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ROBOTS IN
AUTOMOTIVE
INDUSTRY
PRESENTED BY
RAHUL R
S2 M.TECH
CIM
ROLL NO 9
Introduction
 Industrial robots have been used in car factories around the world for decades, but
those in use today are more advanced than ever.
 Car manufacturing robots give automotive companies a competitive advantage.
 They improve quality and reduce warranty costs, increase capacity and relieve
bottlenecks and protect workers from dirty, difficult and dangerous jobs.
 Robots are an absolute necessity for car production companies in order to keep up
with competitors due to extremely high demand, the complex nature of the
product, and a lengthy assembly process.
 There is a range of robotic applications within the automotive industry, and each
application is responsible for making a specific process more accurate and
efficient.
2
Robotic Application in Automotive Industry
 Each robotic application within the automotive industry is designed to either
increase productivity, cut down on waste materials, reduce safety risks, or
improve quality standards.
 There is a multitude of ways in which robots can be used in the car manufacturing
process, which can be classified as
 Robotic welding
 Robotic assembly
 Material removal
 Handling and part transfer
 Robotic painting
 Robotic vision
3
Collaborative Robots or COBOTS
 A cobot, or collaborative robot, is a robot intended for direct human robot
interaction within a shared space, or where humans and robots are in close
proximity.
 Cobots can work side-by-side with human workers, improving their output and
consistency and allowing them to support more in-line processes in a single work
space.
 There are many collaborative robot applications across all industries. These
include
 Assembly
 Dispensing
 Finishing
 Material Handling
 Welding
 Material Removal
 Quality Inspections
4
Fig. 1: Cobots in action
5
Fig.2: TM 12 (Omron) Fig.3: UR10e (Universal Robots)
• Reach (mm): 1300
• Maximum payload (kg): 12
• Maximum speed (m/s): 1.3
• Reach (mm): 1300
• Maximum Payload (kg): 12.5 kg
Robotic Welding (Spot & Arc)
 A car requires a lot of welding before it is completed. Welding involves high
temperatures which expose workers to health risks.
 Robots are an excellent tool for automating welding processes in car production.
Robotic welding gives a well-finished product which appeals to the eye.
 Common uses include arc welding and resistance spot welding.
 Large robots with high payload capabilities and long reach can spot weld car body
panels; while smaller robots weld subassemblies such as brackets and mounts.
 Robotic MIG and TIG arc welding position the torch in the same orientation on
every cycle, and repeatable speed and arc gap ensure every fabrication is welded
to the same high standard.
6
 The top robot models used by automotive manufacturers include
 Motoman UP130, UP6, UP20
 FANUC R-2000 series
 FANUC ARC Mate series
 ABB
 KUKA
7
Fig.3: KUKA Spot Welding Robots in action
8
• Axes: 6
• Payload: 5kg
• H-Reach: 1412mm
• Repeatability: ±0.1mm
• Robot Mass: 127kg
• Structure: Articulated
• Mounting: Floor, Inverted
Fig.4: KUKA KR 5 Arc
Fig.5: FANUC Robot R-2000iD/210FH
• Axes: 6
• Max. payload : 210kg
• Motion range (X, Y): 2605mm, 3315mm
• Repeatability: ±0.05mm
• Mass: 1150kg
• Installation: Floor
Robotic Assembly
 The term assembly is defined as fitting together of two or more discrete pats to form a
new subassembly or product.
 Assembly operations involve a considerable amount of handling and orientation of
parts to mate them properly.
 In automotive manufacturing plants, When it comes to putting parts together, assembly
line robots occupy a sweet spot between humans and dedicated or “hard” automation.
 An assembly robot moves faster and with greater precision than a human, and an off-
the-shelf tool can be installed and commissioned quicker than special-purpose
equipment.
 Repetitive but important tasks such as wheel mounting, screw driving, and windscreen
installation can be carried out at speed with highly accurate automotive robots.
 Robotics also improves the reliability of the assembly line, as the production cycle can
run round the clock and the production schedule is also consistent each day.
9
 Robotic assembly systems come in three configurations:
 six-axis articulated arms
 four-axis “SCARA” robots
 modern “Delta” configuration
 Assembly automation can be specified with vision systems and force sensing.
