A practical approach for automated polishing system of free-form surface path generation based on industrial arm robot Ahmed Kharidege, Du Ting Ting & Zhang Yajun
The International Journal of Advanced Manufacturing Technology ISSN 0268-3768 Volume 93 Combined 9-12 Int J Adv Manuf Technol (2017) 93:3921-3934 DOI 10.1007/s00170-017-0726-y
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Author's personal copy Int J Adv Manuf Technol (2017) 93:3921–3934 DOI 10.1007/s00170-017-0726-y
ORIGINAL ARTICLE
A practical approach for automated polishing system of free-form surface path generation based on industrial arm robot Ahmed Kharidege 1 & Du Ting Ting 1 & Zhang Yajun 1
Received: 16 March 2017 / Accepted: 26 June 2017 / Published online: 31 July 2017 # Springer-Verlag London Ltd. 2017
Abstract In manufacturing environment where a robot arm is programmed to follow a specified path such as in polishing, the geometric coordinate transformation of an automated polishing system frames is needed for polishing workpiece surface. As the presence of kinematic singularities can extremely affect the robot’s performance, singularity regions in the task space for robot are clearly identified using determinant of the robot’s Jacobian matrix. Based on the free-form surface polishing requirements, programing an industrial robot with Teach Pendant manually is skill dependent and time consuming. Therefore, a scheme of an automatic polishing system is proposed, which includes a 6DOF arm robot. An automatic planning and programming system based on data from a CAD system is described to create robot paths. The robot program which contains the polishing path is generated in certain order using RoboGuide software in virtual environment. A Human Machine Interface has been used to control the entire polishing system online. Finally, the generated path is successfully applied in robotic polishing system. The experimental results prove that the proposed method is effective and
Electronic supplementary material The online version of this article (doi:10.1007/s00170-017-0726-y) contains supplementary material, which is available to authorized users. * Ahmed Kharidege
[email protected] Du Ting Ting
[email protected] Zhang Yajun
[email protected] 1
College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
feasible. The outcomes of this work contribute to enhancing the use of arm robot for polish free-form workpiece surfaces. Keywords Industrial robot . Automated polishing system . Path planning . Singularity analysis
1 Introduction Nowadays, there is considerable incentive for automation of die and mold polishing processes. An industrial robot has a lot to contribute to the improvement of efficiency in machining process. Their high level of flexibility and extended working space can outperform conventional machine tools. Due to the extra degree of freedom, industrial robots can machine complicated geometries that otherwise would need special fixturing elements and multiple machining operations [1]. Most of robotic systems are based around a standard 6-axis arm which can either be equipped with material removal devices, such as polishing, grinding, and belts tools. Finishing robots are used for heavy duty tasks such as burr removal and for lighter operations like polishing, lapping, and buffing. Due to advanced technologies, robotic finishing has become feasible and cost effective. Robots have the capability to handle parts weighing up to 450 kg, which allows to deal with complex shaped and heavy components [2]. Production rates achieved by robotic systems cannot be the same as traditional multi-spindle CNC cells, but can conduct multifinishing tasks in a single-part handling operation [2]. Manual operation is a slow process and leads to high reject levels, but robotic machining operations can accurately follow a complex curved path, eliminating these troubles, and produces high-quality produced parts to narrow tolerances with lower reject rates [2]. Polishing process not only can beautify the workpiece appearance but also can enhance the surface quality.
