The 5 Most Common Types of Fixed Industrial Robots

The International Federation of Robotics (IFR) defines five types of fixed industrial robots: Cartesian/Gantry, SCARA, Articulated, Parallel/Delta and Cylindrical (mobile robots are not included in the “fixed” robot category). These types are generally classified by their mechanical structure, which dictates the ways they can move.

Based on the current market situation and trends, we have modified this list by removing Cylindrical robots and adding Power & Force Limited Collaborative robots. Cylindrical robots have a small, declining share of the market and some industry analysts predict that they will be completely replaced by SCARA robots, which can cover similar applications at higher speed and performance. On the other hand, use of collaborative robots has grown rapidly since their first commercial sale by Universal Robots in 2008. This is why collaborative robots are on our list and cylindrical/spherical robots are not.

Therefore, our list of the top five industrial robot types includes:

    • Articulated
    • Cartesian/Gantry
    • Parallel/Delta
    • SCARA
    • Power & Force Limited Collaborative robots

These five common types of robots have emerged to address different applications, though there is now some overlap in the applications they serve. And range of industries where they are used is now very wide. The IFR’s 2021 report ranks electronics/electrical, automotive, metal & machinery, plastic and chemical products and food as the industries most commonly using fixed industrial robots. And the top applications identified in the report are material/parts handling and machine loading/unloading, welding, assembling, cleanrooms, dispensing/painting and processing/machining.

Articulated robots

Articulated robots most closely resemble a human arm and have multiple rotary joints–the most common versions have six axes. These can be large, powerful robots, capable of moving heavy loads precisely at moderate speeds. Smaller versions are available for precise movement of lighter loads. These robots have the largest market share (≈60%) and are growing between 5–10% per year.

Articulated robots are used across many industries and applications. Automotive has the biggest user base, but they are also used in other industries such as packaging, metalworking, plastics and electronics. Applications include material & parts handling (including machine loading & unloading, picking & placing and palletizing), assembling (ranging from small to large parts), welding, painting, and processing (machining, grinding, polishing).

SCARA robots

A SCARA robot is a “Selective Compliance Assembly Robot Arm,” also known as a “Selective Compliance Articulated Robot Arm.” They are compliant in the X-Y direction but rigid in the Z direction. These robots are fairly common, with around 15% market share and a 5-10% per year growth rate.

SCARA robots are most often applied in the Life Sciences, Semiconductor and Electronics industries. They are used in applications requiring high speed and high accuracy such as assembling, handling or picking & placing of lightweight parts, but also in 3D printing and dispensing.

Cartesian/Gantry robots

Cartesian robots, also known as gantry or linear robots, move along multiple linear axes. Since these axes are very rigid, they can precisely move heavy payloads, though this also means they require a lot of space. They have about 15% market share and a 5-10% per year growth rate.

Cartesian robots are often used in handling, loading/unloading, sorting & storing and picking & placing applications, but also in welding, assembling and machining. Industries using these robots include automotive, packaging, food & beverage, aerospace, heavy engineering and semiconductor.

Delta/Parallel robots

Delta robots (also known as parallel robots) are lightweight, high-speed robots, usually for fast handling of small and lightweight products or parts. They have a unique configuration with three or four lightweight arms arranged in parallelograms. These robots have 5% market share and a 3–5% growth rate.

They are often used in food or small part handling and/or packaging. Typical applications are assembling, picking & placing and packaging. Industries include food & beverage, cosmetics, packaging, electronics/ semiconductor, consumer goods, pharmaceutical and medical.

Power & Force Limiting Collaborative robots

We add the term “Power & Force Limiting” to our Collaborative robot category because the standards actually define four collaborative robot application modes, and we want to focus on this, the most well-known mode. Click here to read a blog on the different collaborative modes. Power & Force Limiting robots include models from Universal Robots, the FANUC CR green robots and the YuMi from ABB. Collaborative robots have become popular due to their ease of use, flexibility and “built-in” safety and ability to be used in close proximity to humans. They are most often an articulated robot with special features to limit power and force exerted by the axes to allow close, safe operation near humans or other machines. Larger, faster and stronger robots can also be used in collaborative applications with the addition of safety sensors and special programming.

Power & Force Limiting Collaborative robots have about 5% market share and sales are growing rapidly at 20%+ per year. They are a big success with small and mid-size enterprises, but also with more traditional robot users in a very broad range of industries including automotive and electronics. Typical applications include machine loading/unloading, assembling, handling, dispensing, picking & placing, palletizing, and welding.

Summary­

The robot market is one of the most rapidly growing segments of the industrial automation industry. The need for more automation and robots is driven by factors such as supply chain issues, changing workforce, cost pressures, digitalization and mass customization (highly flexible manufacturing). A broad range of robot types, capabilities and price points have emerged to address these factors and satisfy the needs of applications and industries ranging from automotive to food & beverage to life sciences.

Note: Market share and growth rate estimates in this blog are based on public data published by the International Federation of Robotics, Loup Ventures, NIST and Interact Analysis.

