1. Introduction
Casting is an important part of the equipment manufacturing industry to achieve significant development. According to the huge demand for casting in China’s equipment manufacturing industry, China’s total casting output in 2020 amounted to 51.95 million tons, with a year-on-year growth of 6.6%. Among them, the production of small- and medium-sized castings accounted for the largest proportion [1, Reference Liu, Zhao, Wang, Zhang, Ma, Gao, Li and Wu2]. In the entire production chain of small- and medium-sized castings, the most time-consuming and cumbersome link is sorting castings, whose labor demand accounts for more than 50% of the entire production process. For a long time, there have been problems such as high labor costs, high labor intensity, and low work efficiency.
With the development of artificial intelligence and robotics, the use of robots to sort small- and medium-sized castings has become a mainstream trend. As the end-effector of the sorting robot interacting with the castings, the gripper has an important impact on the robot’s work efficiency and casting damage rate. Therefore, the design of reasonable small- and medium-sized casting sorting robot grippers is a key issue that needs to be solved urgently.
At the present stage, the research on robot grippers is mainly categorized into traditional rigid manipulators, soft manipulators, variable stiffness soft manipulators, and rigid–flexible coupling grippers. The traditional rigid manipulator has the advantages of large load capacity, high control accuracy, etc., and it can replace human beings to complete some dangerous and heavy work [Reference Sivčev, Coleman, Omerdić, Dooly and Toal3–Reference Meng, He and Han5]. However, due to its own hardness and poor toughness, it is difficult to grasp some objects with variable shapes, and it is easy to damage the surface of the object. It is also possible to injure humans as a result of collisions during human–machine interactions [Reference Nasab, Sabzehzar, Tatari, Majidi and Shan6].
In order to solve the drawbacks of traditional rigid manipulators, some scholars began to study soft manipulators with high adaptability and flexibility [Reference Ahmed, Waqas, Jawed, Soomro, Kumar, Hina, Khan, Kim and Choi7, Reference Chen, Zhang, Huang, Cao and Liu8]. For example, Jiang et al. [Reference Jiang and Xu9] proposed a new type of pneumatic soft manipulator to grasp-shaped objects with a diameter within 200 mm with three soft fingers, which has a maximum load of only 480 g. Zhu et al. [Reference Zhu, Feng, Hua, Wang, Hu, Wang and Su10] proposed a pneumatic gripper with four soft fingers, with the maximum bending angle of a single soft finger up to 110 ° and a maximum load of about 5.8 N. However, it is not suitable for grasping flat-shaped objects. Meng et al. [Reference Meng, Xu, Li, Zhang and Xu11] designed a soft manipulator structure modeled after biological cells that has good ductility, but gravity affects the performance of the soft manipulator and lacks autonomous operation. Most of the scholars proposed a soft robot; although it is more adaptive and flexible than the traditional rigid robot, its load capacity and control accuracy are still far from enough [Reference Dou, Zhong, Cao, Shi, Peng and Jiang12]. In order to further improve the load capacity and control accuracy of the soft manipulator, some scholars have also proposed the variable stiffness soft manipulator [Reference Yoon13]. For example, Ren et al. [Reference Ren, Li, Liu, Chen, Yang, Yuan, Li and Yang14] used a particle interference method to realize variable stiffness control of the manipulator when grasping, and the maximum load of the manipulator was equal to its own mass. Liu et al. [Reference Liu, Jing, Huang, Dun, Qiao, Leung and Chen15] proposed a flexible robot manipulator based on shape memory alloy, but the end part was a two-finger gripping claw, which had a limited shape of the objects that could be grasped. Lloyd et al. [Reference Lloyd, Thomas, Venkiteswaran, Pittiglio, Chandler, Valdastri and Misra16] proposed a variable stiffness magnetically driven coiling soft robot with a maximum coiling deformation angle of 400°, which is suitable for grasping tiny objects in narrow spaces. Yang et al. [Reference Yang, Zhu, Liu, Wei, Li and Zhou17] proposed a variable-strength soft robot finger based on thermal polymer, which can be assembled into a two-finger gripping jaw and heated in segments by a polyimide diaphragm, but the grasping process is not stable. Variable stiffness control improves the load capacity and control accuracy of soft robot fingers to a certain extent, but compared with rigid robot fingers, variable stiffness soft robot fingers still cannot meet the demand for load capacity in some industrial production processes, and the response of variable stiffness control takes longer.
