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Assessment of a robot base production using CAM programming for the FANUC control system

  • Peter Michalik EMAIL logo , Jana Fabianova , Leopold Hrabovsky , Marinko Maslarić , Luboslav Straka and Jozef Macej
Published/Copyright: September 27, 2021
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Open Engineering
From the journal Open Engineering Volume 11 Issue 1

Abstract

The subject of the article is the research of the production of a robot base using CAM programming, Autodesk Inventor HSM software, followed by the generation of G code – NC program. The research specifically examined the accuracy of measurement and evaluation of roundness with coaxiality on a 3D measuring device Thome. The surface roughness of the circular holes was measured using a Mitutoyo SJ 400 roughness meter. The maximum deviation of the roundness of the diameter D56H7 measured was 0.011 mm, and the diameter D72H7 measured was 0.013 mm. The coaxiality deviation of the diameters D56H7 and D72H7 measured was 0.017 mm.

Keywords: robot; CAM; coaxiality; roughness; robot base

1 Introduction

Robotics is currently established not only in industry but also in research laboratories. This term can be used even in areas where it is inappropriate, so knowing exactly what the word robot means, how it is controlled and how it can be used in specific applications is very important.

Souza et al. [1] designed a wireless lightweight robotic arm for teaching purposes that serves only to program its path without a gripper. And our robotic arm can be used not only for educational but also for industrial purposes, handling light loads up to 2 kg. Unda et al. [2] introduced the most general case study of the movement of a mobile robot with robotic arm. The four-wheel differential configuration allows greater stability for robotic arm operation. In addition, the design allows the incorporation of sensors such as ultrasonic sensors for interaction with obstacles. The built robotic arm allows the clamping of pieces of less than 20 g, at a maximum distance of 350 mm from the base of the robotic arm. Benitez et al. [3] designed and manufactured a simple robotic arm for online teaching of students. For comparison with our robotic arm, which was produced by the chip milling method, Benitez et al. made arms on a 3D printer with printing arms made of plastic materials. This method of production is commonly available to most students. Sáenz Zamarrón et al. [4] developed Educational Robot Arm, which has four axes of freedom and is also intended for educational purposes, however. Our robotic arm has one less degree of freedom. Similarly, the components of our robotic arm were fabricated by the chip method on a CNC milling machining center. Zamarrón et al. used Robotics Toolbox to simulate the control of the Matlab robotic arm and we are considering the use of LabView software. We also consider the control unit Arduino.

One of the specific applications is the use of robotic workplaces in the handling and transport of various products using trough and hose conveyors, which are currently finding increasing use [6]. The individual components of the robots are also manufactured using CAM programming [7]. The robots are divided according to various criteria. One of them is the division according to kinematics. The first type is the cylindrical coordination system (Figure 1) [13].

Figure 1 Cylindrical coordination system of a robot [13].
Figure 1

Cylindrical coordination system of a robot [13].

This construction performs three primary movements, of which two movements are translational and the third is rotary. The coordination system is not widely represented in the industry and with a construction based on a cylindrical system of movements, it is used for primitive tasks, such as spot welding, or as a simple manipulator. The robot nodes are inspected by the finite element method [5] not only for the magnitude of the stress but also for the magnitude of the deformation [8]. Prior to actual production of some robot components, the properties of the materials are also checked [11]. In addition to conventional machining technologies, water jet machining is used to produce some robot components [10], while it is also necessary to control the quality of the machined surface [12]. At the starting point, the control unit is instructed to start the motors in the order specified by the program until the position required to reach the end point position is reached. The whole cycle is repeated either immediately after reaching the starting point or after instructing a timer or a pulse from the sensor – cameras [9].

2 Robot base

Robot base in Figure 2 shows that 3D model is one of the parts of the robot assembly, it forms the base of the robot, therefore its design paid attention to its rigidity and load-bearing capacity and its dimensions were chosen on the basis of these requirements. The base of the robot houses a gearbox with a servomotor, which performs a rotary movement of the arm installed on the base. High emphasis is placed on mounting the gearbox in the robot base. Its mounting must be as precise as possible in order to prevent or minimize deviations of the arm rotational movement. The gearbox is housed in tolerated holes D56H7 and D72H7. The placement of H7 at a given dimension represents a hole tolerance of 0.015 mm. To produce these tolerated dimensions, a reaming operation with a boring bar was chosen.