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In the context of safety, robots save laborers from the dust associated with polishing process; this dust may cause lung diseases. Manual polishing is time consuming and laborious work and often requires the use of environmentally dangerous chemicals, whereas robots only use hardened disks. Also in cost perspective, robots do not require any safety items. Simulation is a powerful tool to visualize and plan in different areas of research [3]. Even if robots do not physically exist, simulation offers options for solving problems, investigating, visualizing, and testing a robotic cell. Also, injuries can be avoided using simulation tools. These factors make the manufacturing process easier and when used properly make the process cheaper. An ideal simulation process approaches reality so that the robot programs created off-line can be loaded into the real robot controllers without having to correct them at the shop floor. 1.1 Related works In polishing process, laborers health effected seriously by the heavy industrial dust [4]. To solve the problems associated with manual process, robotic polishing is one of the proposed options [5]. In order to cover uniformly the mold surface and to produce fewer ripples errors, polishing paths should be multi-directional rather than monotonic [6]. Numerous research works are carried out in the machining process automation field by industrial robot. A method for robot finishing cast iron stamping dies to remove tool marks was proposed in [7]. Detailed investigation of abrasive polishing of free-form surfaces using scanning paths by a robot has been done by Tam [8]. Tsai demonstrated effectively polishing process for automatic precision polishing tasks using a new compliant abrasive tool [9]. Marquez sophisticated an approach for a robotic polishing cell for mold manufacturing [10]. In [11], a free-form surface on machining center, an automatic precision polishing method of mold steel, was proposed; the relation between the polishing force and tool displacement is built using a regress model. The results validated the feasibility of the model, which has the ability to achieve a mirror effect surface. Commercially robot simulation systems led researchers to propose the use of a postprocessor in an off-line robot programming system [12]. In [13], authors pointed out the need to generate robot paths from computer-aided design (CAD) data of free-form surface. The generated paths are visualized using simulation interface to parameterize the robot path. A considerable amount of literature has been carried out using CAD as an interface between robot and user; for instance, motion information was extracted from a CAD data exchange format file and converted into robot commands [14]. The user here only needs to define the welding path in the drawing. A method for extracting robot paths (positions and orientations) from a CAD drawing of a given robotic cell
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was presented in [15]. The data work paths directly converted and transformed into robot programs. In [16, 17], collision-free path planning in cluttered environments is tackled; an automatic programming system for a robot equipped with a polishing tool was developed. This system is capable to generate collision-free paths for polishing workpieces that have complicated shapes. 1.2 Objective of the research The presented work aimed to design an automatic polishing system which includes a 6DOF robot, linking polishing tool with the workpiece to establish the kinematic model required for an automated polishing path planning. To achieve this objective, the proposed approach is to solve the forward kinematics and build a relationship between the robot, polishing tool, and workpiece. In practical applications, there is a significant issue that should be developed. Such issue is how to design a teaching system to provide the continuous path of the attached polishing tool according to the workpiece’s complex shape. Generally, the desired path which is required for the position and orientation control is obtained through a conventional teaching process using a Teaching Pendant. FANUC is one of robot manufacturers that provide their own simulation package dedicated to their own robots. One of the tools provided with the FANUC robot is the computer software RoboGuide, which simulates the robot in a virtual environment; RoboGuide can be used to manipulate the virtual robot the same as in the real world, and this kinematics can be evaluated using the featured analysis options [18]. In this paper, the automated robotic polishing system can appropriately generate the desired path as performed by skilled workers. The task specification module is in a graphical user interface (GUI) that allows the user to easily specify the polishing curves over a CAD model of the workpiece. Path generator allows the polishing robot to accomplish the polishing task without any teaching process. The reminder of this paper is organized as follows: Section 2 describes the related automated polishing system kinematic model. Section 3 presents the singularity identification and Jacobian matrix calculation. In section 4, stiffness of the polishing tool was investigated using finite element analysis. Polishing assumptions and fundamental theories and the removal rate model are introduced in section 5. Path planning generation is designed using MATLAB and implemented GUI platform was demonstrated in section 6, while robotic polishing system that has been used in this paper is demonstrated in section 7. In section 8, workcell calibration procedure and experimental results obtained with the proposed approach are discussed and shown on how they can effectively be used on a real setup. Lastly, section 9 presents the conclusion and discusses the future research direction.
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2 Robot platform description The FANUC robot Lr Mate 200iD is part of the FANUC series. FANUC (Fujitsu Automated Numerical Control) is a Japanese company that operates mainly in the construction of industrial robots. This robot is available for studies in the Mechanical Design and Theory Laboratory in the Beijing University of Chemical Technology, and used by students of Robotics. From Fig. 1, it can be seen that FANUC Lr Mate 200iD has a serial 6DOF robotic arm with six revolute joints. The maximum work load is 7 kg, range 717 mm with repeatability of ±0.02 mm and the total mass of 25 kg (www.fanuc.co.jp).
2.1 Kinematic model In order to carry out an automatic polishing process, it is important to use adapted tool paths. The kinematic structure of the FANUC Lr Mate 200iD, connector, and polishing tool are shown in Fig. 1. The relationship between the two links of joints can be described using Denavit-Hartenberg parameters represented as link lengths (d1–d6), link offset (a1–a6), joint angles (θ1–θ6), and twist angle (α1–α6) for the robot. The connector is added as the tool frame, where alc is the width of the connector, alp is the width of the polish tool from its cross section, dlc is the length of the connector, dlp is the length of the polishing tool, αlc is the twist angle of the connector, αp is the twist angle of the polishing tool, θlc is the joint angle of the connector, and θp is the joint angle of the polishing tool. In this method, the forward kinematics is determined from some parameters that have to be defined. D-H parameters for this robot was defined for the assigned frames in Table 1, with manipulator specifications listed in Table 2.