In order to develop grippers with both excellent adaptability and load capacity, some scholars have proposed flexible manipulators with rigid–flexible coupling structures. For example, Ji et al. [Reference Ji, Lan, Nie, Huo, Yin and Hong18] designed an anthropomorphic soft manipulator driven by water hydraulics, and the manipulator wrist is a rigid–flexible coupled structure consisting of three bellows and a spindle, which is mainly used for underwater operation. Su et al. [Reference Su, Wang, Lu and Wang19] proposed a seven-degree-of-freedom manipulator with rigid–flexible coupling fingers, with flexible bands on the inside of the fingers and rigid structures on the outside, and the whole is driven by four servomotors. Chen et al. [Reference Chen, Sun, Wang, Chen, Xu, Ni, Ramli, Su and Chu20] proposed a pneumatic bionic hand with a rigid-flexibly coupled structure. The bionic hand is designed based on the fast pneumatic network method and can only grasp some light mass items. Zheng et al. [Reference Zheng, Pi, Guo, Xu, Liu and Kong21] designed a rigid-flexibly coupled gripper for picking clustered tomatoes, which can smoothly grasp tomatoes with a maximum diameter of 95 mm. Zhang et al. [Reference Zhang, Zhang, Ning, Zhou, Wu, Zhao, Li and Liu22] developed a single-degree-of-freedom six-bar spatial gripper, which is mainly used for grasping cylinders with different diameters and has good force adaptability. Wen et al. [Reference Wen, He and Gao23] proposed a multipedal robot with a rigid-flexibly coupled fixture for asteroid detection in microgravity. Ouyang et al. [Reference Ouyang, Guan, Yu, Yang, Cheng, Chen, Zhao, Zhang and Guo24] proposed a dielectric elastomer actuator-based rigid-flexibly coupled soft gripper optimization design method that can improve the stiffness of the soft fingers with a maximum bending angle of 19.66° and a maximum grasping mass of 11.21 g for a three-finger soft claw. He et al. [Reference He, Lian and Song25] proposed an underdriven rigid–flexible coupled robotic fixture that can adapt to objects with different attributes and stabilize grasping. Li et al. [Reference Li, Li, Wang, Shang and Yao26] proposed a soft-swallowing robot with a continuous grasping mode and a maximum tolerable percentage of structural failure of 50%, which has the advantages of high efficiency and adaptability. Phodapol et al. [Reference Phodapol, Harnkhamen, Asawalertsak, Gorb and Manoonpong27] proposed a flexible gripper based on the tarsal morphology of a bumblebee, and the proposed gripper has a higher success rate in grasping objects compared to industrial soft and rigid grippers. Ansary et al. [Reference Ansary, Deb and Deb28] designed an adaptive robotic gripper to grasp objects by wrapping two fingers. The gripper is mainly suitable for gripping lightweight objects and not for industrial gripping. Jiang et al. [Reference Jiang and Zhang29] investigated a soft-rigid hybrid pneumatic actuator consisting of a rigid-foldable twisting skeleton and a soft bellows muscle. The actuator is suitable for working in confined spaces. Wang et al. [Reference Wang, Fang, Zhang and Yang30] proposed a novel 4-DOF two-limb gripper mechanism. This new gripping mechanism is more suitable for gripping different types of parts in large workspaces due to the special features of its drive system.
So far, robotic grippers with rigid–flexible coupling structures have been operating in minimally invasive surgery, agricultural harvesting, underwater sampling, logistics, and transportation, but few researchers have applied them to the green casting industry. However, in the actual production process of small- and medium-sized castings, the shape of the casting is often irregular, with more complex structures [Reference Raha31–Reference Liu, Shan, Liu and Lan34]. When performing the gripping task, the existing gripper is limited by the low adaptability of the rigid gripper and the low load capacity of the soft gripper, and it is difficult to meet various needs at the same time. In order to continue to develop the application potential of rigid–flexible coupling manipulators, this paper proposes a casting sorting robot gripper with rigid–flexible coupling structures. The structure of the gripper is designed based on the robot topology theory and the organizational structure of the human hand, and the main purpose is to integrate the rigid structure and the flexible structure in the same manipulator system so that they can give full play to their respective advantages and realize the complementary advantages of rigidity and flexibility.
In this paper, a small- and medium-sized casting sorting robot gripper is proposed that is characterized by a compact structure, flexible gripping, safety, and reliability. Combined with the proposed design scheme, research on structural design, performance simulation, prototype preparation, control system development, and experimental verification of the gripper is carried out. The main use of 3D rapid prototyping technology, forging technology, and welding technology, with the help of ANSYS and ADAMS software, to do the static mechanics and dynamics of the simulation test, the use of the dual-control system method, and the control of the human–machine interface with the help of LabView, to complete the bending performance of comparative experiments and adaptive gripping experiments. This study can provide a reference for the design and optimization of various types of shaped workpiece clamping manipulators.
