In this article, the lightweight design of a palletizing
manipulator arm structure is carried out. The optimization target is
designed in 3D with Solid Works. To determine the optimization area and the
secondary reconstruction model after the structure is optimized, the
reliability and cost of the design structure are also considered. The
meta-software performs mechanical performance simulation experiments under
the corresponding working conditions for the lightweight structural design
of the target structure via the topology optimization methods. Finally, with
additive manufacturing technology, the design and printing of the filled
skeletal Voronoi structure and the nested-external-removal Voronoi structure
of the palletizing manipulator arm are performed.
Introduction
Additive manufacturing has clear advantages over traditional manufacturing
for the processing of complex structures. For the processing of non-complex
structural parts, the cost of additive manufacturing has always been one of
the most important obstacles to its use in industry. Because the cost of
additive manufacturing is proportional to the amount of the materials used,
the use of topology optimization design for the optimization of the material
layout and design of lightweight, high-performance structures is
particularly suitable for additive manufacturing (Anders et al., 2016; Huang
et al., 2013).
The lightweight design of a structure has developed from the early simple
size optimization (Fleury, 1979; Fleury and Sander, 1983; Haftka, 1982) to
the current shape optimization (Sokolowski and Zochowski, 1999; Haftka and
Grandhi, 1986; Zhang et al., 2019) to topology optimization (Wang et al.,
2003). Topology optimization is a structural optimization algorithm that
adopts the idea of a finite element. Based on the finite-element concept, the element with less stress in the design area is removed, thereby
obtaining the best force transmission path. During the initial stage of the
engineering structure design, the optimal layout scheme and the best form of
the force transmission are explored, as shown in Fig. 1 (Wang et al., 2003).
Topological optimization process using the level set
method: (a) original fixed model with constraints; (b–e) intermediate
optimization process for topology optimization; (f) final optimization of the
structure shape (Wang et al., 2003).
The current research effort is mainly focused on the use of topology
optimization to improve the reliability of a structure in engineering
applications and the development of new topology optimization methods to
optimize the stiffness of the structure. Shi et al. (2019) proposed a multi-constrained
stiffness optimization model based on uncertain loads that solved the
structural stiffness optimization problem of the volume and tail joint. Jiao proposed a method to solve the periodic layout
optimization problem of cyclically symmetric structures by guiding weights
(Jiao et al., 2019). By constructing virtual sector sub-domains, the periodic
layout optimization of cyclically symmetric structures was transformed into
the conventional topology optimization of virtual sector sub-domains.
Additive-free manufacturing technology has been used to design unmanned
aerial vehicles (UAVs) and was combined with additive manufacturing
technology to carry out the design of lightweight cell, lattice, and
honeycomb structures for structures such as wings. Composite materials are
used in lightweight design to achieve better design results and performance
in practical use (Goh et al., 2017).
Inspired by the scaffolding structure of buildings, Wang et al. (2013)
designed a hollow interior with a truss-skin structure on the outside, also
known as a skeleton-skin structure. The number of truss nodes and the truss
were optimized by topology optimization. The optimized design achieved
optimal structural mechanical properties. Additionally, the final printing
of the structure was demonstrated through fused deposition molding, and the
optimized model material was reduced by approximately 75 % compared to the
original model.
Stefan optimized the titanium alloy components of a bionic robot by
combining topology optimization technology and additive manufacturing
technology, improving the flexibility of the robot's activities to meet the
requirements for the finally obtained product (Junk et al., 2018). Cheng et
al. (2019) used topology optimization to functionally design gradient
lattices for components in additive manufacturing. It was found that the
structural framework optimized by the lattice can significantly improve the
mechanical properties of the structure and reduce the weight of the
components, as shown in Fig. 2 (Cheng et al., 2019).
Stent optimized using different printing technologies; (a) topology optimization model; (b) direct laser sintering of small lattices; (c) fused deposition molding; (d) direct laser sintering of large lattices (Cheng
et al., 2019).