Figure 2 3D model of a robot base.
Figure 2

3D model of a robot base.

2.1 Procedure of robot base programming and production

For programming the production of tolerated dimensions of the robot base, the drawing documentation in Figure 3 with a roughness R z of 12.5 μm, made of Al4.5MgMn material, was documented. The tools used to produce tolerated holes were as follows:

  • Face drill 880-D4100C6-03,

  • Carbide 4 – plate cutter with a diameter of Ø 20 mm, and

  • ISCAR BHF-MB50-80 boring bar.

Figure 3 Production drawing of a robot base.
Figure 3

Production drawing of a robot base.

Machining was designed in the CAD/CAM program Autodesk Inventor HSM. The roughened base of the robot was clamped to the worktable using four clamps.

The first step in programming the machining of the robot base was to select the size of the semi-finished product and set its zero value (Figure 4). The zero point was selected in the middle of the cylindrical part of the robot base with a diameter of D140 mm.

Figure 4 Setting of zero point of the robot base.
Figure 4

Setting of zero point of the robot base.

This was followed by drilling and setting the cutting conditions of the defined drilling cycle (Figure 5). A drill bit with a diameter of D41 mm under type designation 880-D4100C6-03 was used for drilling. The drilling depth was 15 mm. After drilling a D41 mm hole, an adaptive milling operation was performed using a carbide four-plate cutter with a diameter of D20 mm. In this machining strategy, the cutter was plunged to a depth of 4 mm and the hole D41 mm was enlarged to a diameter of D72 mm with the addition of 0.3 mm.

Figure 5 Setting of cutting parameters.
Figure 5

Setting of cutting parameters.

The overall dimension of the hole produced was D71.7 mm. An allowance of 0.3 mm was chosen for the last reaming operation when the final dimension of the hole is made to tolerance H7, which means that the hole has a dimension of D72.015 mm (Figure 6).

Figure 6 Defining of adaptive milling parameters.
Figure 6

Defining of adaptive milling parameters.

During programming, a reaming cycle was selected in which a tool was defined – a boring bar, the construction of which allows you to set diameters by one hundredth of a millimeter.

The 3-axis vertical machining center Pinnacle VMC 650S was chosen to produce the robot base. Figure 7 shows hole milling on the Pinnacle VMC 650S. The machining center is controlled by the FANUC control system.

Figure 7 Hole milling on the Pinnacle VMC 650S.
Figure 7

Hole milling on the Pinnacle VMC 650S.

The procedure to produce a hole with the diameter of Ø 56 H7 is the same as for the production of a hole Ø 72 H7.

3 Results

The production of tolerated dimensions was followed by measurements in which the surface roughness of the individual holes, the roundness and the coaxiality of the holes were determined. The surface roughness was measured using a Mitutoyo SJ 400 roughness meter (Figure 8). The measurements were repeated 11 times. The results of measurements for individual holes are shown in the graphs in Figures 9 and 10.

Figure 8 Measurement of the surface roughness of the hole D56H7.
Figure 8

Measurement of the surface roughness of the hole D56H7.

Figure 9 Roughness of the hole surface with diameter D56H7.
Figure 9

Roughness of the hole surface with diameter D56H7.

Figure 10 Roughness of the hole surface with diameter D72H7.
Figure 10

Roughness of the hole surface with diameter D72H7.

The following values were found by measurement: hole D56H7 highest value of surface roughness R a = 1.01 μm, R z = 8.4 μm. The lowest surface roughness R a was 0.58 μm and the lowest R z value was 3.6 μm. For a hole with dimension D72H7, the highest surface roughness was R a = 1.08 μm and R z = 9.2 μm. The lowest surface roughness R a was 0.57 μm and R z was 3.8 μm.

Another measured parameter was the roundness and coaxiality of the holes. These parameters were measured on a 3D measuring device of the Thome type. Figure 11 shows the measurement of these parameters.