2. Structural design of gripper for casting sorting robot
The human hand is a natural rigid–flexible coupling model, and the flexible collaboration of the bones and tendons of the hand ensures good fit and stable grasping of complex-shaped objects within a certain weight range [Reference Molnar, Esteve-Altava, Rolian and Diogo35, Reference Olikkal, Pei, Adali, Banerjee and Vinjamuri36]. Facing the problem of the insufficient adaptability of existing small- and medium-sized casting sorting robot grippers, based on the theory of robot topology and the organizational architecture of the human hand, a casting sorting robot gripper with rigid–flexible coupling structures is designed.
The casting sorting robot gripper contains four major parts: a support body, four active support chains, four follower support chains, and two self-adaptive clamping belts (Fig. 1(a)). The entire system picks up or releases the casting via an adaptive clamping belt driven by four active support chains, which are driven by double-acting cylinders or hydraulic cylinders. The scale constraints of the mechanism of the active support chain can realize the topology of R(⊥P)⊥R⊥R, and the scale constraints of the mechanism of the follower support chain can realize the topology of R
$\,/\!/\,$
R(⊥R)
$\,/\!/$
R. The whole gripper system can be regarded as a parallel mechanism with three degrees of freedom of movement in space, two translations, and one rotation (Fig. 1(b)). Sufficient degrees of freedom enable the adaptive clamping belt to adhere well to the surface of the casting to be grasped. The contact layer of the adaptive clamping belt is a flexible protective sleeve made of super-elastic material, which allows the gripper to adapt to the wrapping and gripping of various irregular castings while avoiding damage to the castings during the grasping process. We adopt the structural design of rigid master and soft slave, in which the active branch chain and the driven branch chain belong to the rigid structure and the adaptive clamping belt belongs to the flexible structure, so that the respective advantages of the rigid structure and the flexible structure can be brought into play in the same manipulator system, such as the strength of the active branch chain and the flexibility of the adaptive clamping belt, and the complementary advantages are realized.
A robotic gripper for casting sorting robot with rigid–flexible coupling structure. (a) Structural diagram, (b) topology schematic 1.
1Note: R1 denotes the rotating pair between the active support chain and the support body; R2 denotes the rotating pair between the follower support chain and the support body. R3 denotes the rotating pair between the upper link and the lower link in the follower support chain; R4 denotes the rotating pair between the lower link in the follower support chain and the inner chain plate lugs. R5 denotes the rotating pair between the inner chain plate lugs and the adaptive clamping belt; R6 denotes the rotating pair between the active support chain and the outer chain plate lugs; R7 denotes the rotating pair between the outer chain plate lugs and the adaptive clamping belt; and P1 denotes the moving pair between the piston of the clamping cylinder of the active support chain and its sleeve.
The grasping flow diagram of the casting sorting robot gripper is shown in Fig. 2. First, before grabbing, select the clamping cylinder of suitable working stroke and the adaptive clamping belt of suitable working length according to the needs of grasping operation; second, after the working parameters are adjusted, the connecting flange of the gripper is fixedly installed on the end flange of the casting sorting robot; third, when performing the grasping task, the two adaptive clamping belts are aligned with the clamping parts of the grabbed castings, so that the middle lower side of the flexible protective sleeve in the adaptive clamping belts is attached to the outer surface of the grabbed castings; Fourth, the clamping cylinder in the active branch chain is activated, the clamping cylinder is elongated, and the driving adaptive clamping belt is tightly wrapped by the grabbing casting. Finally, when it is time to release the gripped casting, the clamping cylinder is shortened, and the adaptive clamping belt is driven away from the casting. Its working principle adopts the rigid-master–flexible-slave control strategy in the rigid–flexible coupling structure, which mainly drives the adaptive clamping belt through the active branch chain and completes the adaptive wrapping and grabbing of special-shaped castings within a certain size and weight range with the cooperation of the driven branch chain.
Casting sorting robot gripper grasping flow chart.
