of 50 ml min⁻¹, maintaining endothermic thermal reaction at 10°C min⁻¹ and exothermic thermal reaction at −10°C min⁻¹ (Figure 13). The results of the thermal analysis show that rise of ceramic proportion in reinforcement has led to increase in heat capacity in the first cycle. For C1 composition (0% ceramic) the heat capacity was 0.63 J g⁻¹, for composition C2 (10% ceramic) the heat capacity was 0.68 J g⁻¹ and for composition C3 (20% ceramic) the heat capacity was 0.77 J g⁻¹. This means that higher proportion of ceramic resulted in more thermal stability in the first heating/cooling cycle. But in the second cycle for all compositions/proportions, heat capacity has been reduced for all compositions/proportions. Also the tendency for thermal deformation of the printed parts in repeated cycles was getting poorer with more ceramic reinforcements. Hence it is ascertained that in repeated cycles, higher ceramic-reinforced thermoplastics should not be subjected to large temperature variations from thermal stability viewpoint.

Figure 13.

DSC analysis for different compositions/proportions.

Conclusions

Following are the conclusions from this study:

The functional prototypes comprising of ABS-BAK-Al₂O₃-SiC have been successfully 3D printed with improved/at par mechanical properties in comparison to feedstock filament/virgin ABS. However, there was little compromise on thermal stability as after every heating/cooling cycle heat capacity value is reducing marginally.

With regard to FDM input parameters from Taguchi optimization viewpoint, 80% infill ratio and 60 mm s⁻¹ infill speed with composition 70%ABS-10%BAK-10%Al₂O₃-10%SiC are the best settings, whereas from historical data viewpoint, composition is the only significant factor and other input parameters are not significant. So one can select any value from the range available (as per commercial setup) for infill ratio and infill speed. The peak strength of FDM printed parts at proposed setting was observed as 23.21 MPa.

It has been observed that sample number 8 with best mechanical properties has relatively less porosity and highest surface hardness. For sample number 8, rendered SEM images and Ra profile outlined formation of more uniform grains (with less Ra value) may be the major reason for better mechanical properties in comparison to sample number 1.

Footnotes

Acknowledgement

The authors are thankful to Manufacturing Research Lab (Department of Production Engineering), Guru Nanak Dev Engineering College, Ludhiana, for providing research facilities.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Rupinder Singh

References

Kumar

Singh

, et al. Investigations for magnetic properties of PLA-PVC-Fe3O4-wood dust blend for self-assembly applications. J Thermoplast Compos. Epub ahead of print 20 June 2019; 1–23. DOI: 10.1177/0892705719857778.

Bartolomé

Bozzo

Sevilla

, et al. ABS 3D printed solutions for cryogenic applications. Cryogenics 2017; 82: 30–37.

Boparai

Singh

. Thermoplastic composites for fused deposition modeling filament: challenges and applications. In: Reference Module in Materials Science and Materials Engineering. Amsterdam: Elsevier, 2018, pp. 1–12. DOI: 10.1016/B978-0-12-803581-8.11409-2.

Sachs

Cima

Williams

, et al. Three dimensional printing: rapid tooling and prototypes directly from a CAD model. J Eng Ind 1992; 114(4): 481–488.

Hull

. Apparatus for production of three-dimensional objects by stereolithography. Patent 4,575,330, USA, 1986.

Kumar

Singh

, et al. On investigation of rheological, mechanical and morphological characteristics of waste polymer-based feedstock filament for 3D printing applications. J Thermoplast Compos. Epub ahead of print 11 June 2019; 1–27. DOI: 10.1177/0892705719856063.

Kumar

Singh

, et al. On investigations of PLA reinforced composite feedstock filaments for multi-material three dimensional printing applications. J Mech Eng Sci (iMech Part C) 2019; inpress.

Hong

Eliaz

Leisk

, et al. A new Ti-5Ag alloy for customized prostheses by three-dimensional printing (3DP™). J Dent Res 2001; 80(3): 860–863.

Ballard

Trace

Ali

, et al. Clinical applications of 3D printing: primer for radiologists. Acad Radiol 2018; 25(1): 52–65.

10.

Dawoud

Taha

Ebeid

. Strain sensing behaviour of 3D printed carbon black filled ABS. J Manuf Process 2018; 35: 337–342.

11.

Divyathej

Varun

Rajeev

. Analysis of mechanical behavior of 3D printed ABS parts by experiments. Int J Sci Eng Res 2016; 7(3): 116–124.

12.

Durfee

Iaizzo

. Medical applications of 3D printing. In: Iaizzo

(ed) Engineering in medicine. Cambridge: Academic Press, 2019, pp. 527–543.

13.

VanKoevering

Morrison

Prabhu

, et al. Antenatal three-dimensional printing of aberrant facial anatomy. Pediatrics 2015; 136(5): e1382.

14.

Benwood

Anstey

Andrzejewski

, et al. Improving the impact strength and heat resistance of 3D printed models: structure, property, and processing correlationships during fused deposition modeling (FDM) of poly (lactic acid). ACS Omega 2018; 3(4): 4400–4411.

15.

Luan

Yao

Liu

, et al. Self-monitoring continuous carbon fiber reinforced thermoplastic based on dual-material three-dimensional printing integration process. Carbon 2018; 140: 100–111.

16.