Vaissier et al. (2019) used an improved genetic algorithm to perform topology optimization
on a support structure for additive manufacturing. The support structure was
designed with a lattice frame, a reduced number of support beams, and a
minimized support ratio of the support structure to the print. The model was
internally and externally supported, reducing material consumption (Vaissier
et al., 2019). Robbins used the topology optimization method to generate a
cell structure for the continuum and calculated the macro structure size by
assuming that the cell structure was uniform (Robbins et al., 2016). The
results show that the topologically optimized structure designed by the cell
structure under a load can meet the necessary requirements. The structure
can be processed and manufactured by 3D printing equipment, as shown in Fig. 3 (Robbins et al., 2016). Seabra et al. (2016) combined the advantages of
additive manufacturing to facilitate the molding of complex structural parts
and used topology optimization and selective laser melting to decrease the weight of an aircraft support. The optimized support was then tested. The
test results showed that the optimized bracket assembly significantly
reduces the overall quality of the structure but improves the safety factor.
Belhabib and Guessasma (2017) used the method of moving asymptotes to
conduct a finite-element analysis of a 3D fused deposition model (FDM) of a topology-optimized hollow structure and tested the printed 3D model by
compression testing to obtain model forces under different load conditions.
Topological optimization of the cell structure and its 3D printed
model: (a) coarse mesh cell topology optimization; (b) fine mesh cell topology
optimization; (c) stainless steel 3D printing (Robbins et al., 2016).
Wang et al. (2018) used the homogenization method to achieve a natural
frequency variable density fusion-molded printed honeycomb structure. By
optimizing the design of a cantilever plate, natural frequency optimization
was demonstrated on the basis of homogenization topology optimization. This
method can enhance the natural frequency of a structure and reduce its
weight. Lu et al. (2014) proposed an algorithm for hollowing out and filling
Voronoi structures based on honeycomb structures inside the model. This
algorithm obtains a higher intensity ratio by adjusting the size of each
Voronoi unit and the hollowing ratio of each model, as shown in Fig. 4 (Lu
et al., 2014). Rezaie et al. (2013) studied the implementation of the topology optimization method during the fused deposition simulation process, proposed a relatively simple method for the application of additive manufacturing to topology
optimization, and then implemented the method using the FDM. A comparison of
the results shows that even if very basic additive manufacturing equipment
is used in the study, the degree of deterioration of the complex contours
from topology optimization into a simple honeycomb structure is quite
limited. Liu and To (2017) proposed a new method for topology optimization based on a level set. This method solved the two main
problems of additive manufacturing design, namely the material anisotropy
and self-supporting manufacturability constraints. The multilevel set method
was used to solve the 3D parallel design problem. The multilevel set
function was used to represent the uniformly sliced additive manufacturing
part, and a new interpolation method of the multilevel set function was
proposed to solve the problem of self-supporting manufacturability
constraints.
Filled Voronoi (Tyson polygon) structure (Lu et al., 2014).
To date, few studies have examined the lightweighting of palletizing robots.
Most of the research on lightweighting of palletizing robots has focused on
the field of lightweight materials, but such lightweight materials are
mostly high-strength, difficult-to-machine materials such as titanium
alloys. This approach is more expensive than the use of structural
optimization design to obtain a lightweight structure of palletizing robots.
The present work does not present a new topology optimization technique or
an additive manufacturing method but rather focuses on the combination of
topology optimization and additive manufacturing technology. This approach
is used to obtain lightweight palletizing robots in order to meet the weight
reduction requirements of the palletizing robot while reducing the
processing difficulty of the structure after topology optimization. This
paper is organized as follows. Section 1 mainly introduces the application
of topology optimization in additive manufacturing. Section 2 uses 3D
modeling software and finite-element software to model the pallet arm's forearm. In Sect. 3, the lightweight design of the arm of the palletizing
manipulator is realized through topology optimization. In Sect. 4, the
finite-element analysis of the optimized manipulator's forearm is performed to verify the mechanical properties of the optimized manipulator. In Sect. 5, 3D printing of the arm of the palletizing manipulator after the
optimization is completed, and the processing feasibility of the scheme is
verified. The conclusions are given in Sect. 6.