The adaptive clamping belt consists of two parts: the inner part is a flat metal clamping layer made of multiple rows of short-pitch precision roller chains, and the outer contact layer is a flexible protective sleeve made of super-elastic material (Fig. 3). In order to tightly wrap the castings, the left and right ends of the adaptive clamping belt with anti-dislodgement hook claws in the corners connected with the follower chain are installed with limit pipe joints. The adaptive clamping belt on the inner side of the flexible protective sleeve contact with the surface layer of the airbag is provided underneath the belt. In the adaptive clamping belt on the casting, to complete the initial package, according to the needs of the airbag to inflate, make it expand the extruding casting, and ensure the reliability of the adaptive clamping belt wrapping the casting in the gripping process.
Schematic diagram of the structure of the adaptive clamping belt. (a) Flexible protective sleeve, (b) metal clamping layer.
Adaptive clamping belt on the inside of the flexible protective sleeve contact surface layer below the airbag made of the silicone rubber materials-60 degree. The choice of 60 degrees of silicone material is based on its aging resistance, high elasticity, and other characteristics of the material performance parameters shown in Table I.
3. Mechanical modeling and simulation analysis of casting sorting robot gripper
3.1. Static analysis of adaptive clamping belts
A second-order Yeoh model is based on the following assumptions: 1) the silicone rubber material is homogeneous and incompressible and 2) the flattened metal clamping layer is not extensible.
The second-order strain energy density function for the Yeoh model is defined by
\begin{equation}W=C_{10}\left(I_{1}-3\right)+C_{20}({I_{1}}-3)^{2}\end{equation}
where W is the strain energy, I 1 is the first invariant of the deformation tensor, and C 10 and C 20 are the parameters of the second-order Yeoh model.
According to the isotropic assumption of rubber theory, the strain energy density function can be used to describe its mechanical properties. The relationship between the strain energy density function W and the three invariants of the deformation tensors I 1, I 2, and I 3 is
\begin{equation}W=W(I_{1},I_{2},I_{3})\end{equation}
The relationship between the three invariants of the deformation tensors I 1, I 2, and I 3 and the elongation ratios λ 1, λ 2, and λ 3 in the three directions of the airbag is
\begin{equation}I_{1}={\lambda _{1}}^{2}+{\lambda _{2}}^{2}+{\lambda _{3}}^{2}\end{equation}
\begin{equation}I_{2}={\lambda _{1}}^{2}\cdot {\lambda _{2}}^{2}+{\lambda _{1}}^{2}\cdot {\lambda _{3}}^{2}+{\lambda _{2}}^{2}\cdot {\lambda _{3}}^{2}\end{equation}
\begin{equation}I_{3}={\lambda _{1}}^{2}\cdot {\lambda _{2}}^{2}\cdot {\lambda _{3}}^{2}=1\end{equation}
Mechanical properties of the silicone rubber materials-60 degree.
In the uniaxial tensile test, assuming that the one direction is the main tensile direction,
$\lambda _{2}=\lambda _{3}$
and
$\lambda _{1}=\lambda$
. Substitution formula (5):
\begin{equation}\lambda _{1}={\lambda _{2}}^{-2}={\lambda _{3}}^{-2}=\lambda\end{equation}
A total of eight sets of uniaxial tensile experiments were performed on the silicone rubber materials-60 degree, and the resulting experimental data were fitted to the data fitting function based on nonlinear least squares fitting. Each sample was tested eight times, and the average value was taken, so C 10 = 0.05 MPa and C 20 = 0.01 MPa were obtained.
The initial state of the adaptive clamping belt can be simplified into a set of linearly arranged independent meshes (Fig. 4(a)). In order to control the bending rate during airbag inflation, it is necessary to build a set of bending models with independent meshes to obtain the equation of bending angle versus input air pressure.
Schematic diagram of the deformation of a set of independent meshes. (a) Initial status, (b) after inflation.
During the inflation process, according to the structure of the adaptive clamping belt, the drive layer will deform unidirectionally under the constraint of the limiting layer, and the final outer wall of the front end of the drive layer can be viewed as a circular arc with radius r (Fig. 4(b)). The deformation of a single grid angle is
$\alpha$
. The whole group has a total of i independent grids, and assuming that the deformation of each independent grid angle is equal, the deformation of the whole group of grid angles is
$=i\cdot \alpha$
. The angle of the center of the circle after bending the driving layer is
$\beta$
. By simulating the deformation of a set of independent meshes and averaging it over several simulations in the ANSYS software,
$\beta =4\alpha$
is obtained.