3923 Table 1 DenavitHartenberg parameters
Link
αi
ai (mm)
di (mm)
θi
1 2 3 4 5
−90° 180° 90° −90° 90°
a1 a2 a3 0 0
d1 0 0 d4 0
θ1 θ2 θ3 θ4 θ5
6 7
180° 0
0 alc
8
0
alp
d6 dlc dlp
θ6 0 0
Individual homogenous transformation matrices i-1Pi for the ith joint of robot where i = 1, 2, 3, 4, 5, 6 joints of robot. The resultant matrix, 0P6, is obtained by multiplying all homogenous transformation matrices; i-1Pi is expressed by Eq. (1), which is the position and orientation of the robot arm. 3 cosθi −cosαi :sinθi sinαi :sinθi ai :cosθi 6 sinθi cosαi :cosθi −sinαi :cosθi ai :sinθi 7 i−1 7 pi ¼ 6 5 4 0 sinαi cosαi di 0 0 0 1 2
6 0
T ¼ 10 T :21 T :32 T :43 T :54 T :65 T
ð1Þ
ð2Þ
0
T6 is the pose matrix of the robot arm relative to the robot base, as expressed in Eq. (2). The end-effector orientation relative to the robot base frame is defined with the three vectors: normal, n, represented by matrix (nx, ny, nz); sliding, s, represented by matrix (sx, sy, sz); and approach, a, represented by matrix (ax, ay, az). The last column of this matrix, (p x , py, pz), represents the location coordinates of the robot arm. 2
nx 6 ny 0 p6 ¼ 6 4 nz 0
sy sy sy 0
3 α y py α y py 7 7 αy py 5 0 1
ð3Þ
To calculate the D-H parameters as per Fig. 1 for the connector (dls, als, θls, αls) and polish tool (dPolish Tool, aPolish Tool, θPolish Tool, αPolish Tool), the actual measurements of the connector are determined. Thus, by multiplying Eqs. (3) and (4), Eq. (5) is obtained, and the resultant matrix PConnector is the position and orientation of the connector. Table 2
Fig. 1 Kinematic structure of FANUC Lr Mate 200iD robot, connector and polishing tool
Robot link lengths
Symbol
a1
a2
a3
d1
d4
d6
alc
dlp
Link length (mm)
50
330
−35
−330
−335
−80
100
110
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Int J Adv Manuf Technol (2017) 93:3921–3934 3 cosθlc −cosαlc :sinθlc sinαlc :sinθlc alc :cosθlc 6 sinθlc cosαlc :cosθlc −sinαlc :cosθlc alc :sinθlc 7 7 ¼6 4 0 sinαlc cosαlc d lc 5 0 0 0 1 2
pl
Connector
Plcp ¼ 0 p6 plc
3 Singularity identification ð4Þ ð5Þ
Likewise, the individual homogenous matrix for connector is expressed as Eq. (6); by multiplying Eqs. (4) and (5), the resultant matrix PConnector, Eq. (7) is obtained for the validated position and orientation of the connector. Thus, the position coordinates of the model are in the last column of the first three rows of the matrix PConnector (XConnector, YConnector, ZConnector). 3 cosθp −cosαp :sinθp sinαp :sinθp ap :cosθp 6 sinθp cosαp :cosθp −sinαp :cosθp ap :sinθp 7 7 ¼6 5 4 0 sinαp cosαp dp 0 0 0 1 2
pPolish
Tool
Plcp ¼ pPolish Tool pConnector
ð6Þ
ð7Þ
Due to application conditions and kinematic structure complexity of 6DOF robot systems which have influence on the position, orientation, and singularity avoidance, path planning is a crucial challenge. These factors represent the key elements for layout design production planning and for an automated manufacturing system. Path planning requires first to check if robot can reach the prescribed parts. This can be simply achieved by placing the part inside the robot’s workspace. The robot workspace represents a set of reachable points without considering their orientation. To visual the robot’s workspace, MATLAB tools can be used (Fig. 2). J Base ¼
JV Jω
ð9Þ
B
J 12 B J 22 B J 32 B J 42 B J 52 B J 62
B
J 13 B J 23 B J 33 B J 43 B J 53 B J 63
B
J 14 B J 24 B J 34 B J 44 B J 54 B J 64
B
J 15 B J 25 B J 35 B J 45 B J 55 B J 65
3 J 16 B J 26 7 7 B J 36 7 7 B J 46 7 7 B J 56 5 B J 66
ð8Þ
Elements of the matrix are determined using a set of equations given in (Appendix A): 3.1.1 Jacobian matrix decoupling In order to define singularity conditions, Jacobian matrix was decoupled to simplify the determinant calculation. The Jacobian matrix JWrist at the wrist is calculated when d6 = 0. The general formula is presented in Eq. (11).
