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Abstract

In the last two decades, several studies have been conducted for the process parametric optimization of fused filament fabrication (FFF) with a variety of thermoplastic composites, especially for mechanical properties. But hitherto less has been conveyed, on the development of dynamic reduced order models (ROMs) for digital twining (DT) of tensile properties (of 3D printed implants/scaffolds) with novel thermoplastic-based composites. In this study, for the generation of dynamic ROM (for hybrid analytics), the signal-to-noise (S/N) ratio was used to ascertain the best settings of parameters for tensile properties of polyvinylidene fluoride (PVDF) composite. The study suggests that the best setting of the FFF process, for the 3D printing of PVDF composite (90% PVDF, 8% hydroxyapatite (HAp), and 2% Chitosan (CS) (for maximizing the tensile properties as per ASTM-D638-Type-V) are nozzle temperature (NT) of 235°C, raster angle (RA) 45°, printing speed (PS) of 60 mm/s respectively resulting in peak load (PL) 394.87 N, peak stress (PSt) 33.92 MPa, Young’s modulus (E) 2.606 MPa. For a modulus of toughness (MOT) of 0.484 MPa, the best settings are NT 230°C, RA 90°, and PS 50 mm/s. The results are supported by the morphological analysis.

Keywords

Polyvinylidene fluoride melt processing tensile strength 3D printing

Introduction

In the commercial FFF process, melted thermoplastic filament is deposited layer by layer to produce 3D objects.^1–4 The use of FFF-based components in healthcare applications (such as medical equipment, tissue engineering, medicine distribution, and surgical planning) has great potential since it has revolutionized the way things are designed and constructed.⁵ The PVDF^6,7 has been used in biomedical applications due to its biocompatibility, mechanical properties, and chemical resistance. It has been regarded as a potential implant material for a variety of uses, including tissue engineering for the heart, nerve regeneration, and bone regrowth. For implants made of Ti or stainless steel, PVDF has been used as a covering to promote biocompatibility and osteointegration in bone regeneration.^8,9 Implants of PVDF^10,11 have been demonstrated to promote bone tissue formation and reduce implant failure rates. As a substrate for nerve conduits, which are the tubular structures that direct the regeneration of wounded nerves, PVDF has been used in nerve regeneration. In animal models, PVDF nerve conduits have shown enhanced nerve regrowth and functional recovery. As a material for scaffolds that encourage the development of cells into functional cardiac tissues, PVDF has been used in cardiac tissue engineering applications.¹² In veterinary applications, PVDF structures have been used to promote the growth of tissue from the heart and improve cardiac function. In the field of implant dentistry and tissue engineering, PVDF composites are regarded as important biological materials.¹³ HAp is the primary chemical component of bone and is also found in teeth, where it contributes to strength and durability.^14,15 Numerous biological applications, including bone transplants, dental implants, and drug delivery systems, make use of HAp.¹⁶ It is an intriguing substance for biomedical applications due to its biocompatibility, osteoconductive qualities, chemical similarity to genuine bone, and bioactivity.¹⁷ Chitosan (CS)¹⁸ is a biopolymer made from chitin, a naturally occurring polymer found in crustacean exoskeletons such as shrimp, fish, and crabs, as well as fungal cell walls. Deacetylating chitin removes acetyl groups from the polymer chain, resulting in CS. Biocompatibility, biodegradability, antibacterial characteristics, and hemostatic qualities are some of the features that make CS an appealing material for biomedical engineering. Wound dressings, medication delivery systems, and dentistry applications all employ CS.^19,20

Single screw extruder (SSE) is a method of molding thermoplastics^21,22 that involves melting the polymer before shaping it through different processing methods. A multitude of characteristics, including the conditions of processing, polymer chemical composition, and desired application, influence the impact of melt processing on thermoplastic substances.^23,24 These variables must be carefully addressed to ensure that the desired material properties are maintained during the melt process procedure. Melt processing cycles may alter the tensile properties of PVDF composites.^25,26 The fabrication parameters along with the number of cycles need to be carefully determined to achieve the required mechanical properties of the composite.^27,28 In the last two decades, several studies have been conducted for the process parametric optimization of FFF with a variety of thermoplastic composites, especially for mechanical properties. But hitherto less has been conveyed, on the development of dynamic ROM for DT of tensile properties while 3D printing (of implants/scaffolds) with novel thermoplastic-based composites. In this study, for the generation of dynamic ROM (for hybrid analytics), the S/N ratio was used to ascertain the best settings of parameters for tensile properties of 3D printed PVDF composite (90% PVDF, 8% HAp, and 2% CS) as an extension of previously reported study on characterization of PVDF composite.⁸

Materials and methods

PVDF (in granular form) of extrusion grade was procured from a local market (Solvay, Gujarat, India). The pellet has a density of 1.75 g/cm³, a melting temperature of 160–190°C, and a glass transition temperature (T_g) of -40°C. CS (deacetylation degree >90%, pH 7–9 at 25°C) and HAp (colorless and brittle) were procured from Marine Hydrocolloids, Kochi, Kerala, India. The methodology adopted for this study is shown in Figure 1.