The midpoint of the front outer wall of the independent grid of the drive layer is used as the coordinate origin to establish a right-angled coordinate system (Fig. 4(b)), and the coordinates of the circular arc of the front outer wall of the inflated drive layer can be expressed as
\begin{equation}x^{2}+(y-{y_{0}})^{2}=r^{2}\!\left(-\frac{d}{2}\leq x\leq \frac{d}{2},y\lt 0\right)\end{equation}
where the variables x and y represent the horizontal and vertical coordinates, respectively, of the point on the arc of the outer wall of the front end of the drive layer after inflation of the drive layer,
$r=d/[2\sin (\beta /2)]$
and
$y_{0}=d/[2\tan (\beta /2)]$
.
In the initial state, the thickness of the driving layer is H, and the thickness of the connecting layer is h. During the inflation process, the drive layer expands, resulting in the connecting layer being gradually stretched and thinned. According to the Boltzmann distribution law and the structure of the adaptive clamping belt, the force distribution of the driving layer and the connecting layer is uniform and equal in size, and the main elongation λ 1 is related to the centroid angle
$\beta$
after bending of the driving layer by the equation (8):
\begin{equation}\lambda _{1}=\frac{\beta }{2\sin \left(\frac{\beta }{2}\right)-2\beta \cdot \sin (\frac{h}{2})}\end{equation}
The initial length of the driving layer is L, and the length d after deformation can be expressed as
\begin{equation}d=2\!\left[2\lambda _{1}\cdot \left(2h+H\right)\cdot \sin \left(\frac{h}{2}\right)+4\right]\end{equation}
Assuming that the deformation of the outer wall of the drive layer also increases linearly to y(x) during the increase in air pressure from 0 to p, the work acting on the outer wall can be expressed as
\begin{equation}U\left(p,\beta \right)=\int _{0}^{d}p\cdot y\left(x\right)dx\end{equation}
Therefore, the strain energy density function can be expressed as
\begin{equation}W\left(\beta \right)=C_{10}\!\left(\lambda ^{2}+\frac{1}{\lambda ^{2}}-2\right)+C_{20}\!\left(\lambda ^{2}+\frac{1}{\lambda ^{2}}-2\right)\end{equation}
The deformation energy after deformation of the driving layer can be expressed as
\begin{equation}V\left(\beta \right)=H\cdot L\cdot W(\beta )\end{equation}
According to the law of conservation of energy, the work acting on the outer wall of the drive layer is equal to the deformation energy of the drive layer after deformation; therefore:
\begin{equation}U\left(p,\beta \right)=V(\beta )\end{equation}
The relationship between p and
$\alpha$
can be introduced through the relationship between p and
$\beta$
. By associating equations (7)–(13), the relationship equation between the whole set of mesh bending angles θ and the input air pressure p can be obtained as follows:
\begin{equation}\theta =i\cdot \alpha =i\cdot f(p)\end{equation}
According to the established mathematical model, an independent mesh bending trend is obtained (Fig. 5). When the input air pressure reaches 70 kPa, the bending angle is about 56°, and the subsequent growth of the bending angle is gradually flattened with the increase in air pressure.
Variations of the drive bending angle with the pneumatic pressure.
3.2. Finite element simulation analysis of the drive layer
In order to verify the feasibility of multidirectional and multiangle bending of the adaptive clamping belt, simulation analysis was conducted on the driving layer using ANSYS finite element software. The Yeoh model of second-order hyperelastic material is selected; set C 10 = 0.05 MPa and C 20 = 0.01 MPa; tetrahedral mesh division is used; boundary conditions are set as fixed constraints; and air pressure perpendicular to the surface of the internal chambers of the drive layer is applied without taking into account the role of compressed air at the exit of the drive layer. Without considering gravity and friction, the bottom surface of the airbag is fixed to the flat metal clamping layer by means of a flexible protective sleeve. As shown in Fig. 6, the deformation of the independent grids of the drive layers in the adaptive clamping belt under different input air pressures is shown, divided into 8 levels, and the input air pressure difference of 10 kPa in each stage is shown. As the input air pressure increases, so does the bending angle of the independent mesh of the drive layer. In the process of increasing the input air pressure, the bending angle change rate of the independent grid is getting faster and faster, but when the input air pressure exceeds 40 kPa, the bending angle change rate of the independent grid gradually slows down. When the input air pressure reaches 70 kPa, the independent grid bending angle of the driving layer is 52° under the constraint of the limit layer. The input air pressure continues to increase, but the bending angle of the independent mesh hardly changes. From the input air pressure of different levels, it can be seen that the inner side of the independent mesh is more stressed than the outer side, and the outer side of the independent mesh is more deformed than the inner side (the inner side of the independent mesh is the side close to the limit layer, and the outer side of the independent mesh is the side close to the connecting layer), which verifies the accuracy of the mathematical model built in Section 3.1.