Finite-element modeling of the palletizing manipulator armTopology optimization of the continuum based on the density-stiffness interpolation model
Prior to the topology optimization of the structure, the objective function,
design variables, and constraints should be determined as the three elements
of topology optimization. After these three elements are determined, the
general mathematical model of topology optimization can be expressed as follows.
Objective function:minf(x).Design variable:X=X1X2…XnT,gi(x)≤0,i=1,2,…,k(1).Restrictions:hi(x)=0,j=1,2,…,l.X – in the structural topology optimization problem, one or more sets of design variables corresponding to the objective function; f(x) – objective function, the ultimate goal of topology optimization.
Objective functions are mostly the structural flexibility, structural
weight, and structural size. gi(x),hi(x) – the constraints in the structural topology optimization process are represented by inequality and equality constraints.
Structural topology optimization is divided into discrete and continuous
topology optimization according to the types of optimization variables.
Currently, the main optimization methods include the homogenization method,
variable density method, variable thickness method, progressive structure
optimization method, independent continuous mapping method, and level set method (Lipson and Gwin, 1977). The solid isotropic material penalty model
known as SIMP (solid isotropic micro-structure with penalization) is the
main interpolation model for variable density topology optimization used in
current studies. Prior to optimization, it is necessary to assume that the
material density in the design area of the optimization object is variable,
and the optimization goal is the material density. The function is the
optimal distribution of the material. The advantage of the variable density
method is that the calculation time is reduced and the design procedure is
simple; its disadvantage is that the solution accuracy is lower than that of
the homogenization method (Sethian and Wiegmann, 2000; Sigmund, 1994; Young
et al., 1999).
The basic idea of the variable density method is based on the assumption
that a solid material is isotropic and that the relative density of the
variable material is artificially changed. The density is a design variable,
and the empirical formula is used to represent the nonlinear relationship
between the elastic modulus and density. This model is called the
interpolation model of the variable density topology optimization method
(Zuo and Saitou, 2017). The empirical formula for this nonlinear
relationship is given by
Ei=Emin+f(ρi)(Eo-Emin),i=1,2…N.
In the formula, Ei is the material elastic modulus of the ith unit;
Emin is the modulus of elasticity of the cavity element with an element density ρi of 0;
and E0 is the elastic modulus of a full material unit with a unit density ρi of 1.
The value of Emin is usually taken as Emin=E0/1000.
f(ρi) is the penalty function.
In the optimization design of the topological structure of the mechanical
products studied in this work, the SIMP interpolation model of the variable
density method is used. The general form of the interpolation model function
with penalty factor p is
fp(ρi)=ρip.
In the formula, ρi is the relative element density value, 0<ρmin≤ρi≤1; ρmin is the lower unit density value, and p is the penalty factor for the interpolation model.
The penalty effect determines the final optimization result. At the same
time, the penalty effect is determined by the value of the penalty factor p.
When the value is obtained in the space of the penalty factor, a larger
value of the penalty factor p will give a greater penalty effect. For the
optimization results, an excessively large or excessively small value of p will adversely affect the optimization results.
In this paper, in the topology optimization of the manipulator's forearm,
the penalty factor p is set to 0.3 to obtain the best topology optimization
result. The SIMP interpolation model based on the variable density method is
obtained, with the minimum flexibility as the optimization target and the
constraints on the volume and mass fractions of the material. The
mathematical model is given by
4x=(x1,x2,…,xn)T,5min:C(xe)=∑i=1mwwqci(xe)-cimncimax-cimin1p,s.t.:f=v-v1v0,dt1≤d‾,σt1≤σ‾,0<xmin≤xe≤1.
In the formula, m is the total number of working conditions under each load; ωi is the ith working condition weighted value;
p is the penalty factor of the interpolation model, taking p≥2;
ci(xe) is the compliance function under the ith working condition and is the objective function; cimax and cimin are the maximum compliance and minimum
compliance, respectively, at the ith operating condition;
v is the volume of the original structure model before optimization;
v0 is the design area volume during topology optimization;
v1 is the minimum density unit volume;
f is the residual volume percentage after the variable density topology
optimization;
dt1 and σt1 are the node displacements and stresses of the
corresponding elements under the first working condition;
d‾ and σ‾ are the upper limit of the joint
displacement and the stress of the structural element;
xmin is the node and displacement lower limit.