Error-Free Assembly of Medical Components

A SUV and a medical device used in a lab aren’t very similar in their looks, but when it comes to manufacturing them, they have a lot in common. For both, factory automation is used to increase production volume while also making sure that production steps are completed precisely. Read on to learn about some ways that sensors are used in life science manufacturing.

Sensors with switching output

Automation equipment producers are creative builders of specialized machines, as each project differs somehow from previous ones. When it comes to automated processes in the lab and healthcare sectors where objects being processed or assembled are small, miniaturization is required for manufacturing equipment as well.  Weight reduction also plays an important role in this, since objects with a lower mass can be moved quickly with a smaller amount of force. By using light-weight sensors on automated grippers, they can increase the speed of actuator movements.

Conveyor system using photoelectric sensors for object detection

Photoelectric sensors are quite common in automated production because they can detect objects from a distance. Miniaturized photoelectric sensors are more easily placed in a production process that works with small parts. And photoelectric sensors can be used to detect objects that are made of many different types of material.

A common challenge for lab equipment is to detect clear liquids in clear vessels. Click here for a description of how specialized photoelectric sensors face this challenge.

Specialized photoelectric sensors for clear water detection

Image Processing

Within the last several years, camera systems have been used more frequently in the production of lab equipment. They are fast enough for high-speed production processes and support the use of artificial intelligence through interfaces to machine learning systems.

Identification

In any production setting, products, components and materials must be identified and tracked. Both optical identification and RFID technology are suitable for this purpose.

Sample analysis with industrial camera

Optical identification systems use a scanner to read one-dimensional barcodes or two-dimensional data matrix or QR codes and transmit the object information centrally to a database, which then identifies the object. The identification cost per object is pretty low when using a printed label or laser marking on the object.

When data must be stored directly on or with the object itself, often because the data needs to be changed or added to during the production process, RFID (Radio Frequency Identification) is the best choice. Data storage tags come in many different sizes and can store different amounts of data and have other features to meet specific needs. This decentralized data storage has advantages in fast production processes when there is a need for real-time data storage.

Data of RFID tag at pallet are read and written with RFID read/write head and transferred via bus module

There are numerous parallels between automation in the life science sector and general factory automation. While these manufacturing environments both have their own challenges, the primary automation task is the same: find the best sensor for your application requirements. Being able to choose from many types of sensors, with different sizes and characteristics, can make that job a lot easier. For more information about the life sciences industries, visit https://www.balluff.com/en-us/industries/life-science.

Factor 1 sensors make auto production more flexible

Have you ever climbed a mountain with a backpack? Then you understand that the lower the load, the less power is needed and the lower the energy consumption. The same is true for cars. And in regard to electric vehicles, this is even more important: The more weight that can be saved somewhere else, the larger the battery can be, thus increasing the range of the electric car.

Lightweight construction is key for weight reduction. By using a sensible mix of materials, weight can be saved without compromising functionality and safety or drastically increasing costs. High-strength steels or light metals are used for body parts or seat frames. However, this mix of materials has an impact on automotive production when it comes to selecting the sensor technology. Inductive sensors have become an indispensable part of automotive construction; however, they react to different metals. This would mean frequent adjustments during production. Fortunately, we have Factor 1 sensors.

Inductive sensors react to metals. Their task is to detect metal objects without contact. The distance at which the corresponding object can be detected by the respective sensor is called the switching distance. The switching distance for standard inductive sensors depends on the material of the metal. Steel, for example, is detected much better than aluminum or copper. The switching distance can be reduced by up to 70% for non-ferromagnetic materials.

 

To eliminate this problem, Factor 1 sensors were developed. They offer all of the advantages of inductive sensors with the added bonus of having the same switching distance for all metals. This makes them ideally suited for the detection of changing objects (steel, aluminum, brass, copper etc.) and a perfect fit for the production of electric cars or anywhere different types of metals need to be used and identified. And because Factor 1 sensors are magnetic-field resistant, they can be used in areas  with strong electromagnetic fields, such as welding plants.

For more information, visit https://www.balluff.com/local/us/products/product-overview/sensors/inductive-sensors/#/inductive-factor-1-sensors

How flexible inspection capabilities help meet customization needs and deliver operational excellence

As the automotive industry introduces more options to meet the growing complexities and demands of its customers (such as increased variety of trim options) it has rendered challenges to the automotive manufacturing industry.

Demands of the market filter directly back to the manufacturing floor of tier suppliers as they must find the means to fulfill the market requirements on a flexible industrial network, either new or existing. The success of their customers is dependent on the tier supplier chain delivering within a tight timeline. Whereby, if pressure is applied upon that ecosystem, it will mean a more difficult task to meet the JIT (just in time) supply requirements resulting in increased operating costs and potential penalties.