Simulation results of the driver layer.
3.2. Dynamic simulation analysis of gripper for casting sorting robot
In order to more clearly reflect the dynamic characteristics of the rigid–flexible coupling model of the casting sorting robot gripper. It is necessary to carry out mesh delineation of the adaptive clamping belt by the ANSYS software and then import the corresponding rigid components into the ADAMS software to replace them, so as to establish the rigid–flexible coupling model of the casting sorting robot gripper (Fig. 7).
Rigid–flexible coupling dynamics model of the gripper of casting sorting robot.
Due to the complexity of the gripper structure of the casting sorting robot, in order to more accurately reflect the movement status of the gripper during the working process, it is necessary to simplify and process the gripper model appropriately. First of all, the parts that do not participate in the transmission inside the gripper are boolean operations. Second, the dimensional tolerances and assembly errors of the model are not considered. Third, in the rigid–flexible coupling model of the gripper, except for the adaptive clamping band that was flexibly processed by ANSYS, the rest of the components were treated as rigid bodies.
In order to test the maximum diameter of the gripper to cope with the castings with complex shapes, the current motion simulation of the gripper is now divided into two main working conditions for research. First of all, the materials of each component of the gripper are defined, in which the active and driven support chains are made of aluminum alloy, and the support chain is made of steel. The mechanical properties are illustrated in Table II. Secondl, the casting sorting robot gripper virtual prototype components add a fixed pair between the support of the motion pair added to the clamping virtual prototype component and the earth, the support and the active support chain in the clamping cylinder, the support and the follower chain of the upper linkage to add the rotation of the vice, the active support chain in the clamping cylinder and the piston rod to add the moving vice between the piston rod and the drive, and this moving vice to add. The driving speed of the piston rod is set to 6 mm/s, the simulation time is 8 s, and the simulation step is 0.001 s.
Simulation condition 1: The gripper carries out adaptive wrapping and gripping operations on a rigid casting with a rectangular body at one end and a hexagonal steel at the other end. The state of the simulation process is shown in Fig. 8.
Rigid–flexible coupling dynamics model of the gripper of casting sorting robot under working condition 1.
Simulation condition 2: The gripper carries out adaptive wrapping and gripping operations on rigid castings that are cylinders at one end and circular table bodies at the other end. The state of the simulation process is shown in Fig. 9.
Rigid–flexible coupling dynamics model of the gripper of the casting sorting robot under working condition 2.
According to the ADAMS simulation results, the maximum diameter of the casting sorting robot gripper for stable gripping is 140 mm.
4. Castings sorting robot gripper prototype construction and system design
A prototype of a casting sorting robot gripper was built by combining forging and welding technologies according to the gripper structure design scheme described in Section 2. The prototype support is made of forged 10 mm steel plate, the clamping cylinder in the active support chain is an aluminum alloy cylinder of model MAL16*50, and the flat metal clamping layer in the adaptive clamping belt is a precision roller chain of model 04C-2*18. In order to improve the surface friction of the grippers, rubber pads with a thickness of 1 mm were pasted on each of the end grippers.
The fabrication process for the adaptive clamping belt is as follows: 1) preparation of a flat metal clamping layer (limiting layer); 2) installation of an airbag (driving layer); 3) covering of a flexible protective sleeve (connecting layer) (Fig. 10). The production process of the airbag is as follows: silicone mixing, resting, pouring, demolding, and sealing, where the molds used are generated using 3D printing technology.
Adaptive clamping belt structure model.
In order to test the adaptive gripping ability of the casting sorting robot gripper, a test platform as shown in Fig. 11 is constructed, and the hardware system of the platform mainly includes the casting sorting robot gripper module, computer, MCU control module, solenoid valves, relays, miniature air pumps, pneumatic pressure sensors, speed controllers, silicone hoses, etc., and the control system is mainly divided into the human–machine interaction interface and the microcontroller control program.
Castings sorting robot gripper test platform.
The human–machine interface was controlled by LabView programming, as shown in Fig. 12. According to the gripping process of the casting sorting robot gripper, the manual control of the clamping cylinder of the active pivot chain and the drive layer of the adaptive clamping belt are designed so that they can independently control the air pressure load in each air cavity and coordinate with each other to complete the multidirectional and multiangle bending of the adaptive clamping belt.
Mechanical properties of the virtual prototype of the casting sorting robot gripper components.
Human–computer interaction interface.