Figure 1.

Methodology adopted.

The tensile test specimen was prepared as per ASTM-D638-Type-V (Figure 2(a)). The open-source 3D printer (FFF) was used for printing tensile samples. A tensile test was performed on the micro-UTM (Make: FUTEK, Model: MBA 500, torque and thrust biaxial sensor) (Figure 2(b)).

Figure 2.

(a)Tensile test specimen, (b) micro UTM.

Table 1 shows the input process parameters selected for FFF. The remaining parameters (bed temperature: 60°C, infill pattern: rectilinear, wall thickness: 0.4 mm, infill density: 100%, layer thickness 0.10 mm, No. of top/bottom layers: 2, No. of perimeters: 2, etc.) were kept fixed. A design of the experiment (D.O.E.) was created using three factors NT, PS, RA, and three levels (Table 1) as per Taguchi L₉ orthogonal array (OA) along with observed mechanical properties (Table 2).

Table 1.

Selected parameters for FFF.

Factor	Levels
Factor	Level I	Level II	Level III
NT (°C)	225	230	235
PS (mm/s)	40	50	60
RA (°)	0	45	90

Table 2.

Tensile test observations.

R. No	Input parameter			Output parameter				S/N ratio (db)
R. No	NT (°C)	PS (mm/s)	RA (°)	PL (N)	PSt (MPa)	E (MPa)	MOT (MPa)	PL	PSt	E	MOT
R1	225	40	0	169.12 ± 0.96	12.56 ± 0.27	1.77 ± 0.12	0.26 ± 0.03	44.5638	21.9770	4.93489	−11.8352
R2	225	50	45	197.09 ± 0.83	16.57 ± 0.28	1.98 ± 0.30	0.42 ± 0.04	45.8930	24.3791	5.91134	−7.6810
R3	225	60	90	274.22 ± 0.72	18.04 ± 0.53	2.15 ± 0.09	0.34 ± 0.03	48.7620	25.1204	6.64069	−9.4217
R4	230	40	45	287.05 ± 0.19	21.43 ± 0.29	2.12 ± 0.44	0.38 ± 0.02	49.1591	26.6204	6.50621	−8.5658
R5	230	50	90	377.67 ± 0.70	22.39 ± 0.45	2.25 ± 0.39	0.49 ± 0.04	51.5423	26.9991	6.98166	−6.3031
R6	230	60	0	393.21 ± 0.89	25.24 ± 0.45	2.31 ± 0.39	0.31 ± 0.03	51.8923	28.0401	7.26095	−10.3999
R7	235	40	90	393.21 ± 0.85	27.52 ± 0.49	2.41 ± 0.45	0.45 ± 0.02	51.8923	28.7904	7.61146	−7.4527
R8	235	50	0	319.21 ± 0.56	28.93 ± 0.52	2.44 ± 0.32	0.37 ± 0.03	50.0813	29.2255	7.73711	−8.8258
R9	235	60	45	394.88 ± 1.18	33.92 ± 0.42	2.61 ± 0.48	0.37 ± 0.03	51.9291	30.6091	8.31949	−8.7780

Note: The tensile testing of samples (R1 to R9) as per the ASTM-D 638-type-V standard was performed on the UTM.

S/N ratios for tensile properties were calculated for the “larger is better type case” as per equation (1) (Table 2). The stress v/s strain diagram is shown in Figure 3.

\frac{S}{N} = - 10 * \log_{10} {\frac{1}{n} \sum_{i = 1}^{n} \frac{1}{{y_{i}}^{2}}}

(1)

Where n represents the number of observations y_i represents the value of the observed characteristic.

Figure 3.

Stress versus strain diagram of tensile specimens.

Results and discussion

Based on Table 2, an analysis of variance (ANOVA) was performed (Table 3) for all mechanical properties. The rank table for the PL, PSt, E, and MOT is depicted in Table 4. Further, the plots of the primary effect and residual effect for the S/N ratio are shown in Table 5 respectively.

Table 3.

ANOVA table for S/N ratio and percentage of contribution for tensile properties.