 Vision can guide a robot to pick up a component from a conveyor, reducing or even
eliminating the need for precise location, and visual serving lets a robot rotate or
translate one piece to make it fit with another.
 Force sensing helps with part assembly operations like insertion, giving the robot
controller feedback about how well parts are going together or how much force is
being applied.
10
11
Fig.6 : FANUC SR-12iA Environmental
Option SCARA Robot
• Axes : 4
• Maximum Payload : 12 kg
• Reach : 900 mm
• Repeatability : ± 0.015 mm (J1,J2), ± 0.01 mm
(J3), ± 0.005° (J4)
• Mounting : Floor / Wall
Fig.7 : FANUC M-2iA/3A Delta Robot
• Axes : 6
• Maximum Payload : 3 kg
• Reach : 800 mm
• Repeatability : ± 0.1 mm
• Mounting : Ceiling
Material Removal Robots
 Robots can follow a complex path multiple times without failing, making it the
perfect tool for cutting and trimming jobs.
 Examples include cutting fabrics such as headliners, trimming flash from plastic
moldings and die castings, and polishing molds.
 Light robots with force-sensing technology are better-suited to this type of work.
Force-sensing technology lets the robot maintain constant pressure against a
surface in applications like these.
 Robotic material removal doesn't have to mean the robot holds the tool. Many
applications work better when the robot holds the workpiece taking it to a fixed
tool.
 This increases flexibility by allowing the robot to perform multiple operations.
12
13
Fig.8 : Motoman GP110B
• Axes : 7
• Maximum Payload : 110 kg
• Repeatability : 0.04 mm
• H Reach : 2236 mm
• V Reach : 3792 mm
• Weight : 790 kg
Fig.9 : Kawasaki ZX130S
• Axes : 6
• Maximum Payload : 130 kg
• Repeatability : ±0.3 mm
• Maximum Reach : 2651 mm
• Mounting : Floor
Handling and Part Transfer
 The handling of car parts is often one of the most dangerous parts of a production
line for human employees, due to external risk factors such as molten metal, sharp
edges or trip hazards.
 Risks can also include repetitive strain injuries or back problems due to lifting
significant weight or lifting at awkward angles.
 Robots are the perfect solution, as they can be exposed to these hazards with
relatively little risk to the machinery itself.
 This both speeds up the handling process, as well as reducing accident rates and
injury claims by removing the need for humans to be present in dangerous
environments.
14
15
Fig.10 : Kawasaki MG10HL
• Axes : 6
• Maximum Payload : 1000 kg
• Maximum Reach : 4005 mm
• Repeatability : ±0.1 mm
• Mounting : Floor
Fig.11 : Motoman GP250
• Axes : 6
• Maximum Payload : 250 kg
• Repeatability : 0.05 mm
• H Reach : 2710 mm
• V Reach : 3490 mm
Robotic Painting
 Another application for robotics which has been around in the automotive industry
for decades is robotic painting.
 Early versions of paint robots tended to be hydraulic variants and these may still be
used today, though with lower quality results and more safety risk.
 Newer variants are electronic, highly accurate, and produce a much more consistent
coating to exact requirements.
 Many car factories use paint robots to apply paint across the entire exterior of cars,
and some can even be used to paint the internal furnishings of the vehicle.
 Robots are used to paint all different sized automotive parts because they can help
provide consistent finish from one part to another.
 They are used for large exterior parts like doors, hoods, wheels, or bumpers, and also
used on small interior components like knobs, consoles and glove boxes.
16
17
Fig.12 : Schematic of a typical four layer automotive coating system
18
Fig.13 : Motoman MPX2600
• Axes : 6
• Maximum Payload : 15 kg
• H Reach : 2000 mm
• V Reach : 3643 mm
• Repeatability : ± 0.2 mm
• Mounting : Floor, Wall, Ceiling
Fig.14 : Fanuc P-250iB
• Axes : 6
• Maximum Payload : 15 kg
• Repeatability : ± 0.2 mm
• Reach : 2800 mm
• Mounting : Floor, Ceiling, Wall, Shelf
Robotic Vision
 Robotic vision often referred to as “machine vision” is also a key tool for quality control,
or vision checking, as it can flag finished products which do not meet certain standards or
a specification for review, preventing sub-par products from reaching the point of sale.
