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Additive manufacturing of mechanical testing samples

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3D printing in additive manufacturing is considered as one of key technologies to the future high-precision manufacturing in order to benefit diverse industries in building construction, product development, biomedical innovation. Yu (Roger) Dong discusses 3D printed mechanical testing samples of PLA and PLA/wood fibre composites.

Image courtesy of interviewee. May 12, 2024

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This video video discusses the significance of 3D printing, also known as additive manufacturing, in advanced manufacturing technologies, contrasting it with traditional subtractive methods. It emphasizes the layer-by-layer approach that allows for complex geometries and reduced material usage and costs. The focus is primarily on Fused Deposition Modeling (FDM) for producing polymeric materials, particularly polylactic acid (PLA), known for its biodegradability and environmental friendliness.

Key aspects of the study include identifying factors influencing the tensile strength of 3D printed materials, with findings indicating that the number of shells is the most significant factor. The study uses statistical design experiments, specifically the Taguchi design of experiments (DOE), coupled with analysis of variance (ANOVA), to determine optimal material formulations and processing parameters.

The results indicate that while increasing the number of shells enhances tensile strength, the type of material (with or without wood fibers in PLA) also plays a critical role in mechanical properties. The presence of defects such as voids can weaken the strength, but increasing the number of shells can mitigate this issue.

This video video concludes by highlighting the importance of balancing material selection, processing parameters, design properties, printing time, and cost for optimal outcomes in 3D printing. It suggests that future research may benefit from integrating machine learning and artificial intelligence to improve product design and optimization techniques in additive manufacturing while minimizing waste and cost.

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Here are some articles that are similar in theme to the video , discussing the significance and developments in 3D printing and additive manufacturing technologies:

1. **"Advancements in Additive Manufacturing: A Review"** - This article reviews recent developments in additive manufacturing technologies, highlighting their advantages over traditional manufacturing methods and discussing various approaches like Fused Deposition Modeling (FDM) and Selective Laser Sintering (SLS).

2. **"Optimizing 3D Printing Parameters for Enhanced Material Performance"** - This study explores various parameters that affect the performance of 3D printed materials, using design of experiments and statistical analysis to optimize tensile strength and overall material properties.

3. **"The Role of Material Composition in the Mechanical Properties of 3D Printed Parts"** - This paper focuses on how different compositions of 3D printing materials, such as PLA with additives like wood fibers, impact the mechanical strength and flexibility of printed components.

4. **"Utilization of Machine Learning in Optimizing Additive Manufacturing"** - This article discusses the integration of machine learning techniques in the additive manufacturing process, showcasing how AI can predict optimal printing parameters and enhance material performance while reducing waste.

5. **"Environmental Impact of 3D Printing Materials: Assessing the Benefits of Biodegradable Plastics"** - This article examines the environmental implications of using biodegradable materials like PLA in 3D printing, discussing both the benefits and the challenges associated with their production and use.

6. **"Layer-by-Layer Manufacturing: Understanding the Dynamics of FDM Technology"** - This research delves into the specifics of the layer-by-layer approach in FDM technology, analyzing how different process settings influence the outcome and structural integrity of printed models.

7. **"Identifying Defects in 3D Printed Parts: Implications for Quality Control"** - This paper addresses common defects that arise during 3D printing, such as voids and misalignments, and discusses methods for quality assurance and improvement in manufacturing consistency.

These articles cover topics related to 3D printing technologies, material science, optimization techniques, and environmental considerations, similar to the themes discussed in the original video.

X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

00:00.135 --> 00:03.205
3D printing or additive manufacturing is,

00:03.845 --> 00:06.965
a quite important advanced manufacturing technology

00:07.225 --> 00:11.805
as opposed to, subtractive manufacturing like,

00:11.875 --> 00:15.445
milling, machining, extrusion, injection molding, et cetera.

00:15.825 --> 00:18.325
Uh, basic to deal with material processing

00:18.905 --> 00:22.405
and the product development in a layer by layer manner

00:22.905 --> 00:24.565
for complex geometry

00:25.105 --> 00:28.045
and a significant reduction of material consumption of cost.

00:28.865 --> 00:30.765
Uh, so in particular,

00:30.905 --> 00:35.565
few deposition modeling we call FDM used in this study,

00:35.705 --> 00:37.925
mainly focused on additive manufacturing

00:38.185 --> 00:39.805
of polymeric materials

00:40.225 --> 00:43.005
and the polymer composites, which is,

00:43.005 --> 00:44.845
follows a very similar principle of

00:45.435 --> 00:48.725
melt extrusion in the conventional polymer processing

00:49.035 --> 00:52.365
that you, you know, melt the polymers up

00:52.365 --> 00:53.525
to its melting temperature.