Figure 11 Measurement of 3D parameters with the Thome device.
Figure 11

Measurement of 3D parameters with the Thome device.

Figure 12 shows the assembled robot base with robotic arm 1, gearbox and actuator.

Figure 12 Assembled robot base with robotic arm 1.
Figure 12

Assembled robot base with robotic arm 1.

The quality of the machining process was evaluated using the process capability index C P, which is defined by the following relation:

(1) C P = USL − LSL 6 σ ,

where USL is the upper specification limit, LSL is the lower specification limit and σ is the standard deviation of the process.

For diameter 56H7 the value of C P = 0.56 and for diameter 72H7 the value of C P = 0.74.

4 Conclusion

The proposed method of production of the robot base was realised on a three-axis machining center Pinnacle VMC 650S. The quality of machining was verified using the process capability index C P. The CAD/CAM program Autodesk Inventor HSM was used for CAM programming. Cylindrical holes with diameters D56H7 and D72H7 were made in three operations: drilling, adaptive milling–punching and reaming. The result of measuring the roundness of the hole D56H7 was 0.011 mm, and for the hole D72H7 it was 0.013 mm. The coaxially deviation between holes D56H7 and D72H7 was 0.017, which meets the required tolerance for given dimensional range. After evaluating these results, we can conclude that the chosen method of production and measurement of the robot base allows you to create the required assembly along with a recommendation for production of other robot components.

Acknowledgments

This work is a part of research project VEGA 1/0045/18 and SK-SRB-18-0053.

  1. Conflict of interest: Authors state no conflict of interest.

References

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Received: 2021-06-04
Revised: 2021-07-14
Accepted: 2021-07-26
Published Online: 2021-09-27

© 2021 Peter Michalik et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/eng-2021-0085
Keywords for this article
robot; CAM; coaxiality; roughness; robot base
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BY 4.0