 This can include colour analysis, blob detection, pattern recognition, edge detection, and
much more.
 The robot works using an algorithm and a camera which aims to match pictures of parts
that the robot will interact with. If the algorithm cannot match the picture, the part is not
accepted and the robot will not carry out any work on it.
 Robotic vision is highly valued in the automotive industry as it allows individual car parts
to be fitted together with exact precision to create a higher quality product.
19
 Machine Vision systems have wide-ranging uses in the automotive industry. They can
be employed for automated assembly, material handling, and quality control. Some of
the applications are:
1. Robotics
Advanced machine vision systems enable a robot to locate parts or subassemblies that it is
working on. They locate parts that the robot can pick up and send information to the robot
regarding the right position and orientation to affix the parts. They can prove invaluable in
improving speed and accuracy in the assembly lines.
2. Dimensional Gauging
Manual measurement processes are time-consuming and prone to error. Machine vision systems
with their 2D and 3D recognition capabilities are adept at measuring dimensions of lines, arcs,
angles, and tolerances. They can be configured to determine curved surfaces, sequences, heights,
etc. consistently and accurately.
20
3. Assembly Verification
Advanced machine vision systems can be fed with images of parts and patterns which they can
recognize and verify in 3D in the actual parts and sub-assemblies and report inaccuracies. This
increases the reliability of quality control significantly.
4. Flaw Detection
One of the best ways to reduce product recalls is to identify defects before vehicles leave the
assembly line. Machine vision systems play a crucial role here. They use powerful pattern
recognition capabilities to find mistakes like scratches, dents, misalignments, mistakes in welding
specifications, and other imperfections. This improves quality and saves time, effort, material, and
money spent on rework.
5. Print Verification
Automobiles have different types of marking methods across various parts. Machine vision systems
use optical character verification (OCV) methods to verify if parts and components are labeled and
marked correctly. They can identify and verify character density, inking, laser etching, stamping,
engraving on labels, and markings on cast parts.
21
6. Code Reading
Automotive companies can use machine vision systems to read barcodes, 2D data matrix codes, and
characters printed on parts, labels, and packages. This sophisticated technology helps in identifying
parts and patterns and provides unit-level data for error-proofing, process control, and track quality-
control metrics.
22
Fig.15 : Robotic Inspection of Automotive parts
Thank You!

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Robots in automotive industry

  • 2. Introduction  Industrial robots have been used in car factories around the world for decades, but those in use today are more advanced than ever.  Car manufacturing robots give automotive companies a competitive advantage.  They improve quality and reduce warranty costs, increase capacity and relieve bottlenecks and protect workers from dirty, difficult and dangerous jobs.  Robots are an absolute necessity for car production companies in order to keep up with competitors due to extremely high demand, the complex nature of the product, and a lengthy assembly process.  There is a range of robotic applications within the automotive industry, and each application is responsible for making a specific process more accurate and efficient. 2
  • 3. Robotic Application in Automotive Industry  Each robotic application within the automotive industry is designed to either increase productivity, cut down on waste materials, reduce safety risks, or improve quality standards.  There is a multitude of ways in which robots can be used in the car manufacturing process, which can be classified as  Robotic welding  Robotic assembly  Material removal  Handling and part transfer  Robotic painting  Robotic vision 3
  • 4. Collaborative Robots or COBOTS  A cobot, or collaborative robot, is a robot intended for direct human robot interaction within a shared space, or where humans and robots are in close proximity.  Cobots can work side-by-side with human workers, improving their output and consistency and allowing them to support more in-line processes in a single work space.  There are many collaborative robot applications across all industries. These include  Assembly  Dispensing  Finishing  Material Handling  Welding  Material Removal  Quality Inspections 4 Fig. 1: Cobots in action