It is sent to the microcontroller control module through a computer. The microcontroller control module sends control commands to the governor after analysis. The governor can adjust the flow rate of the micro-air pump according to the instructions. The gas is transported from the micro-air pump through a silicone hose through an electromagnetic valve to the gripper module of the casting sorting machine. During the movement of the gripper, the air pressure sensor can monitor the pressure in the air cavity in real time and transmit the pressure signal to the microcontroller control module, which in turn controls whether the relay is powered or not and then controls the electromagnetic valve through the relay to realize various gripping positions of the gripper.
5. Experimental analysis
5.1. Bending performance experiments of adaptive clamping belt
Some of the states of the casting sorting robot gripper during the gripping motion are shown in Fig. 13. Combined with the designed pneumatic control system, different external pneumatic loads can be applied to each clamping cylinder, resulting in multiangle bending of the adaptive clamping belt as a whole and realizing the diversity of wrapping postures. Each adaptive clamping belt can be controlled independently during the experiment. Gripping object diversity is achieved by applying different external air pressure loads to each airbag in the drive layer of the adaptive clamping belt.
Adaptive clamping belt bending performance test. (a) Single adaptive clamping belt bending test, (b) adaptive clamping belt bending experiment under no-load condition.
5.2. Comparative performance experiments of casting sorting robot grippers
The designed casting sorting robot gripper, in the process of gripping the workpiece, first drives the adaptive clamping belt through the active support chain to complete the preliminary wrapping of the gripped workpiece and then inflates the airbag of the driving layer to make the adaptive clamping belt tightly squeeze and wrap the workpiece under the constraints of the limiting layer. For the existing rigid mechanical gripper, due to its lack of adaptability, it usually cannot smoothly grasp shaped workpieces, and it is easy to damage the surface of the workpieces during the gripping process. The soft gripper is usually used for gripping lightweight objects, and its load capacity is low.
As shown in Fig. 14, the designed casting sorting robot gripper and rigid mechanical gripper are used to carry out grasping test comparison experiments. During the experimental process, the designed casting sorting robot gripper successfully grasps the caliper bracket casting in 1s (Fig. 14(a)), while the rigid mechanical gripper slips after grasping for 0.4 s and is unable to smoothly grasp the caliper bracket casting (Fig. 14(b)). For the soft mechanical gripper, although it can complete the wrapping of the caliper bracket castings, it cannot bear the weight of the caliper bracket castings, which makes the gripper excessively deformed.
Comparative experiments of casting grasping. (a) The designed casting sorting robot gripper gripping the caliper bracket casting, (b) rigid manipulator gripping the caliper bracket casting.
By analyzing the common small- and medium-sized castings, we selected six types of workpieces with a mass range of 100–1000 g to replace the castings for the gripping experiment. As shown in Fig. 15, the selected workpieces are closer to the small- and medium-sized castings in terms of their hardness, surface roughness, and other physical properties. By using the designed casting sorting robot gripper, rigid mechanical gripper, and soft body manipulator for each of the six types of workpieces, 10 gripping tests were conducted, and the results are shown in Fig. 16. The designed casting sorting robot gripper can successfully complete the gripping test of six types of workpieces, and the comprehensive success rate is as high as 96.4%. The gripping success rate is much higher than that of the same specifications of the rigid mechanical gripper and the soft robotic hand. To a certain extent, it solves the existing small- and medium-sized castings sorting robot gripper self-adaptive insufficient problem, but also for all kinds of shaped workpiece clamping robot design and optimization provides a reference.
Gripped workpieces. (a) Cylinder, (b) cuboid, (c) sphere, (d) bracket, (e) plier, (f) cone, (g) pipe joint, (h) shovel, (i) hexagonal steel, (j) pipe wrench.
Number of successful grabs for each gripper.
5.3. Adaptation experiment of casting sorting robot gripper
In order to verify the adaptability and load capacity of the gripper of the casting sorting robot, we carried out adaptive experiments by gripping the workpieces shown in Fig. 15 in turn, and the parameters of the workpieces are shown in Table III. In the process of gripping the workpiece, first increase the pressure of the clamping cylinder in the active support chain, and after the adaptive clamping belt has completed the initial wrapping of the workpiece, inflate the airbag of the adaptive clamping belt to tightly squeeze and wrap the gripped workpiece. As shown in Fig. 17, if the workpiece does not fall during the gripping process, the gripping is considered successful.
Parameters of the gripped workpieces.
2Note: The wrapping angle refers to the central angle between the adaptive clamping belt and the contact arc of the workpieces when the casting sorting robot gripper completes the adaptive wrapping and stabilizes the clamping of the workpiece.