00:53.905 --> 00:56.965
And then the filament material will, go

00:56.965 --> 01:00.165
through the nozzles to actually build up the layers,

01:00.485 --> 01:03.005
structures by, you know, in the materials

01:03.225 --> 01:04.605
for making the products.

01:05.465 --> 01:09.525
Um, so I believe adding manufacturing,

01:10.245 --> 01:13.685
probably going to in combination of artificial intelligence,

01:14.345 --> 01:17.365
will offer some future opportunity,

01:17.425 --> 01:21.725
in advanced smart manufacturing towards, industrial 4.0.

01:22.185 --> 01:25.485
Um, you know, that's gonna be a very broad,

01:25.485 --> 01:27.405
opportunities and, and the scope.

01:27.955 --> 01:30.525
Most polymeric material polymer comps,

01:30.525 --> 01:31.605
mechanical properties,

01:31.695 --> 01:35.005
particular tenile strands is a key parameters

01:35.505 --> 01:38.125
to identify load bearing capacity.

01:38.345 --> 01:41.925
So maximizing tensile strengths to reduce mechanical failure

01:42.625 --> 01:46.125
is of particularly important in structure, integrity

01:46.225 --> 01:48.245
and product quality and durability.

01:48.825 --> 01:53.445
So we attempt, to 3D print polylactic acid PLA,

01:53.445 --> 01:57.565
which is wide used in biomedical field, due

01:57.565 --> 01:59.965
to is about high biodegradability

02:00.745 --> 02:02.445
and, environmental friendly

02:02.705 --> 02:05.125
and bell compatibility, as well

02:05.125 --> 02:07.525
as some PLA wood fiber compasses

02:07.525 --> 02:10.405
to identify the key parameters,

02:10.425 --> 02:14.125
to influence tensile strengths, using statistical,

02:14.185 --> 02:15.365
design experiments.

02:15.985 --> 02:18.485
Uh, so among all parameters, we found

02:18.485 --> 02:21.125
that number shells is most significant factor

02:21.745 --> 02:25.805
to maximize the tensile strengths of a neat PL sample only,

02:26.425 --> 02:30.645
which is followed by layer height and infield density.

02:31.545 --> 02:32.645
Um, you know, generally

02:32.665 --> 02:35.565
as expected in additive manufacturing, particularly in the,

02:35.565 --> 02:37.405
fields deposition modeling process.

02:37.905 --> 02:41.325
So more number shells generally means, you know,

02:41.465 --> 02:44.645
better build up for the hierarchical structures and then the

02:44.645 --> 02:46.165
therefore the higher strengths,

02:46.565 --> 02:49.125
stronger printed parts in the, need.

02:49.245 --> 02:52.445
PLA samples. Takeuchi design experiments,

02:52.545 --> 02:56.925
or DOE method has been widely used in identifying,

02:57.475 --> 02:58.725
significant factors,

02:59.215 --> 03:02.965
Parameters in robust design of products,

03:03.065 --> 03:06.525
and the parametric optimization in advanced manufacturing.

03:07.185 --> 03:10.885
Uh, it is often coupled with the analysis of variance ANOVA

03:11.225 --> 03:14.605
to detect, either optimum, material formulation

03:14.785 --> 03:17.645
or processing parameters in response

03:17.705 --> 03:21.805
to different targeted goals such as, the maximization

03:21.825 --> 03:23.125
of tenile strengths

03:23.345 --> 03:26.725
or minimization of dimensional errors, et cetera.

03:27.185 --> 03:29.885
So it's based on very limited orthogonal

03:29.885 --> 03:31.165
experimental design area.

03:31.625 --> 03:34.205
Uh, rather than going through the full skill experiments

03:34.505 --> 03:37.685
or full skill, trials, you know,

03:37.685 --> 03:40.725
basically can provide a sufficient optimization process

03:41.245 --> 03:43.245
analysis used mainly in the field

03:43.265 --> 03:46.725
of mechanical manufacturing and material engineering.

03:47.345 --> 03:50.405
Uh, so in our study we select three,

03:50.805 --> 03:52.605
commonly used 3D printing parameters,

03:52.855 --> 03:56.565
which includes the infield density layer thickness,

03:56.985 --> 04:00.565
and the number shells at two different levels in,

04:00.565 --> 04:03.525
initially designed L eight, you know,

04:03.745 --> 04:07.125
is based on experimental rans with factual interactions.