  • 5. 5 Fig.2: TM 12 (Omron) Fig.3: UR10e (Universal Robots) • Reach (mm): 1300 • Maximum payload (kg): 12 • Maximum speed (m/s): 1.3 • Reach (mm): 1300 • Maximum Payload (kg): 12.5 kg
  • 6. Robotic Welding (Spot & Arc)  A car requires a lot of welding before it is completed. Welding involves high temperatures which expose workers to health risks.  Robots are an excellent tool for automating welding processes in car production. Robotic welding gives a well-finished product which appeals to the eye.  Common uses include arc welding and resistance spot welding.  Large robots with high payload capabilities and long reach can spot weld car body panels; while smaller robots weld subassemblies such as brackets and mounts.  Robotic MIG and TIG arc welding position the torch in the same orientation on every cycle, and repeatable speed and arc gap ensure every fabrication is welded to the same high standard. 6
  • 7.  The top robot models used by automotive manufacturers include  Motoman UP130, UP6, UP20  FANUC R-2000 series  FANUC ARC Mate series  ABB  KUKA 7 Fig.3: KUKA Spot Welding Robots in action
  • 8. 8 • Axes: 6 • Payload: 5kg • H-Reach: 1412mm • Repeatability: ±0.1mm • Robot Mass: 127kg • Structure: Articulated • Mounting: Floor, Inverted Fig.4: KUKA KR 5 Arc Fig.5: FANUC Robot R-2000iD/210FH • Axes: 6 • Max. payload : 210kg • Motion range (X, Y): 2605mm, 3315mm • Repeatability: ±0.05mm • Mass: 1150kg • Installation: Floor
  • 9. Robotic Assembly  The term assembly is defined as fitting together of two or more discrete pats to form a new subassembly or product.  Assembly operations involve a considerable amount of handling and orientation of parts to mate them properly.  In automotive manufacturing plants, When it comes to putting parts together, assembly line robots occupy a sweet spot between humans and dedicated or “hard” automation.  An assembly robot moves faster and with greater precision than a human, and an off- the-shelf tool can be installed and commissioned quicker than special-purpose equipment.  Repetitive but important tasks such as wheel mounting, screw driving, and windscreen installation can be carried out at speed with highly accurate automotive robots.  Robotics also improves the reliability of the assembly line, as the production cycle can run round the clock and the production schedule is also consistent each day. 9
  • 10.  Robotic assembly systems come in three configurations:  six-axis articulated arms  four-axis “SCARA” robots  modern “Delta” configuration  Assembly automation can be specified with vision systems and force sensing.  Vision can guide a robot to pick up a component from a conveyor, reducing or even eliminating the need for precise location, and visual serving lets a robot rotate or translate one piece to make it fit with another.  Force sensing helps with part assembly operations like insertion, giving the robot controller feedback about how well parts are going together or how much force is being applied. 10
  • 11. 11 Fig.6 : FANUC SR-12iA Environmental Option SCARA Robot • Axes : 4 • Maximum Payload : 12 kg • Reach : 900 mm • Repeatability : ± 0.015 mm (J1,J2), ± 0.01 mm (J3), ± 0.005° (J4) • Mounting : Floor / Wall Fig.7 : FANUC M-2iA/3A Delta Robot • Axes : 6 • Maximum Payload : 3 kg • Reach : 800 mm • Repeatability : ± 0.1 mm • Mounting : Ceiling
  • 12. Material Removal Robots  Robots can follow a complex path multiple times without failing, making it the perfect tool for cutting and trimming jobs.  Examples include cutting fabrics such as headliners, trimming flash from plastic moldings and die castings, and polishing molds.  Light robots with force-sensing technology are better-suited to this type of work. Force-sensing technology lets the robot maintain constant pressure against a surface in applications like these.  Robotic material removal doesn't have to mean the robot holds the tool. Many applications work better when the robot holds the workpiece taking it to a fixed tool.  This increases flexibility by allowing the robot to perform multiple operations. 12
  • 13. 13 Fig.8 : Motoman GP110B • Axes : 7 • Maximum Payload : 110 kg • Repeatability : 0.04 mm • H Reach : 2236 mm • V Reach : 3792 mm • Weight : 790 kg Fig.9 : Kawasaki ZX130S • Axes : 6 • Maximum Payload : 130 kg • Repeatability : ±0.3 mm • Maximum Reach : 2651 mm • Mounting : Floor
  • 14. Handling and Part Transfer  The handling of car parts is often one of the most dangerous parts of a production line for human employees, due to external risk factors such as molten metal, sharp edges or trip hazards.  Risks can also include repetitive strain injuries or back problems due to lifting significant weight or lifting at awkward angles.  Robots are the perfect solution, as they can be exposed to these hazards with relatively little risk to the machinery itself.  This both speeds up the handling process, as well as reducing accident rates and injury claims by removing the need for humans to be present in dangerous environments. 14