The general formula of the JBase matrix is:
J Base
The Jacobian is defined as the coefficient matrix of any set of equations that relates the velocity state of the endeffector described in the Cartesian space to the actuated joint rates in the joint velocity space. The calculated Jacobian matrix JEnd-effector is with respect to the endeffector fame. Jacobian matrix was calculated using the vector method to investigate the singularity as in Eq. (8).
Z 0 : 0 Pn Z 1 : 0 Pn −0 P1 Z 1 : 0 Pn −0 P2 Z 1 : 0 Pn −0 P3 … Z n−1 : 0 Pn −0 Pn ¼ Z1 Z2 Z3 … Z n−1 Z0
J Base ¼ 0 R6 : J End‐effector
J 11 6BJ 6 B 21 6 J 31 ¼6 6BJ 6 B 41 4 J 51 B J 61
3.1 Jacobian matrix calculation
The calculated Jacobian matrix JBase is with respect to the base fame whereas the JEnd-effector matrix is calculated using Eq. (9).
2B
Singularity is defined as the configuration where the values of joint position cause the Jacobian to become singular (the determinant of the Jacobian is zero) or when the manipulator loses degree(s) of freedom [19]. Most of the researchers used the Denavit and Hartenberg convention in their research [20]. The majority of industrial robots take advantage of Pieper’s theory introduced in [21]. Puma and Fanuc families were unified in [22] by developing a unified Reconfigurable Puma-Fanuc model. Singularity conditions were investigated for the unified model. Classification of the singularity is also studied in [23]. Other approach for singularity analysis is to find a representation for the determinant of the Jacobian [24]. To develop the reachable workspace of serial 6DOF manipulators for determining the effective travel path regions, a technique is presented to put forth advantages of workspace visualization [25]. An approach called the task reconstruction method offers a solution to algorithmic singularities and kinematics. The method not only offers a singularity free trajectory but also guarantees task performance [26].
B
2W
ð10Þ
J Wrist
J 11 6WJ 6 W 21 6 J 31 ¼6 6WJ 6 W 41 4 J 51 W J 61
3 J 13 0 0 0 7 J 23 0 0 0 7 W 7 J 33 0 0 0 7 W W W W J 43 J 44 J 45 J 46 7 7 W J 53 W J 54 W J 55 W J 56 5 W W W W J 63 J 64 J 65 J 66
W
W
W
W
J 12 J 22 W J 32 W J 42 W J 52 W J 62
ð11Þ
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Fig. 2 Show the robot workspace
ð18Þ
3.1.2 Singularity analysis
C i ¼ −α1 −α2 :cosðθ2 Þα3 :cosðθ2 −θ3 Þ þ d 4 :sinðθ2 −θ3 Þ ¼ 0
The 6 × 6 Jacobian matrix configuration is singular if and only if: Det(J) = 0. In order to simplify the calculation, matrix J can be divided into four blocks of 3 × 3 matrices, as shown in Eq. (12). J 11 J 12 End‐effector ð12Þ J¼ J 21 J 22
Using the D-H parameters from Table 1 and solving Eq. (17), when θ3 = 85.179°, the boundary singularity is occurred. It can be seen that the wrist is located on the x2 axis. Likewise, the boundary singularity is located on the y2 axis, while forearm interior singularity occurs when the wrist center intersects the axis of the base rotation. The Jacobian matrices are given in Eqs. (20), (21), and (22), respectively.
Referring to [24, 27], the Jacobian matrix JWrist will have the block triangular form: J 11 J 3x3 Wrist ð13Þ J¼ J 21 J 22 using the recursive Newton-Euler method. According to [24], the determinant of Jacobian is independent of the velocity reference point selected. The following equation can be written: Det Wrist J ¼ Det End‐effector J ð14Þ Combination of Eqs (13) and (14) will result: Det Wrist J ¼ Detð J 11 Þ:Detð J 22 Þ
ð16Þ
Boundary singularities as in Eq. (17):
C b ¼ −α3 :sinðθ3 Þ þ d 4 :cosðθ3 Þ ¼ 0 &
Interior singularities as in Eq. (18):
2
J 11
3 0 0 0 5 ¼ 4 −36:4733 0 0 0 86:4733 −416:4733
ð19Þ
2
2
J 11
3 0 416:4733 −416:4733 5 ¼ 4 380:0017 0 0 0 0 0
ð20Þ
2
2
ð15Þ
The forearm singularities can be identified by verifying the determinant of the matrix J11. Two conditions will be resulted. Forearm singularities &
J 11 ¼ 2 J 0 :0 J 11
ð17Þ
J 11
3 0 −33:5666 324:5953 5 ¼ 40 0 0 0 105:3656 −260:9366
ð21Þ
2
Singularity conditions of the robot can be decoupled into two determinants: Detð J 11 Þ ¼ 0 and Detð J 22 Þ ¼ 0:
2
ð22Þ
The singular configurations are shown in Fig. 3. Angles θ2 and θ3 need to be solved. When boundary and interior singularities occur, the angle θ3 is calculated by solving Eq. (23). θ3 ¼ atan
d4 a3
ð23Þ
Two values of angle θ3 are 85.179° and −94.821°. The value for θ2 is solved by using Eqs. (9), (10), (19), (20), and (21) in Appendix B. The joint angle is calculated θ2 = 90°. The robot is moved to the calculated singular position (see Fig. 4).