The optimization process is illustrated in detail in Fig. 5.
Topology optimization flowchart.
Robotic forearm simplified model
This work aims to perform lightweight structure design of the arm of a
numbering manipulator. The palletizing manipulator is mainly composed of a
base, a steering table, a robot arm, a robot arm and a pallet gripper, as shown in Fig. 6.
3D sketch of the palletizing robot.
To meet the purpose of use, the model parameters of the pallet arm
manipulator arm are analyzed, as shown in Table 1. To ensure that the finite-element software obtains accurate model results during the calculation
process, to reduce the calculation time of the finite-element analysis, and to save computer memory resources during the calculation, Solid Works is used to simplify the modeling of the manipulator.
When meshing, the middle part of the forearm of the manipulator is used as
the optimization area. To observe the mesh situation and ensure the accuracy
of the static and modal analyses, the parameter of the element size is set
to 8 mm. The part uses the default mesh size. The number of nodes of the
divided robotic arm model is 13 227, the number of meshes is 73 382, and the
meshed robotic arm model is shown in Fig. 7.
Robot arm forearm grid division.
The material of the manipulator forearm mostly uses lightweight materials
such as cast aluminum or Q235. These materials are used to ensure the
accuracy of the movement and the flexibility for grasping the material. The
properties of the material are shown in Table 2.
Under extreme conditions, the manipulator forearm is connected to the
manipulator arm through the forearm connection shaft. In this case, the
manipulator forearm can be regarded as a fixed constraint. According to the
above analysis, considering the weight and external load of the palletizing
gripper, the manipulator forearm has an end load of 600 N.
Static analysis of the manipulator forearm
A load of 600 N was applied to the manipulator forearm along the negative
direction of the z axis, and the model was subjected to a static analysis
and a static cloud diagram, as shown in Fig. 8.
Static cloud diagram of the manipulator forearm model; (a) equivalent stress cloud diagram; (b) equivalent strain cloud diagram.
Figure 8 shows that the maximum equivalent stress appears at the connecting
shaft of the boom in the model. The maximum equivalent stress is 2.28 MPa,
which is far below the material's yield limit of 235 MPa. The maximum
equivalent strain of the manipulator's forearm also appears near the
connecting axis of the arm, and the maximum deformation of the joint is
1.27×10-5 mm. Under the condition of its own weight and
external load, the manipulator forearm exhibits a stress concentration and
bending deformation under the limit lifting conditions. This phenomenon
makes the node mesh larger in terms of the deformation relative to the other
parts.
Modal analysis of the robotic arm
Using ANSYS Workbench to perform a modal analysis of the manipulator forearm model, the natural frequencies and corresponding modes of the palletizing
manipulator forearm were determined, and the first six modes of the
manipulator forearm for analysis were selected. Using the finite-element method to obtain the mode shapes of the forearm modes up to sixth order as shown in Fig. 9, the natural frequency and mode analysis was performed, with
the result shown in Table 3.
Analysis of the natural frequencies and mode shapes of the first
six modes.
OrderFrequencyVibration model(Hz)163.37Bend back and forth in the xoz plane2111.37Bend up and down in the xoy plane3402.7Bending and torsional deformation4465.66Bending and torsional deformation5585.03Bending and torsional deformation6650.51Bend up and down in the xoz plane
An examination of the results presented in Fig. 9 and Table 3 shows that the
fundamental frequency of the first-order mode is 63.37 Hz. As the mode order
number increases, the corresponding modal frequency also increases. The
frequency is in a higher frequency range, so that the manipulator forearm
has good rigidity and a large optimization space, avoiding resonance
phenomena.
Topology optimization design of the robot manipulator
The SIMP interpolation model of the variable density method combined with
ANSYS Workbench optimization software was used to optimize the structural
topology of the manipulator forearm.