  • 15. 15 Fig.10 : Kawasaki MG10HL • Axes : 6 • Maximum Payload : 1000 kg • Maximum Reach : 4005 mm • Repeatability : ±0.1 mm • Mounting : Floor Fig.11 : Motoman GP250 • Axes : 6 • Maximum Payload : 250 kg • Repeatability : 0.05 mm • H Reach : 2710 mm • V Reach : 3490 mm
  • 16. Robotic Painting  Another application for robotics which has been around in the automotive industry for decades is robotic painting.  Early versions of paint robots tended to be hydraulic variants and these may still be used today, though with lower quality results and more safety risk.  Newer variants are electronic, highly accurate, and produce a much more consistent coating to exact requirements.  Many car factories use paint robots to apply paint across the entire exterior of cars, and some can even be used to paint the internal furnishings of the vehicle.  Robots are used to paint all different sized automotive parts because they can help provide consistent finish from one part to another.  They are used for large exterior parts like doors, hoods, wheels, or bumpers, and also used on small interior components like knobs, consoles and glove boxes. 16
  • 17. 17 Fig.12 : Schematic of a typical four layer automotive coating system
  • 18. 18 Fig.13 : Motoman MPX2600 • Axes : 6 • Maximum Payload : 15 kg • H Reach : 2000 mm • V Reach : 3643 mm • Repeatability : ± 0.2 mm • Mounting : Floor, Wall, Ceiling Fig.14 : Fanuc P-250iB • Axes : 6 • Maximum Payload : 15 kg • Repeatability : ± 0.2 mm • Reach : 2800 mm • Mounting : Floor, Ceiling, Wall, Shelf
  • 19. Robotic Vision  Robotic vision often referred to as “machine vision” is also a key tool for quality control, or vision checking, as it can flag finished products which do not meet certain standards or a specification for review, preventing sub-par products from reaching the point of sale.  This can include colour analysis, blob detection, pattern recognition, edge detection, and much more.  The robot works using an algorithm and a camera which aims to match pictures of parts that the robot will interact with. If the algorithm cannot match the picture, the part is not accepted and the robot will not carry out any work on it.  Robotic vision is highly valued in the automotive industry as it allows individual car parts to be fitted together with exact precision to create a higher quality product. 19
  • 20.  Machine Vision systems have wide-ranging uses in the automotive industry. They can be employed for automated assembly, material handling, and quality control. Some of the applications are: 1. Robotics Advanced machine vision systems enable a robot to locate parts or subassemblies that it is working on. They locate parts that the robot can pick up and send information to the robot regarding the right position and orientation to affix the parts. They can prove invaluable in improving speed and accuracy in the assembly lines. 2. Dimensional Gauging Manual measurement processes are time-consuming and prone to error. Machine vision systems with their 2D and 3D recognition capabilities are adept at measuring dimensions of lines, arcs, angles, and tolerances. They can be configured to determine curved surfaces, sequences, heights, etc. consistently and accurately. 20
  • 21. 3. Assembly Verification Advanced machine vision systems can be fed with images of parts and patterns which they can recognize and verify in 3D in the actual parts and sub-assemblies and report inaccuracies. This increases the reliability of quality control significantly. 4. Flaw Detection One of the best ways to reduce product recalls is to identify defects before vehicles leave the assembly line. Machine vision systems play a crucial role here. They use powerful pattern recognition capabilities to find mistakes like scratches, dents, misalignments, mistakes in welding specifications, and other imperfections. This improves quality and saves time, effort, material, and money spent on rework. 5. Print Verification Automobiles have different types of marking methods across various parts. Machine vision systems use optical character verification (OCV) methods to verify if parts and components are labeled and marked correctly. They can identify and verify character density, inking, laser etching, stamping, engraving on labels, and markings on cast parts. 21
  • 22. 6. Code Reading Automotive companies can use machine vision systems to read barcodes, 2D data matrix codes, and characters printed on parts, labels, and packages. This sophisticated technology helps in identifying parts and patterns and provides unit-level data for error-proofing, process control, and track quality- control metrics. 22 Fig.15 : Robotic Inspection of Automotive parts