Gripper for gripping the workpieces. (a) Gripping a cylinder, (b) gripping a cuboid, (c) gripping a sphere, (d) gripping a bracket, (e) gripping a plier, (f) gripping a cone, (g) gripping a pipe joint, (h) gripping a shovel, (i) gripping a hexagonal steel, (j) gripping a pipe wrench.
The gripper has a good fit and stable gripping of all kinds of small- and medium-sized workpieces, as shown in Fig. 15, which proves that the shape of the gripped workpieces will not limit the gripping ability of the gripper. The maximum load capacity of the gripper was experimentally measured to be 930 g, and the maximum wrap angle was 296°, and it still has some upward space.
5.4. Characteristics of the designed gripper for casting sorting robots
In the actual production process of small- and medium-sized castings, the shape of the casting is often irregular, with more complex structures. Existing grippers are limited by the low adaptability of rigid grippers and the low load capacity of soft grippers when performing gripping tasks, which makes it difficult to meet various demands at the same time. In order to solve the problem of the insufficient adaptability of existing small- and medium-sized casting sorting robot grippers, we designed a casting sorting robot gripper with a rigid–flexible coupling structure. The mechanical design of the gripper imitates the gripping posture of the human hand; with its compact structure and flexible gripping, it can be applied to wrapping and gripping all kinds of small- and medium-sized castings. The gripper adopts the pneumatic dual-control system method to ensure that the casting is firmly clamped without damaging the casting, which improves the working efficiency of the gripper. Compared with existing rigid manipulators, soft manipulators, and variable stiffness manipulators, the casting sorting robot gripper we designed can realize high-efficiency and high-performance adaptive gripping with the stability of a rigid manipulator and the suppleness of a soft manipulator, while ensuring that the weight of the casting is less than 930 g and the diameter is less than 140 mm. In order to ensure gripping reliability, the designed gripper is not suitable for gripping castings with a thickness of less than 15 mm or a length of less than 100 mm, as this would result in the adaptive clamping belt not being able to fit snugly around the gripped casting.
In order to more intuitively represent the characteristics of the casting sorting robot gripper, we compared the designed casting sorting robot gripper with existing similar designs, as shown in Table IV.
Comparative analysis of the performance of similar grippers.
6. Conclusions
In this paper, for the existing small- and medium-sized casting sorting robot gripper with insufficient self-adaptability, a casting sorting robot gripper with a rigid–flexible coupling structure is proposed.
(1) Based on robotic topological structure theory and human-handed organizational architecture, a casting sorting robot gripper with rigid–flexible coupling structure was designed, and the gripper adopted the control strategy of rigid-master–soft-slave, which was manifested in the active branch chain driving the adaptive clamping belt and realizing the grasping and sorting of castings with the cooperation of the driven branch chain.
(2) The second-order Yeoh model was used to establish the static mathematical model of the adaptive clamping belt, and the relationship between the bending angle of the entire grid and the input air pressure was obtained. Through ANSYS software simulation and analysis, the bending changes of the adaptive clamping belt driving layer in the process of input air pressure were obtained. Dynamic simulation of the gripper was simulated by ADAMS software, and the maximum gripping diameter of the gripper was obtained as 140 mm.
(3) A casting sorting robot gripper prototype is developed, the pneumatic control system of the gripper is designed, and the human–machine interaction interface is developed through LabView programming. Comparative experiments on the bending performance of the adaptive clamping belt were conducted to verify the accuracy of the hydrostatic model and simulation analysis. Finally, after several experimental analyses, the designed casting sorting robot gripper is characterized by strong adaptability and high robustness, with a maximum load capacity of 930 g and a maximum wrap angle of 296°, which can complete the gripping operation within 1s, and the comprehensive gripping success rate reaches 96.4%.
In the future, we will continue to optimize the structure of the casting sorting robot gripper to further improve its load capacity under the premise of ensuring safety and reliability. With the help of computer vision and other technologies, intelligent sorting can be achieved, and sorting efficiency can be improved.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S0263574724001000
Author contributions
The authors have worked together to complete this research. All authors contributed to the study’s conception and design. Biao Cheng performed research, analyzed the data and was involved in writing the manuscript. Chengjun Wang collected, analyzed, and interpreted the data from different research articles to formulate a summary and to create a roadmap for future researchers. Material preparation was performed by Chengjun Wang and Biao Cheng.
Financial support
The research described in this paper was financially supported by the Natural Science Foundation of Anhui Province (Grant No. 2208085ME128).
Competing interests
The authors declare no conflicts of interest exist.
Ethical approval
None.