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  59. Simulation assessment of the half-power bandwidth method in testing shock absorbers
  60. Predictive analysis of the impact of the time of day on road accidents in Poland
  61. User’s determination of a proper method for quantifying fuel consumption of a passenger car with compression ignition engine in specific operation conditions
  62. Analysis and assessment of defectiveness of regulations for the yellow signal at the intersection
  63. Streamlining possibility of transport-supply logistics when using chosen Operations Research techniques
  64. Permissible distance – safety system of vehicles in use
  65. Study of the population in terms of knowledge about the distance between vehicles in motion
  66. UAVs in rail damage image diagnostics supported by deep-learning networks
  67. Exhaust emissions of buses LNG and Diesel in RDE tests
  68. Measurements of urban traffic parameters before and after road reconstruction
  69. The use of deep recurrent neural networks to predict performance of photovoltaic system for charging electric vehicles
  70. Analysis of dangers in the operation of city buses at the intersections
  71. Psychological factors of the transfer of control in an automated vehicle
  72. Testing and evaluation of cold-start emissions from a gasoline engine in RDE test at two different ambient temperatures
  73. Age and experience in driving a vehicle and psychomotor skills in the context of automation
  74. Consumption of gasoline in vehicles equipped with an LPG retrofit system in real driving conditions
  75. Laboratory studies of the influence of the working position of the passenger vehicle air suspension on the vibration comfort of children transported in the child restraint system
  76. Route optimization for city cleaning vehicle
  77. Efficiency of electric vehicle interior heating systems at low ambient temperatures
  78. Model-based imputation of sound level data at thoroughfare using computational intelligence
  79. Research on the combustion process in the Fiat 1.3 Multijet engine fueled with rapeseed methyl esters
  80. Overview of the method and state of hydrogenization of road transport in the world and the resulting development prospects in Poland
  81. Tribological characteristics of polymer materials used for slide bearings
  82. Car reliability analysis based on periodic technical tests
  83. Special Issue: Terotechnology 2019 - Part II
  84. DOE Application for Analysis of Tribological Properties of the Al2O3/IF-WS2 Surface Layers
  85. The effect of the impurities spaces on the quality of structural steel working at variable loads
  86. Prediction of the parameters and the hot open die elongation forging process on an 80 MN hydraulic press
  87. Special Issue: AEVEC 2020
  88. Vocational Student's Attitude and Response Towards Experiential Learning in Mechanical Engineering
  89. Virtual Laboratory to Support a Practical Learning of Micro Power Generation in Indonesian Vocational High Schools
  90. The impacts of mediating the work environment on the mode choice in work trips
  91. Utilization of K-nearest neighbor algorithm for classification of white blood cells in AML M4, M5, and M7
  92. Car braking effectiveness after adaptation for drivers with motor dysfunctions
  93. Case study: Vocational student’s knowledge and awareness level toward renewable energy in Indonesia
  94. Contribution of collaborative skill toward construction drawing skill for developing vocational course
  95. Special Issue: Annual Engineering and Vocational Education Conference - Part II
  96. Vocational teachers’ perspective toward Technological Pedagogical Vocational Knowledge
  97. Special Issue: ICIMECE 2020 - Part I
  98. Profile of system and product certification as quality infrastructure in Indonesia
  99. Prediction Model of Magnetorheological (MR) Fluid Damper Hysteresis Loop using Extreme Learning Machine Algorithm
  100. A review on the fused deposition modeling (FDM) 3D printing: Filament processing, materials, and printing parameters
  101. Facile rheological route method for LiFePO4/C cathode material production
  102. Mosque design strategy for energy and water saving
  103. Epoxy resins thermosetting for mechanical engineering
  104. Estimating the potential of wind energy resources using Weibull parameters: A case study of the coastline region of Dar es Salaam, Tanzania
  105. Special Issue: CIRMARE 2020
  106. New trends in visual inspection of buildings and structures: Study for the use of drones
  107. Special Issue: ISERT 2021
  108. Alleviate the contending issues in network operating system courses: Psychomotor and troubleshooting skill development with Raspberry Pi
  109. Special Issue: Actual Trends in Logistics and Industrial Engineering - Part II
  110. The Physical Internet: A means towards achieving global logistics sustainability
  111. Special Issue: Modern Scientific Problems in Civil Engineering - Part I
  112. Construction work cost and duration analysis with the use of agent-based modelling and simulation
  113. Corrosion rate measurement for steel sheets of a fuel tank shell being in service
  114. The influence of external environment on workers on scaffolding illustrated by UTCI
  115. Allocation of risk factors for geodetic tasks in construction schedules
  116. Pedestrian fatality risk as a function of tram impact speed
  117. Technological and organizational problems in the construction of the radiation shielding concrete and suggestions to solve: A case study
  118. Finite element analysis of train speed effect on dynamic response of steel bridge
  119. New approach to analysis of railway track dynamics – Rail head vibrations
  120. Special Issue: Trends in Logistics and Production for the 21st Century - Part I
  121. Design of production lines and logistic flows in production
  122. The planning process of transport tasks for autonomous vans
  123. Modeling of the two shuttle box system within the internal logistics system using simulation software
  124. Implementation of the logistics train in the intralogistics system: A case study
  125. Assessment of investment in electric buses: A case study of a public transport company
  126. Assessment of a robot base production using CAM programming for the FANUC control system
  127. Proposal for the flow of material and adjustments to the storage system of an external service provider
  128. The use of numerical analysis of the injection process to select the material for the injection molding
  129. Economic aspect of combined transport
  130. Solution of a production process with the application of simulation: A case study
  131. Speedometer reliability in regard to road traffic sustainability
  132. Design and construction of a scanning stand for the PU mini-acoustic sensor
  133. Utilization of intelligent vehicle units for train set dispatching
  134. Special Issue: ICRTEEC - 2021 - Part I
  135. LVRT enhancement of DFIG-driven wind system using feed-forward neuro-sliding mode control
  136. Special Issue: Automation in Finland 2021 - Part I
  137. Prediction of future paths of mobile objects using path library
  138. Model predictive control for a multiple injection combustion model
  139. Model-based on-board post-injection control development for marine diesel engine
  140. Intelligent temporal analysis of coronavirus statistical data
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