04:07.545 --> 04:10.405
So it has been found that number shells is most significant

04:10.585 --> 04:14.445
factor, used in the printed PLA samples.

04:14.955 --> 04:17.245
Further, we chose only number of shells

04:17.345 --> 04:20.685
and the material types, namely the PLA

04:20.685 --> 04:23.005
and the PLA wood fiber comps,

04:23.005 --> 04:24.805
to look at the reinforcement effect.

04:25.185 --> 04:29.565
And then we rerun the L six A, with two different levels

04:29.625 --> 04:33.325
to maximize oten cell impact strengths,

04:33.425 --> 04:38.165
as a DOE responses overall results, to conclude that,

04:38.225 --> 04:40.005
overall results that demonstrate

04:40.005 --> 04:43.045
that number shells plays a very,

04:43.045 --> 04:45.845
the most significant role in enhancing the tensile strengths

04:45.845 --> 04:48.805
of 3D printed, PLA samples.

04:49.745 --> 04:53.445
Uh, however, material type with without the,

04:53.445 --> 04:58.005
wood fibers in PLA is critical in influencing old,

04:58.155 --> 05:01.285
tensile flex impact strength in mechanical properties,

05:01.675 --> 05:03.645
typical reinforcement effect,

05:03.645 --> 05:06.485
which is very critical in the composite material systems.

05:07.185 --> 05:11.685
Um, this in our 3D printed PLA wood fiber compasses,

05:12.105 --> 05:13.125
that kind

05:13.125 --> 05:17.325
of reinforcing effect effect is not quite evident at all.

05:17.985 --> 05:21.525
And this normally due to the, very, you know, the,

05:21.545 --> 05:24.965
the detrimental mechanical strengths, in the compasses

05:24.985 --> 05:27.685
as opposed to those of the neat PLA.

05:28.025 --> 05:29.325
So we did some,

05:29.475 --> 05:33.085
microscopic analysis using the optical microscope,

05:33.585 --> 05:36.285
and we found, and, that most

05:36.285 --> 05:38.485
of the printed material samples inevitably

05:39.155 --> 05:40.325
possess either single

05:40.345 --> 05:43.485
or multiple, field, cavities or void.

05:43.665 --> 05:47.405
So these void or cavities are actually open cavities are

05:47.605 --> 05:49.525
actually typical defects to

05:50.045 --> 05:51.645
deteriorate mechanical strengths.

05:52.025 --> 05:54.725
Uh, although that's, if we increase the number of shells,

05:54.755 --> 05:56.685
that kind of defect could be

05:56.685 --> 05:59.125
Significant, could be significantly reduced.

05:59.875 --> 06:04.565
This study actually shows how important material selection

06:04.585 --> 06:07.445
and 3D printing parameters,

06:07.785 --> 06:10.725
can ultimately influence, you know,

06:10.725 --> 06:14.005
mechanical properties of final polymer or,

06:14.005 --> 06:15.245
or composite products.

06:15.385 --> 06:18.245
So the design experiment approach is a deemed

06:18.305 --> 06:20.285
and appropriate method for material

06:20.905 --> 06:24.485
and processing optimization in contemporary manufacturing.

06:25.035 --> 06:29.285
However, further research, may gradually shift

06:29.345 --> 06:31.045
to the use of machine learning,

06:31.155 --> 06:33.805
deep learning in artificial intelligence

06:33.805 --> 06:35.485
to achieve better accuracy

06:35.985 --> 06:39.725
and robustness of design of product optimization,

06:39.905 --> 06:43.325
in place of the statistical design experiments

06:43.645 --> 06:45.005
approach or analysis.

06:45.705 --> 06:49.285
Uh, another very important point I like to raise here is

06:49.385 --> 06:53.205
for most the design, material, researchers,

06:53.645 --> 06:56.325
scientists, the, a good balance should be generally made

06:56.555 --> 07:00.125
between the material selection, processing parameters,

07:00.585 --> 07:03.525
design properties, and 3D printing time

07:03.625 --> 07:05.845
and material cost, in the future.

07:06.145 --> 07:09.685
So that, you know, not only we can actually,

07:09.715 --> 07:11.805
well utilize this advanced,

07:12.245 --> 07:15.485
additive manufacturing 3D printing, technology,

07:15.585 --> 07:18.805
but also we could actually achieve the best,

07:18.805 --> 07:22.245
product quality with the minimum, material waste and,

07:22.265 --> 07:22.925
and the cost.