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Fig. 3 Singular configurations
(a) Forearm Boundary Singularity Configuration. (b)Forearm Interior Singularity Configuration.
Wrist singularities: Wrist singularity can be identified by verifying the determinant of the matrix J22 (see Eq. (24). Det ð J 22 Þ ¼ − sinðθ5 Þ ¼ 0
ð24Þ
That is true when θ5 = 0°, or θ5 = 180°, which physically means that joints 4 and 6 are lined up.The graphical representation of the wrist singular condition is given below in Fig. 5. The robot home zero position, which is presented in Fig. 1 and the same in Fig. 5, is singular because Z3 is parallel to Z5. The associated Jacobian matrix is given in Eq. (25). 2 3 −1 0 −1 0 ð25Þ J 22 ¼ 4 0 −1 0 5 0 0 0 In order to avoid the singularities problem, the position of the workpiece in the robot base coordinate system should be considered according to its size and shape. Before generate polishing paths, singularity region investigation can be utilized in robot span paths optimization. By visualizing the singularity regions in prior, the manufacturing layout design will
be more accurate. In such cases, it is of utmost importance to locate the workpiece in a place so that it will be out of singular regions. In this experiment, the workpiece which will be machined is a small size; singularity avoidance task is easy to be achieved.
4 Polishing tool stiffness Broadly, robots offers high-speed machining conditions, but with significantly lower stiffness and positioning accuracy. In order to improve the positioning accuracy of the tool on the workpiece during the polishing process, a rigid connection between arm robot and polishing tool should be considered. In this experiment, steel connector of Q235 as connecting part with thickness of 5 mm is used for bearing the quality of the polishing tool part and tool polishing pressure. Figure 6 illustrates that the servo motor weight is 1.3 kg; the connector that is subject to the quality of the polishing tool is about 2.0 kg. In order to ensure the connection strength, a finite element analysis software used for this investigation is Abaqus 6.10 as shown in Fig. 6. System stiffness decreases by several factors; they are called as clearance, strain, and deflection. From the above graph, the maximum displacement at the end edge of the connector is 1.29 × 10−5 mm; the maximum position of the motor axis is 1.135 × 10−5 mm. As the motion accuracy of robot is ±0.02 mm, this displacement value can be neglected (less than the robot motion accuracy). Therefore, the polishing tool strength satisfies the research requirements.
5 Coordinate pose calculation
Fig. 4 Forearm boundary and interior singularity configuration
When machining curved surfaces using robot polishing system, the complex curved surfaces positions is the most challenged part.
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Fig. 5 Wrist singularity configuration
The coordinate system {A} is the coordinate system of the robot end-effector (polishing tool); the coordinate system {B} is the workpiece coordinate system. The attitude and the coordinate system {A} are consistent; the coordinate system {C} in the YC axis is tangent to any point on the curve. The polishing direction of the end-effector (polishing tool) is along the ZC axis and perpendicular to the YC axis. The XC axis is perpendicular to the surface. Figure 7 shows the coordinate relationship of the robot’s free-form surface. When curve function formula is obtained as in Appendix C, the surface is subdivided according to certain rate to determine the number of points and then to use the above method to calculate the position vector and attitude of each point on the surface. The path planning of robot polished surfaces is actually to get the position vector and attitude matrix of any point on the workpiece surface relative to the world coordinate system. After knowing the curve function, we can get coordinates of any point in the coordinate system {B}, which is the position vector of the point in the coordinate system {B}; this point with respect to the coordinates is the attitude in {B} need to be solved. After obtaining the rotation matrix of the point, we can get the position vector and rotation matrix of the point with respect to the coordinate system {A} according to the translation and rotation. 5.1 Polishing removal rate Machining surface quality is effected by polishing pressure between the polishing tool and the machined surface, rather
than the polishing force [28]. And polishing pressure changes with the change of curved surface curvature. The Preston equation is the empirical formula used in the polishing process. The Preston equation is used to describe the relationship between the removal of the workpiece in the polishing process and the polishing process parameters and the characteristics of the polishing tool [29]. In this equation, the polishing speed, pressure, and other influence coefficients are classified as a proportional constant (related to the material of the workpiece to be polished, the material of the polishing tool, and the operating temperature). The expression is as follows: t
Δz ¼ ∫0 k p vs pc d t ;
ð26Þ
where Δz − dt is the time amount of the material removed, kp is wear Coefficient, vs is the relative speed between the workpiece and tool, pc is the relative pressure of polishing area, and dt is polishing tool dwell time. In addition, according to the Archard equation [30], the material removal rate per unit of polishing path length is expressed as: wl ¼
dh p vs ¼ k abr c ; dl H v va
ð27Þ
where karb is the wear coefficient, Hv is hardness of the material, and va is the polishing tool speed. In the contact area between the polishing tool and the workpiece, the amount of material removed per length of the polishing path can be obtained by integrating the length of the path Fig. 8.