Using the middle part of the manipulator arm as the optimization area, the
relative density of the structural unit is taken as the design variable, the
size and volume fraction of the manipulator arm below a certain value are
taken as the constraints, and the minimum flexibility is taken as the
optimization goal. Using the ANSYS Workbench for topology optimization, the
optimization constraint of 30 % is set for the topology optimization
model. The results are shown in Fig. 10. The dark area of the optimized area
represents a density value of 0 corresponding to the removed area, and the
non-dark area represents a density value of 1 corresponding to the reserved
area. The remainder of the colors are in between 0 and 1, and the proportion
of the total area occupied by these values is small.
Topology optimization cloud diagram.
Two forearm models based on a Rhino Grasshopper
For the topology optimization of the forearm of the manipulator, Solid Works
is first used to model the 3D structure of the forearm of the manipulator
and simplify the structural features, and then ANSYS Workbench is used for finite-element analysis, and the manipulator is verified by static analysis and modal analysis of the forearm of the manipulator. The optimized space of the forearm under external load is optimized by the continuous variable
density topology optimization method to obtain a preliminary topology
optimized structure model, and the Solid Works and Rhino design software is combined to create a secondary reconstruction model of the robot arm; then
ANSYS Workbench compares the excellent mechanical properties of the two
structural models and finally realizes the 3D printing of the two structures, providing a reference solution for the structural design and
manufacturing of the palletizing robot arm. A detailed block diagram of the
palletizing robot arm lightweight design is shown in Fig. 11.
Flowchart of robot arm optimization.
The topologically optimized structure obtained by the finite-element method is used as a reference, and the secondary reconstruction design of the model
is performed because the boundary of the structure is in a zigzag
checkerboard. This is also called the Voronoi diagram. According to previous
research, the hexagonal honeycomb structure has the characteristics of high
strength, low weight, heat dissipation, and energy absorption. Following the continuous development of the materials and processing methods of the honeycomb structure, it has been gradually applied to the fields of
lightweight structure design in aerospace and other industries. The Voronoi
structure is a special form of the honeycomb structure, and the Voronoi unit
is also a representative of the steady-state structure of the regular
hexagonal structure. Therefore, this study uses the Voronoi structural unit
to design the lightweight palletizing manipulator forearm.
Filled endoskeleton Voronoi structural design
The Rhino Grasshopper parametric modeling software is used to design the
internal honeycomb skeleton structure of the robot forearm. The specific
design steps are the following.
Step 1: import the original model of the forearm into the program box.
Step 2: fill the box with random points.
Step 3: generate the corresponding Tyson polygon at random points.
Step 4: enlarge the honeycomb structure, keeping the linear structure.
Step 5: delete redundant coincident line types.
Step 6: use the linear structure to generate a honeycomb tubular structure.
Step 7: cut off the allowance of the external honeycomb structure of the model.
Step 8: perform the Boolean operation between the model shell and Tyson polygon structure.
Step 9: convert format and generate entity.
The specific design process is shown in Fig. 12. The optimized arm model of the palletizing robot obtained using this process is shown in Fig. 13.
Voronoi structure design process in the filled bones.
Optimized forearm model: (a) arm model after filling; (b) overall
material removal model.
Design of the Voronoi structure with nested external removal
The realization of the Voronoi structure in the design area of the
manipulator forearm is performed by the Rhino Grasshopper module, and the
specific design steps are as follows.
Step 1: spread out the side surface of the model as a flat surface.
Step 2: fill in random points on the side surface after tiling.
Step 3: generate the corresponding plane Thiessen polygon structure from random points.
Step 4: enlarge the Tyson polygon structure, keeping the linear structure.
Step 5: delete redundant coincident line types.
Step 6: take back the flat side surface to the model surface.
Step 7: use the linear structure to generate a Tyson polygon tubular structure.
Step 8: perform the Boolean operation.
Step 9: convert the format and generate the entity.
The robot arm model of the random point of the Voronoi structure is
obtained, as shown in Fig. 14.
Voronoi structural design of nested external removal: (a) procedural process; (b) Voronoi diagram random points; (c) Voronoi diagram mechanical arm model.
Optimized finite-element analysisStructural performance analysis of the filled endoskeleton Voronoi structure
Static and modal analyses are performed on the optimized forearm model, and
a static analysis is performed on the optimized forearm, as shown in Fig. 15. Figure 15 shows that the maximum stress value of the optimized forearm is
8.66 MPa, which is far lower than the material yield strength of 235 MPa and
has a sufficient safety margin. The maximum displacement value is
4.33×10-5 mm, and the deformation is small. The requirements
for the mechanical properties of the robot arm are met. A comparison of the
performance characteristics before and after forearm optimization is shown
in Table 4.