Tensile characteristics	ANOVA table for S/N ratio
Tensile characteristics	Source	DEF	Sequ SS	Adju SS	Adju MS	Fi	Pr	Percentage contribution (%)
PL	NT (°C)	2	44.0246	44.0246	22.0123	71.97	0.014	73.511
	PS (mm/s)	2	8.6492	8.6492	4.3246	14.14	0.066	14.442
	RA (°)	2	6.6025	6.6025	3.3012	10.79	0.085	11.024
	Residual error	2	0.6117	0.6117	0.3059			1.021
	Total	8	59.8880
PSt	NT (°C)	2	49.5870	49.5870	24.7935	96.92	0.010	85.684
	PS (mm/s)	2	6.7877	6.7877	3.3939	13.27	0.070	11.728
	RA (°)	2	0.9852	0.9852	0.4926	1.93	0.342	1.702
	Residual error	2	0.5117	0.5117	0.2558			0.884
	Total	8	57.8717
E	NT (°C)	2	6.37427	6.37427	3.18713	78.60	0.013	75.740
	PS (mm/s)	2	1.67331	1.67331	0.83665	20.63	0.046	19.882
	RA (°)	2	0.28728	0.28728	0.14364	3.54	0.220	3.415
	Residual error	2	0.08110	0.08110	0.04055			0.963
	Total	8	8.41596
MOT	NT (°C)	2	3.1747	3.1747	1.5874	8.03	0.111	14.755
	PS (mm/s)	2	6.6128	6.6128	3.3064	16.72	0.056	30.734
	RA (°)	2	11.3329	11.3329	5.6664	28.65	0.034	52.671
	Residual error	2	0.3956	0.3956	0.1978			1.838
	Total	8	21.5160

Note: Fi: fisher’s value; Adju: adhusted; SS: sum of squares; MS: mean of squares; DEF: degree of freedom; Pr: probability.

Table 4.

Rank table for tensile characteristics.

Tensile characteristics	Rank table
Tensile characteristics	Level	NT (°C)	PS (mm/s)	RA (°)
PL	1	46.41	48.54	48.85
	2	50.86	49.17	48.99
	3	51.30	50.86	50.73
	Delta	4.89	2.32	1.89
	Rank	1	2	3
PSt	1	23.83	25.80	26.41
	2	27.22	26.87	27.20
	3	29.54	27.92	26.97
	Delta	5.72	2.13	0.79
	Rank	1	2	3
E	1	5.829	6.351	6.644
	2	6.916	6.877	6.912
	3	7.889	7.407	7.078
	Delta	2.060	1.056	0.434
	Rank	1	2	3
MOT	1	−9.646	−9.285	−10.354
	2	−8.423	−7.603	−8.342
	3	−8.352	−9.533	−7.726
	Delta	1.294	1.930	2.628
	Rank	3	2	1

Table 5.

Main effect and residual plots for S/N ratios of tensile properties.

The probability (P)-value for the PL, PSt, E, and MOT were also calculated. As observed from Table 5, for PL, PSt the only significant parameter is NT. For E, NT and PS were observed as significant parameters. As regards MOT, RA was observed as the only significant parameter. A regression analysis was also performed to establish a relationship between the input and output parameters (equations (2)–(5)).

PL (N) = - 3472 + 15.56 NT (° C) + 3.55 PS (mm / s) + 0.606 RA (°)

(2)

PSt (MPa) = - 321.6 + 1.4405 NT (° C) + 0.2615 PS (mm / s) + 0.00449 RA (°)

(3)

E (MPa) = - 10.411 + 0.05190 NT (° C) + 0.012983 PS (mm / s) + 0.001019 RA (°)

(4)

MOT (MPa) = - 0.74 + 0.00477 NT (° C) - 0.00082 PS (mm / s) + 0.001207 RA (°)

(5)