Fig. 6 Deflection analysis of the polishing tool
(a) Deflection analysis of the polishing tool (b) Deflection analysis when subjected to Polishing
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6 Path generation
Fig. 7 Coordinate relation diagram of robotic polishing surface
It is known that in the Preston equation, dt can be seen as the time that the polishing tool consumes along the length of the path movement dl, dt ¼
dl : va
ð28Þ
Then, the Preston equation can be expressed as: dl l Δz ¼ ∫0 k p vs pc : va
ð29Þ
Comparing the two equations, we can see the relationship between the two equations in the wear coefficient: kp ¼
k arb : Hv
ð30Þ
Therefore, the two material removal model equations have similar physical meaning; the essential difference is that the wear coefficient of the different values. The removal rate of the material is along the polishing path, and the removal rate is the amount of removal on the unit path length, and the speed on the polishing area is a very important factor. The distribution of the velocity on the path of the material is of great significance to the removal of the material and the surface quality of the workpiece after polishing.
Fig. 8 Motion polishing sketch
Generally, path is obtained using conventional robotic teaching method when executing an industrial robot motion. When conventional teaching method for parts with complex curved surface is achieved, the user has to input a large number of teaching points (x, y, z) along the surface. Such teaching method is skill dependent, time consuming, and error prone. The previous section describes the path planning method for robots to polish free-form surfaces. After obtaining the position vector and point attitude, it is necessary to input the six values of each point into the RoboGuide software in a certain order to generate the polishing path. This method of calculation is computationally intensive when changing the polishing path. In this work, TP file will be generated in RoboGuide offline software, TP file is the polished path, and TP programs are binary files that can be edited through the robot’s Teach Pendant. It also can be compiled/decompiled from an LS file. TP programs offer a limited assembler-like functionality. The LS file is also a polish path file, but it is in TXT format and can be modified. Therefore, in this way, LS file automatically created greatly reduce the time consuming and improve work efficiency. In MATLAB, the calculation problem is divided into three parts: & & &
Calculation process. Create path planning control interface. Generate executable file and create the polishing path in LS file format.
Thus, coordinate values of the points required in the path are calculated and MATLAB will automatically generate coordinate values according to the calculation results. 6.1 GUI implementation When pose calculation of the midpoint of polishing path is completed, it is necessary to link the pose of point with the path planning. Thus, the path planning control interface of the robot’s polished surface is made. To create polishing path, it only needs to input the relevant parameters; the path planning LS file can be created as well. The robot polishing path planning control interface includes the workpiece parameters, path planning, and writing LS file composed of three interfaces. In workpiece parameter, setting interface can choose the type of the workpiece to be polished, set function type of surface to be polished, input function parameters and the position and attitude coordinates of each polishing point on the workpiece surface can be obtained by MATLAB; polishing point is shown in the left side of Fig. 9.
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Fig. 9 Graphical user interface
(a)
(b)
(c) (d) (a)Workpiece Parameters. (b) Polishing Path. (c) Creating LS file. (d) LS program file
In the polishing path interface, an appropriate polishing path generation program of a variety of workpiece polishing path diagrams can be selected. In creating LS file interface, the user needs to input parameters such as movement type, movement speed, unit speed, and robot movement termination type; and to give position and coordinates of robot coordinate system relative to the workpiece coordinate system. The full LS Program file was used in this experiment is in Appendix D, as shown in Fig. 9.