Static cloud map after robot forearm optimization: (a) stress
cloud map after optimization; (b) model displacement cloud map after
optimization.
Performance comparison before and after forearm optimization.
An examination of the results presented in Table 4 shows that after the
optimization of the forearm, the maximum stress and the maximum displacement
have increased. The maximum stress has increased by approximately 5.08 MPa,
the maximum displacement has increased by approximately 2.41×10-5 mm, and the change is very small. The overall performance of the
forearm is basically unchanged. The total weight of the model of the forearm
is reduced from 221.940 to 173.892 kg, corresponding to a reduction of approximately 22 %, achieving the goal of weight reduction.
The same method as that of the original model is used to perform modal
analysis of the optimized bone-filled forearm. The resulting mode diagram is shown in Fig. 16. The results of the modal analysis of the forearm and the
vibration mode are shown in Table 5.
First six-order modes of the bone-filled forearm: (a) first-order
mode; (b) second-order mode; (c) third-order mode; (d) fourth-order mode; (e) fifth-order mode; (f) sixth-order mode.
Optimized first six modes of the bone-filled forearm.
OrderFrequencyVibration model(Hz)149.80Bend back and forth in the XOZ plane287.15Bend up and down in the XOY plane3326.16Bending and torsional deformation4348.5Bending and torsional deformation5510.3Bending and torsional deformation6561.42Bending and torsional deformation
An examination of the data presented in Table 5 shows that the first
six-order modal frequencies of the robot's forearm optimization are in the range of 49.801–561.42. Although the natural frequency of the
forearm optimization has been reduced to some degree, it is much higher than
the working vibration frequency of the palletizing robot at 15 Hz. Thus,
design of the arm can avoid the occurrence of resonance phenomena.
Performance analysis of the nested externally removal Voronoi structure
Using the same load and boundary conditions, the finite-element analysis of three Voronoi structure manipulator models at different random points after
optimization is performed. The static cloud diagram obtained by the analysis
is shown in Fig. 17. The data for the comparison of performance and quality
between the optimized model and the original manipulator's forearm are shown
in Table 6.
Static analysis of the optimized manipulator arm model: (a) random
point equivalent stress cloud diagram; (b) random point equivalent strain
cloud diagram.
Comparison of the structure and performance of the new manipulator
forearm model and the original model.
Table 6 shows that although the maximum stress and maximum strain of the
optimized manipulator arm are increased, the maximum stress is less than the
material's yield limit of 235 MPa, and the maximum strain is only
1.84×10-4 mm, which is within the allowable deformation range
of the manipulator arm. The quality of the optimized model is reduced by
approximately 22 % compared to the original model. From the above
analysis, it is observed that the optimized manipulator arm meets the design
requirements and achieves the purpose of a lightweight structure.
The modal analysis of the first six-order frequency is performed on the
nested externally removable manipulator using modal analysis. The resulting
mode diagram is shown in Fig. 18, and the specific mode data are shown in
Table 7.
First six-order modes of the nested external removal: (a) first-order mode; (b) second-order mode; (c) third-order mode; (d) fourth-order
mode; (e) fifth-order mode; (f) sixth-order mode.
Comparison of the performance of the two optimized arm models
A comparative performance analysis is carried out for the above two
optimized forearm models. For quality, the two optimized models show a
reduction by 22 % compared to the original model. Under this condition,
the performance of the optimized models is compared. A comparison of the
stress and strain of the two models shows that the maximum stress of the
bone-filled model is 7.36 MPa, the maximum stress of the nested external
removal model is 35.11 MPa, and the stress of the bone-filled model is lower
than that of the nested removal model. At the same time, for strain, the
maximum strain of the bone-filled model is 3.68×10-5 mm and the maximum strain of the nested external removal model is 1.84×10-4 mm, so that the strain of the bone-filled model is also lower
than that of the nested removal model. The stress and strain of both are
within the allowable range, as shown in Fig. 19.