00:00 - 3D printing or additive manufacturing is, a quite important advanced manufacturing technology as opposed to, subtractive manufacturing like, milling, machining, extrusion, injection molding, et cetera. basic to deal with material processing and the product development in a layer by layer manner

00:22 - for complex geometry and a significant reduction of material consumption of cost. so in particular, few deposition modeling we call FDM used in this study, mainly focused on additive manufacturing of polymeric materials and the polymer composites, which is, follows a very similar principle of melt extrusion in the conventional polymer processing that you, you know, melt the polymers up

00:52 - to its melting temperature. And then the filament material will, go through the nozzles to actually build up the layers, structures by, you know, in the materials for making the products. so I believe adding manufacturing, probably going to in combination of artificial intelligence, will offer some future opportunity, in advanced smart manufacturing towards, industrial 4.0. you know, that's gonna be a very broad,

01:25 - opportunities and, and the scope. Most polymeric material polymer comps, mechanical properties, particular tenile strands is a key parameters to identify load bearing capacity. So maximizing tensile strengths to reduce mechanical failure is of particularly important in structure, integrity and product quality and durability. So we attempt, to 3D print polylactic acid PLA, which is wide used in biomedical field, due

01:57 - to is about high biodegradability and, environmental friendly and bell compatibility, as well as some PLA wood fiber compasses to identify the key parameters, to influence tensile strengths, using statistical, design experiments. so among all parameters, we found that number shells is most significant factor to maximize the tensile strengths of a neat PL sample only,

02:26 - which is followed by layer height and infield density. you know, generally as expected in additive manufacturing, particularly in the, fields deposition modeling process. So more number shells generally means, you know, better build up for the hierarchical structures and then the therefore the higher strengths, stronger printed parts in the, need. PLA samples. Takeuchi design experiments, or DOE method has been widely used in identifying,

02:57 - significant factors, Parameters in robust design of products, and the parametric optimization in advanced manufacturing. it is often coupled with the analysis of variance ANOVA to detect, either optimum, material formulation or processing parameters in response to different targeted goals such as, the maximization of tenile strengths or minimization of dimensional errors, et cetera. So it's based on very limited orthogonal

03:29 - experimental design area. rather than going through the full skill experiments or full skill, trials, you know, basically can provide a sufficient optimization process analysis used mainly in the field of mechanical manufacturing and material engineering. so in our study we select three, commonly used 3D printing parameters, which includes the infield density layer thickness, and the number shells at two different levels in,

04:00 - initially designed L eight, you know, is based on experimental rans with factual interactions. So it has been found that number shells is most significant factor, used in the printed PLA samples. Further, we chose only number of shells and the material types, namely the PLA and the PLA wood fiber comps, to look at the reinforcement effect. And then we rerun the L six A, with two different levels to maximize oten cell impact strengths,

04:33 - as a DOE responses overall results, to conclude that, overall results that demonstrate that number shells plays a very, the most significant role in enhancing the tensile strengths of 3D printed, PLA samples. however, material type with without the, wood fibers in PLA is critical in influencing old, tensile flex impact strength in mechanical properties, typical reinforcement effect, which is very critical in the composite material systems.

05:07 - this in our 3D printed PLA wood fiber compasses, that kind of reinforcing effect effect is not quite evident at all. And this normally due to the, very, you know, the, the detrimental mechanical strengths, in the compasses as opposed to those of the neat PLA. So we did some, microscopic analysis using the optical microscope, and we found, and, that most of the printed material samples inevitably

05:39 - possess either single or multiple, field, cavities or void. So these void or cavities are actually open cavities are actually typical defects to deteriorate mechanical strengths. although that's, if we increase the number of shells, that kind of defect could be Significant, could be significantly reduced. This study actually shows how important material selection and 3D printing parameters,

06:07 - can ultimately influence, you know, mechanical properties of final polymer or, or composite products. So the design experiment approach is a deemed and appropriate method for material and processing optimization in contemporary manufacturing. However, further research, may gradually shift to the use of machine learning, deep learning in artificial intelligence to achieve better accuracy

06:35 - and robustness of design of product optimization, in place of the statistical design experiments approach or analysis. another very important point I like to raise here is for most the design, material, researchers, scientists, the, a good balance should be generally made between the material selection, processing parameters, design properties, and 3D printing time and material cost, in the future. So that, you know, not only we can actually,

07:09 - well utilize this advanced, additive manufacturing 3D printing, technology, but also we could actually achieve the best, product quality with the minimum, material waste and, and the cost.

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