7 Overview of robotic polishing system This section describes the robotic polishing system. The preparation process must include several polishing steps and these steps have to be performed in good manner. The use of proper working tools facilitates the process a lot. Actuator that used in this system is 6-axis arm robot with polishing tool as end-effectors. Arm robot that is used in this research is FANUC Lr Mate 200iD, R-30iB mate controller shown in Fig. 10, and RoboGuide off-line programming software. The robot consists of an anthropomorphic structure with six degrees of freedom. Polishing tool is attached to the end-effectors of the arm robot. Figure 7 shows
the implementation of the polishing robot system. The polishing head consists of Yaskawa Servo Motor, Servo Drive model (SGDV-2R8A01) selected and coupled with polishing tool. This selected motor is Yasukawa Σ-V series, model SGMJV04A3E6S. The Σ-V series is mainly used for applications requiring “high speed, high frequency, high positioning accuracy.” The system uses programmable logic controller (Siemens S7-300) as the control core to set up communication between PC and industrial robot, servo driver, and control the whole polishing system online through machine interface. This article presents the task specification module is in a graphical user interface (GUI) that allows the user to easily specify the polishing curves over a computer-aided design (CAD) model of the workpiece. When robot integrated into a system with a part presentation device, finishing tools, and system software, it can make an excellent automated polishing system. The 6DOF robot with its axes of motion can deal with complex shaped workpieces, and with the proper head and tooling design can successfully polish most part surfaces. Combining artificial polishing design idea and machine polishing production line, the polishing system can achieve automatic process. The system structure is shown in Fig. 10.
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Fig. 10 System components of the automated polishing system
The workpieces that used in this experiment is aluminum alloy (6061). This alloy exhibits high strength, excellent machining, and polishing characteristics. It can be used as die in propeller manufacturing. The machined workpiece has two kinds of surfaces, one is a plane and the other is a spherical surface Table 3.
completed in MATLAB. Each interface design procedure described is the following: & & &
7.1 Interface design Robotic polishing system cannot be integrated into the control system. Therefore, converting RoboGuide.m file to the executable RoboGuide.exe is needed. Then, the executable file RoboGuide.exe can be called in the WinCC Human Machine Interface to integrate the control system of the robot polishing and path planning control interface. 7.2 Human Machine Interface control system Designing a Human Machine Interface of the control system is followed by completing the communication settings. The Human Machine Interface configuration mainly composed of the main interface, parameter setting, and alarm monitoring. The interface of path planning is
The main interface is to control motor start and stop. Parameter setting interface, to control motor speed, control robot movement speed. Alarm monitoring interface, to monitor motor input and output speed.
8 Experimental set up In this section, the ability of the proposed method is examined through some experiments on polishing task using an industrial robot FANUC Lr Mate 200iD with R-30iB controller. Using S7-300 PLC and WinCC to develop the Human Machine Interface of robot polishing control system. The integration of path planning interface and the human-machine interaction interface of the control system is realized using MATLAB. As the designing control system of robot and the Human Machine Interface is completed, control of the entire experimental platform is more convenient, fast, and efficient. 8.1 Workcell alignment and calibration
Table 3
Parameters of workpiece and polishing brush
Name
Density (kg/m3)
Elastic modulus (GPa)
Poisson’s ratio
Workpiece Polishing brush
2800 1150
68.8 1.68
0.25 0.28
Robot calibration is a process by which parameter values of real robot affecting its accuracy are established through direct or indirect measurement and to be used to improve its accuracy by modifying the positioning software. Calibration procedure includes modeling, measurement, parameter identification, and implementation of compensation [30].
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Fig. 11 Snapshots of experimental procedure of robot polishing plate surface
Tolerances are the main problem of robot. Robot calibration has high impact on an accepted off-line programming because if the virtual environment not exactly be mapped onto the real world, an automatically generated programs cannot be used in practice. Robot coordinates which are computed offline are often unusable without touchup due to several inaccuracy issues. A workcell alignment process is finally performed to define the exact position of the reference frames, depending on the real devices and the robot kinematic and dynamic behaviors; robot work-cycle iterative executions have been achieved for the final tuning and achievement of all requirements and technical criteria. Performing a series of tests, minor part misplacement, and the robot’s kinematic errors, the polishing tool has been conditioned so that its surface matches well with the workpiece. To achieve a reflective surface with mirror finish, the preparation process must involve several polishing steps. Robot polishing experimental system built control system designed; path planning methods have been introduced and verified the control system and the effect of automatic path planning. The machined workpiece has two kinds of surfaces, one is a plane and the other is a spherical surface.