Based on Table 5, it has been ascertained that enhancing the NT, PL, PSt, E, and MOT improved. This may be because high temperature helped in the proper stacking of layers. Raising the NT resulted in an appropriate flow of materials while laying layers, leading to higher layer adhesion. As a result, raising the NT enhanced the tensile strength. At the same time, the tensile strength was also increased by increasing the PS as this may have resulted in less cooling time for intermediate-layer stacking and resulted in improved interlayer fusion. Furthermore, increasing the RA from 0 to 90° increased the PL, E, and MOT. The PSt was increased up to 45° RA but decreased at 90°, this may be due to its layer orientation. The PL results indicate that NT, PS, and RA contribute 73.51%, 14.40%, and 11.02% respectively. For the PSt, the percentage of contribution was observed as 85.68%, 11.72%, and 1.70%. For the E, the percentage of contribution was 75.74%, 19.88%, and 3.41% respectively. The residual error contributed 1.02% for PL, 0.88% for PSt, and 0.96% for E (Table 3). The MOT was increased with an increase in RA, due to better layer orientation resulting in more strain. For MOT, results indicate that the NT, PS, and RA contribute 14.75%, 30.73%, and 52.67% respectively. Table 6 shows the SEM images along with porosity (%) as per ASTM B 276 and grain size No. as per ASTM E 112 of samples with the best and worst mechanical properties (PL, PSt, E, and MOT) as per Table 2. It may be noted that the samples prepared as per R1 (Table 2) resulted in the worst mechanical properties and samples prepared as per R9 resulted in the best mechanical properties in terms of PL, PSt, and E. For MOT, the best results were noticed for a sample as per R5. As observed from Table 6, the porosity (%), and grain size No. is in line with the observed mechanical properties (less porosity (%) and high grain size No. was observed for higher values of PL, PSt, and E). Further, for detailed analysis of surface characteristics, SEM images were processed on Gywddion software to give output in the form of 3D rendered images, surface roughness (Ra) profiles, amplitude distribution function (ADF), bearing ratio curve (BRC), and peak count (PC) (Table 7). As observed from Table 7, the samples with less porosity (%), higher grain size No. (i.e., with fine grains) have better Ra. The ADF (representing the probability of getting the highest peak) was more for samples prepared as per R1 as compared to R9 (for PL, PSt., and E), and R5 (for MOT). The BRC which also represents the cumulative probability to get a higher peak also followed the same pattern. As regards PC, for samples R9 and R5, the values were observed less than R1. These observations are counter-verified by 3D-rendered images which clearly show a more uniform distribution of grains in R5 and R9.

Table 6.

SEM, porosity (%), and grain size no. for the worst and best samples (as per Table 2).

Table 7.

Analysis of surface characteristics (as per Table 6).

For digital twinning, of the FFF process for tensile properties of PVDF composites-based functional prototypes, there are two main requirements: (I) real-time data and (II) development of dynamic ROM. For real-time data, it is proposed to use a sensor based on the concept of a microstrip patch antenna (MPA).^8,25,27 In a standard MPA-based sensor, the dielectric property in terms of dielectric constant (ϵ_r) is monitored using a Vector network analyzer (VNA) and significant studies have been reported on the calibration of VNA output in industrial, scientific, and medical (ISM) band for fetching this data in Bluetooth/Wi-Fi range through a smartphone as an internet of things (IoT) based solution. The idea here is to calibrate the change in ϵ_r while tensile loading with the in-process 3D printing using FFF. In other words, when the tensile load is applied on FFF based printed sample, there is a change in ϵ_r before and after putting the load. Similarly, with 3D printing, there will be a change in ϵ_r of the sensor (when the sample is printed under different processing conditions i.e., input parameters). For example, if one is putting high infill density while printing the ϵ_r will be different in comparison to low infill density. Similarly, there will be the effect of NT, PS, and other input parameters. This helps to generate real-time data while printing and mimics the change in ϵ_r while destructive testing (tensile loading). Hence one may predict the mechanical property online while printing itself before going to the destructive testing stage (by using the dynamic ROM developed based on previous experimental data). The schematic for the proposed solution is shown in Figure 4.

Figure 4.

Digital twin model of tensile properties for PVDF composites.

Conclusions

In this study, PVDF composite has been explored for PL, PSt, E, and MOT in response to selected input process parameters (NT, PS, and RA) for the development of dynamic ROM (for digital twinning). The following are the conclusions from this study:-

• The study suggests that NT has a considerable impact on PL, PSt, and E. Statistical analysis showed that the PL, PSt, and E increase with an increase in NT. The reverse trend for the same was observed for the RA.

• The study suggests that the best setting of the FFF process, for the 3D printing of PVDF composite (90% PVDF, 8% HAp, and 2% CS (for maximizing the tensile properties as per ASTM-D638-Type-V) are NT 235°C, RA 45°, PS 60 mm/s respectively resulting in PL 394.87 N, PSt 33.92 MPa, E 2.606 MPa. For a MOT of 0.484 MPa, the best settings are NT 230°C, RA 90°, and PS 50 mm/s.

• Finally, a dynamic ROM for digital twin has been prepared to assist in hybrid analytics.

Further studies may be conducted to use the proposed dynamic ROM for understanding the failure mechanism while tensile loading of selected PVDF composite.

Footnotes

Acknowledgements

The authors acknowledge the research support provided by the National Institute of Technical Teachers Training and Research Chandigarh.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors are thankful to the Department of Science and Technology for funding under FIST Level-0, Project No. SR/FST/College-/2020/997.

ORCID iDs

Minhaz Husain

Rupinder Singh

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On digital twinning of fused filament fabrication for tensile properties of polyvinylidene fluoride composites-based functional prototypes

Abstract

Keywords

Introduction

Materials and methods

Results and discussion

Conclusions

Footnotes

Acknowledgements

Declaration of conflicting interests

Funding

ORCID iDs

References