Meeting customer requirements creates operational challenges including lost production time due to product varieties and tool change time increases. Finding ways to simplify tool change and validate the correct components are placed in the correct assembly or module to optimize production is now an industry priority. In addition, tracking and traceability is playing a strong role in ensuring the correct manufacturing process has been followed and implemented.

How can manufacturing implement highly flexible inspection capabilities while allowing direct communication to the process control network and/or MES network that will allow the capability to change inspection characteristics on the fly for different product inspection on common tooling?

Smart Vision Inspection Systems

Compact Smart Vision Inspection System technology has evolved a long way from the temperamental technologies of only a decade ago. Systems offered today have much more robust and simplistic intuitive software tools embedded directly in the Smart Vision inspection device. These effective programming cockpit tools allow ease of use to the end user at the plant providing the capability to execute fast reliable solutions with proven algorithm tools. Multi-network protocols such as EthernetIP, ProfiNet, TCP-IP-LAN (Gigabit Ethernet) and IO-LINK have now come to realization. Having multiple network capabilities delivers the opportunity of not just communicating the inspection result to the programmable logic controller (via process network) but also the ability to send image data independent of the process network via the Gigabit Ethernet network to the cloud or MES system. The ability to over-lay relevant information onto the image such as VIN, Lot Code, Date Code etc. is now achievable.  In addition, camera housings have become more industrially robust such as having aluminum housings with an ingress protection rating of IP67.

Industrial image processing is now a fixture within todays’ manufacturing process and is only growing. The technology can now bring your company a step closer to enabling IIOT by bringing issues to your attention before they create down time (predictive maintenance). They aid in reaching operational excellence as they uncover processing errors, reduce or eliminate scrap and provide meaningful feedback to allow corrective actions to be implemented.

Operational Excellence – How Can We Apply Best Practices Within the Weld Shop?

Reducing manufacturing costs is absolutely a priority within the automotive manufacturing industry. To help reduce costs there has been and continues to be pressure to lower MRO costs on high volume consumables such as inductive proximity sensors.

Traditionally within the MRO community, the strategy has been to drive down the unit cost of components from their suppliers year over year to ensure reduce costs as much as possible. Of course, cost optimization is important and should continue to be, but factors other than unit cost should be considered. Let’s explore some of these as it would apply to inductive proximity sensors in the weld shop.

Due to the aggressive manufacturing environment within weld cell, devices such as inductive proximity sensors are subjected to a variety of hostile factors such as high temperature, impact damage, high EMF (electromagnetic fields) and weld spatter. All of these factors drastically reduce the life of these devices.

There are  manufacturing costs associated with a failed device well beyond that of the unit cost of the device itself. These real costs can be and are reflected in incremental premium costs such as increased downtime (both planned and unplanned),  poor asset allocation, indirect inventory, expedited freight, outsourcing costs, overtime, increased manpower, higher scrap levels, and sorting & rework costs. All of these factors negatively affect a facility’s Overall Equipment Effectiveness (OEE).

Root Cause

In selection of inductive proximity sensors for the weld manufacturing environment there are root cause misconceptions and poor responses to the problem. Responses include: leave the sensor, mounting and cable selection up to the machine builder; bypass the failed sensor and keep running production until the failed device can be replaced; install multiple vending machines in the plant to provide easier access to spare parts (replace sensors often to reduce unplanned downtime);  and the sensors are going to fail anyway so just buy the cheapest device possible.

None of these address the root cause of the failure. They mask the root cause and exacerbate the scheduled and unscheduled downtime or can cause serious part contamination issues down stream, resulting in enormous penalties from their customer.

So, how can we implement a countermeasure to help us drive out these expensive operating costs?

  • Sensor Mounting – Utilize a fixed mounting system that will allow a proximity sensor to slide into perfect mounting position with a positive stop to prevent the device from being over extended and being struck by the work piece. This mounting system should have a weld spatter protective coating to reduce the adherence of weld spatter. This will also provide extra impact protection and a thermal barrier to further assist in protecting the sensing device asset.
  • The Sensor – Utilize a robust fully weld protective coated stainless steel body and face proximity sensor. For applications with the sensor in an “on state” during the weld cycle and/or to detect non-ferrous utilize a proper weld protective coated Factor 1 (F1) device.
  • Cabling – A standard cable will not withstand a weld environment such as MIG welding. Even a cable with protective tubing can have open areas vulnerable for weld berries to land and cause burn through on the cables resulting in a dead short. A proper weld sensor cord set with protective coating on the lock nut, high temp rated and weld resistant overmold to a weld resistant jacketed cable should be used.

By implementing a weld best practice total solution as described above, you will realize significant increases in your facilities OEE contributing to the profitability and sustainability of your organization.

Ask these 3 simple questions:

1) What is the frequency of failure

2) What is the Mean Time To Repair (MTTR)

3) What is the cost per minute of downtime.

Once you have that information you will know with your own metrics  what the problem is costing your facility by day/month/year. You may be surprised to see how much of a financial burden these issues are costing you. Investing in the correct best practice assets will allow you to realize immediate results to boost your company OEE.