Liu

. Rapid prototyping of continuous carbon fiber reinforced polylactic acid composites by 3D printing. J Mater Process Tech 2016; 238: 218–225.

17.

Drummer

Cifuentes-Cuéllar

Rietzel

. Suitability of PLA/TCP for fused deposition modeling. Rapid Prototyping J 2012; 18(6): 500–507.

18.

Rahim

Abdullah

Akil

, et al. The improvement of mechanical and thermal properties of polyamide 12 3D printed parts by fused deposition modelling. eXPRESS Polym Lett 2017; 11(12): 963–982.

19.

Han

Yang

, et al. Carbon fiber reinforced PEEK composites based on 3D-printing technology for orthopedic and dental applications. J Clin Med 2019; 8(2): 240.

20.

Weng

Wang

Senthil

, et al. Mechanical and thermal properties of ABS/montmorillonite nanocomposites for fused deposition modeling 3D printing. Mater Design 2016; 102: 276–283.

21.

Oladapo

Zahedi

Adeoye

. 3D printing of bone scaffolds with hybrid biomaterials. Compos Part B Eng 2019; 158: 428–436.

22.

Le Duigou

Castro

Bevan

, et al. 3D printing of wood fibre biocomposites: from mechanical to actuation functionality. Mater Design 2016; 96: 106–114.

23.

, et al. Influence of layer thickness, raster angle, deformation temperature and recovery temperature on the shape-memory effect of 3D-printed polylactic acid samples. Materials 2017; 10(8): 970.

24.

Kalita

Bose

Hosick

, et al. Development of controlled porosity polymer-ceramic composite scaffolds via fused deposition modeling. Mater Sci Eng C 2003; 23(5): 611–620.

25.

Blok

Longana

, et al. An investigation into 3D printing of fibre reinforced thermoplastic composites. Addit Manuf 2018; 22:176–186.

26.

Yao

Deng

Zhang

, et al. A method to predict the ultimate tensile strength of 3D printing polylactic acid (PLA) materials with different printing orientations. Compos Part B Eng 2019; 163: 393–402.

27.

Chen

, et al. 3D printing of ceramics: a review. J Eur Ceram Soc 2019; 39(4): 661–687. DOI: 10.1016/j.jeurceramsoc.2018.11.013.

28.

Hamidi

Tadesse

. Single step 3D printing of bioinspired structures via metal reinforced thermoplastic and highly stretchable elastomer. Compos Struct 2019; 210: 250–261.

29.

Hao

Liu

Zhou

, et al. Preparation and characterization of 3D printed continuous carbon fiber reinforced thermosetting composites. Polym Test 2018; 65: 29–34.

30.

Janssen

Peters

Brecher

. Efficient production of tailored structural thermoplastic composite parts by combining tape placement and 3D printing. Proc CIRP 2017; 66: 91–95.

31.

Tlegenov

Hong

. Nozzle condition monitoring in 3D printing. Robot CIM-Int Manuf 2018; 54: 45–55.

32.

Liu

. A novel free-hanging 3D printing method for continuous carbon fiber reinforced thermoplastic lattice truss core structures. Mater Design 2018; 137: 235–244.

33.

Quan

Suhr

, et al. Printing direction dependence of mechanical behavior of additively manufactured 3D preforms and composites. Compos Struct 2018; 184: 917–923.

34.

Corcione

Palumbo

Masciullo

, et al. Fused deposition modeling (FDM): an innovative technique aimed at reusing Lecce stone waste for industrial design and building applications. Constr Build Mater 2018; 158: 276–284.

35.

Sezer

Eren

. FDM 3D printing of MWCNT re-inforced ABS nano-composite parts with enhanced mechanical and electrical properties. J Manuf Process 2019; 37: 339–347.

36.

Xia

Nematollahi

Sanjayan

. Printability, accuracy and strength of geopolymer made using powder-based 3D printing for construction applications. Automat Constr 2019; 101: 179–189.

37.

Singh

Kumar

, et al. Waste thermosetting polymer and ceramicas reinforcement in thermoplastic matrix for sustainability: thermomechanical investigations. J Thermoplast Compos 2019. DOI: 10.1177/0892705719847237.

Experiment number	Composition	Infill ratio	Infill speed (mm s⁻¹)
1	C1	60	50
2	C1	80	60
3	C1	100	70
4	C2	60	60
5	C2	80	70
6	C2	100	50
7	C3	60	70
8	C3	80	50
9	C3	100	60

Experiment number	Composition	Infill ratio	Infill speed (mm s⁻¹)
1	C1	60	50
2	C1	80	60
3	C1	100	70
4	C2	60	60
5	C2	80	70
6	C2	100	50
7	C3	60	70
8	C3	80	50
9	C3	100	60

Investigations on 3D printed thermosetting and ceramic-reinforced recycled thermoplastic-based functional prototypes

Abstract

Keywords

Introduction

Materials and methods

Experimentation

Results and discussion

Prediction for peak strength at proposed settings

Conclusions

Footnotes

Acknowledgement

Funding

ORCID iD

References

Experiment number	Composition	Infill ratio	Infill speed (mm s⁻¹)
1	C1	60	50
2	C1	80	60
3	C1	100	70
4	C2	60	60
5	C2	80	70
6	C2	100	50
7	C3	60	70
8	C3	80	50
9	C3	100	60