Optimal nested-external-removal-type first six-order modes of the
small arm.
OrderFrequencyVibration model(Hz)126.90xoy in-plane bending228.842Bending deformation along the y axis3121.53Bending and twisting combination4195.48Bending and twisting combination5256.24Bend up and down in the xoz plane6294.16Bend deformation along the y axis
Comparison of the performance of the two optimized models: (a) quality comparison between the two optimized models and the original model;
(b) stress–strain comparison between the two optimized models.
The modal comparison and analysis of the two optimized models and the
original model shows that the first-order frequency of the bone-filled model
is 49.8 Hz, and the first-order frequency of the externally removed model is
29.8 Hz. Both are 15 Hz higher than the working frequency of the manipulator
arm, avoiding the occurrence of resonance. This is shown in Fig. 20.
Modal comparison between the two optimized models and the
original model.
Topology optimization components for additive manufacturing
For the manipulator arm designed in this paper, the traditional
manufacturing method for the fabrication of the porous structure is too complicated and cannot be carried out. In recent years, it was demonstrated
that the combination of topology optimization and additive manufacturing can
solve the processing and molding problems caused by topology optimization
results. To verify the feasibility of the use of additive manufacturing
technology to solve the problem of processing of complex structures after
the topological optimization design of the robotic arm, the FDM was used to 3D print the traction chassis model.
According to the results of the abovementioned model optimization analysis,
the optimized forearm model is saved in the STL format in Solid Works and
then imported into the Cura slicing software for layered slicing processing.
Next, the fusion deposition molding printer is used to realize 3D printing
of the palletizing robot forearm model, as shown in Fig. 21.
3D printed model of the robot's forearm; (a) 3D-printed model of
the filled-in skeletal Voronoi structure; (b) 3D-printed model of the Voronoi
structure with nested outer removal.
Conclusions
Taking the forearm of a number stacking robot as a research object, the analysis and design of the lightweight structure were carried out. Through
the structural analysis of the palletizing robot, a simplified model of the
manipulator forearm was created. The finite-element ANSYS Workbench software was used to perform static and modal analyses of the forearm under typical
working conditions. The model stress and strain were obtained. Based on the
cloud diagram of the topology optimization results, two different
lightweight models of the manipulator arm of the filled-in-skeleton and
nested-external-removal Voronoi structures were designed. The optimized
model of the manipulator arm with the nested-external-removal-type Voronoi
structure can be applied in the cases where the load weight ratio is high,
the structure is lightweight, the control circuit is complicated, and the
wiring must be routed inside the robot arm, while the bone-filled Voronoi
structure manipulator arm is more suitable for lightweight structures under
heavy loads and where external wiring is possible. The two optimized
structures proposed in this paper can meet most of the topological
optimization and lightweight requirements in the field of industrial robots and provide guidance for the structural optimization design and development
of industrial robots. The mechanical properties and quality of the structure
between the new model and the original model were compared. To ensure the
structural performance of the manipulator forearm limit, the structural
quality of the two optimized models was reduced from 222 to 173 kg,
reaching the manipulator forearm and achieving the goal of lightweight
design. Finally, 3D printing technology was used to realize the model
processing of the two structures. The use of 3D printing digital
manufacturing technology and computer-aided digital design technology
provides a set of feasible solutions for the personalized design and
manufacturing of the manipulator forearm.
Code availability
All the code used in this paper can be obtained upon request from the corresponding author.
Data availability
All the data used in this paper can be obtained upon request from the corresponding author.
Author contributions
JC established an overall paper research
framework, QC conducted detailed optimization and data experiments on the overall paper, and HY carried out paper revisions and financial support of the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by the National Natural Science Foundation of China
(NSFC grant no. 61303087) and the Major Scientific and Technological Innovation Projects (grant no. 2019JZZY010455). The authors thank a senior editor from American Journal Experts for linguistic advice.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant no. 61303087) and the Major Scientific and Technological Innovation Projects (grant no. 2019JZZY010455).
Review statement
This paper was edited by Jeong Hoon Ko and reviewed by Lin Lu and one anonymous referee.
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