8.2 Experimental procedure 8.2.1 Experimental procedure for plane surface polishing The experimental system is shown in Fig. 11. According to the experiments, the first step of plane polishing process is designing path planning in MATLAB as shown in Fig. 11a. Robotic modeling was created for the polishing application and generating LS file and send to off-line programming to simulate polishing path in virtual environment as shown in Fig. 11b. It presents the real scenario, which confirms the efficiency of the graphical simulation. The simulation was developed using the RoboGuide software. In Fig. 11c, it creates TP file to controller to start robot in Human Machine Interface with rotation speed of 1000 rpm. Figure 11d–g shows the polishing process after 15, 45, and 60 s, respectively. Finally, robot back to the initial normal position. During the process, robot is mounted on the worktable. The workpiece is fixed onto the worktable so that the relative position of the workpiece and table remain unchanged. Polishing task has been completed, robot can move according to the designed path, and the motion process is basically according to the set polishing parameters. There are no collisions and interference in the robot motion process.
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Fig. 12 Snapshots of experimental procedure of robot polishing aspherical surface
As shown in Fig. 11h, i, it is possible to observe a smooth polishing, which was already expected. By checking the surface before and after polishing, the polished workpiece can be found in good condition. The control system can control the entire polishing process smoothly. It is convenient for the start and stop robot and control motor speed. The robot can execute according to the automatically planned polishing paths. After polishing, it was noticed that the workpiece edge is not polished well, due to the installation of the robot and the fixed workpiece errors. 8.2.2 Experimental procedure for aspherical surface polishing The calculation of the polishing path midpoints for the aspherical surface workpiece is different to the previous one (plane surface), and the calculation of the position of the points in the path is more complicated. The series of images in Fig. 12 show the polishing procedure. The second case study was carried out using a workpiece as shown in Fig. 12. According to the experiments, the first step of aspherical polishing process is designing path planning in MATLAB as shown in Fig. 12a. Generating LS file and sending it to off-line programming to simulate polishing path in
virtual environment is shown in Fig. 12b. In Fig. 12c, it shows generating TP file to controller to start robot in Human Machine Interface. Figure 11d–g shows the polishing process after 5, 10, 15, and 20 s, respectively. Finally, polishing process is completed, robot back to the normal position. Fig. 12h, i show the workpiece before and after polishing. The Human Machine Interface is adequate to control the start, stop, and rotation rate of the motor. The results from the experiments that have been carried out during the work show that some points cannot be polished due to workpiece slope surface, and the robot rotation limit are not considered when planning polishing path, in addition to the position error in the experiment, also yields to weaken the polishing effects. Therefore, in the future work, planning the polishing path needs to consider the workpiece slope so that robot can rotate the angle.
9 Conclusion Automated polishing system platform was built based on an industrial robot. The FANUC LR Mate 200iD robot was modeled to calculate the kinematics. The robot tool position and pose transformation problem were studied. Singularity
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configurations of a 6DOF industrial robot were investigated. Polishing paths that make the robot pass near these configurations have been avoided. Polishing aspherical workpiece in virtual workstation was established by RoboGuide to simulate the polishing path. The GUI aids the user in verifying that the whole surface is to be correctly polished. The mirror effects of polished surfaces using robotic polishing system are examined. Finally, according to the change of the workpiece curvature, the feeding rate on axis of the tool was adjusted to ensure the uniformity of the polishing spot area in different positions of the workpiece. The effectiveness of the proposed polishing method is proven through polishing experiments using an industrial robot. For further precise polishing aspheric workpiece forms, the higher precision processing technology is needed; this paper summarizes several problems need to be solved in two perspectives:
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In the perspective of singularity avoidance, due to its motion restrictions, the polishing method adopted in this paper is the polishing of the robot’s fixed position, but for the large aspheric surfaces or more complex workpiece forms, the robot will produce singularity in some regions. To solve the problems, a robotic mobile (wheeled base) polishing system can realize the work area. It can reduce the cost of adding robots. The extra degrees of redundancy also give the robot the dexterity needed to maintain a compliant motion so that the end-effector is always perpendicular to the surface of the workpiece. In the perspective of automation, a semi-automatic polishing system has been used in this paper. An operator provides continuous supervision; the feasibility of fully automating the process will be investigated. Non-contact method may present an alternative to allow the surface defect to be measured rapidly and with an acceptable accuracy. In order to fulfill this, vision machine will be integrated with the FANUC arm robot (Lr Mate 200iD) to be fully closed loop control of the polishing process.
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These two characteristics represent the key elements for production planning and layout design of the automated manufacturing systems.
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Acknowledgements The work described here forms part of the Ph.D. dissertation of the first author. This work was financially supported by the Fundamental Research Funds for the Central Universities (YS1403, ZY1521).
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