Kawasaki Robotics //treeforeurope.com/eu-africa/ Tue, 24 Jun 2025 04:19:42 +0000 en-GB hourly 1 //www.altis-dxp.com/?v=6.8.4 //treeforeurope.com/tachyon/sites/5/2022/02/cropped-site-icon.png?fit=32%2C32 Kawasaki Robotics //treeforeurope.com/eu-africa/ 32 32 Kawasaki Robotics //treeforeurope.com/eu-africa/blog/ready-for-a-robot-integration-insights-for-welding-applications/ Mon, 10 Mar 2025 05:27:20 +0000 urn:uuid:f7fb1a01-9e34-444b-b2b4-62f2d986f91b By Brandon Day, Senior Engineer Robotic Metal Fabrication

Robot integration in many welding applications makes sense, given that welding is not conducive to pleasant working conditions for humans. Not only is it a hot and dirty process, but it also emits unhealthy chemicals such as hexavalent chromium when welding stainless steels and other chromium-containing material. Although these health concerns might seem like enough reason to implement a robot, there are other considerations.

To ensure that robot automation is beneficial for an application, Kawasaki Robotics North America recommends evaluating these four questions for the best return on investment:

1. What is the part size?

Small parts that fit inside the welding cell are ideal for robot welding, but larger sized parts are possible.

2. How large is the volume size?

Best case scenario for robot welding is high volume (thousands of parts per day, hundreds of thousands per year). Also, consider whether the application includes volumes high enough to create the return that is necessary to pay off the machine in a respectable amount of time.

3. Are tolerances tight enough?

Tight tolerances on parts are necessary to provide accurate repeatability. The robot welds on the exact same spot every time; therefore, too much tolerance will cause the weld to miss often.
Kawasaki robot spot welding sedan

4. Is manpower an issue?

A worker shortage might demand robot integration in a welding application.

Although robots offer accuracy, repeatability and labor and cost savings, human welders are still a necessity. Their knowledge of the art 鈥?including the ins and outs such as torch angles, gasses, metrology, metallurgy 鈥?is required to properly program robots for these applications. Because every major manufacturer has its own welding specifications, it is key for every facility
that performs welding to have a specialist on staff who can interpret these different specs.

Robot Programming Made Easy

When robots are added to an application, instead of doing the welding themselves, welders can learn the job of programming the robots. Progressing into this higher skillset leads to not only a higher paying position where a welder is running multiple robot cells, but also a healthier work environment.

Kawasaki Robotics North America makes it easy for anyone with basic welding knowledge, including engineers, technicians and programmers, to learn robot welding programming quickly and thoroughly. The robot provider offers a three-and-a-half-day training course, 鈥淎rc Operations and Programming,鈥?at its training center in Wixom, Michigan.

The class is designed for basic robot and 168网开奖查询记录结果:arc welding operations, programming and safety training. It includes topics such as operating controls, selecting proper menus for programming, positioning the robot by use of pendant control, and program creation procedures and modification techniques. Upon course completion, students will know how to run the robot system in both manual and automatic modes, create a block step program using weld conditions, teach a path using weld and non-weld steps, and modify programs.

]]> Kawasaki Robotics //treeforeurope.com/eu-africa/blog/human-ingenuity-kippei-matsudas-journey-from-nfl-ai-competition-winner-to-ai-solution-developer-at-kawasaki-robotics/ Fri, 28 Feb 2025 05:51:39 +0000 urn:uuid:dd1bdea5-5eab-4c59-be82-0d70cf076617

Kippei Matsuda: Robot Technology Development Department, System Technology Development Center, Technology Development Division. Dr. (Engineering)

Describe the NFL challenge, the solution, and what made it exciting?

The competition aimed to identify player collisions, with more than 1,000 data scientists worldwide competing to build an accurate solution using NFL game footage and sensor information. American Football is known to be one of the toughest, physically demanding sports in the world involving high-impact contact, and although players wear protective gear, injuries are prevalent. Head collisions, in particular, often result in serious injuries or disabilities, and it’s been challenging to find ways to reduce the impact of collisions along with effective treatment after an injury occurs. If we could accurately identify which players suffered head impacts during a match, we could effectively administer treatment and advance the research into the effects of helmets and how to mitigate the impact. Doing this type of research manually would be extremely time-consuming, so the NFL held this competition to use AI technology to solve this problem.

The key to winning this competition was the successful integration of two distinct data types, video and sensor information. We utilized video images to identify player collisions and estimated player positions using sensors attached to the players. The development of an AI system that meticulously analyzed and predicted even the smallest changes in player position and posture, such as crouching or falling, set us apart. This innovation significantly improved our accuracy compared to other participants. As a result, our processing speed was 83 times faster than manual operation, and tasks that took 3-4 days could now be completed in just 2 hours, which was greatly appreciated.

Kippei Matsuda
In general, AI image analysis involves detecting objects in images, but in this competition, we had to consider the three-dimensional (3D) positions of the players on the field on the screen, which was an exciting challenge that we had never done before. It is difficult to analyze data because you have to look at it over and over again, but the NFL video was so powerful that I enjoyed watching it over and over again. Thanks to this, by the end of the competition, I was able to imagine the players’ movements just by looking at the titles of the videos.

What made you participate in the NFL challenge & what were the results?

It all started with me thinking it could be part of my studies. Theoretical aspects can be learned from books and other sources, but knowing how to use actual data and run a simulation is difficult. As a developer, touching and analyzing data and repeating trial and error is essential. I was attracted by the competition as it provided materials that led to practical learning.

“Participating in the competition allowed me to put AI development into practice”

Kippei Matsuda
Honestly, I never felt the competition itself was hard; what was difficult was finding time for my studies and family, as worked on this during my personal time. When I was playing with my children at the park, I would suddenly think, “Maybe I could do that part this way,” and it would bother me. It was hard to relax. I was on the train, and I was shaking. Since the start of this project, I did not think I could win, but when I did, I was thrilled. I have no complaints about winning and was happy to share the news with everyone around me. I could hardly get any work done that day!

How do you work with AI vision today at Kawasaki Robotics?

I am currently involved in developing products that utilize AI vision specifically for robotics. AI analyzes images captured by cameras and processes them in various ways; for example, in our depalletization solution, it processes images of the product that need to be unloaded. Depalletizing solutions are used to improve unloading efficiency at distribution centers and factories. The Depalletizing Solution is equipped with 3D AI vision and is capable of highly sophisticated analysis of the cargo it handles.
Kawasaki Robotics depalletizing solution is equipped with 3D AI vision and is capable of highly sophisticated analysis of the cargo it handles. By specializing in unloading, we have achieved high performance at a low cost.Kippei Matsuda
Manual unloading is very costly and time-consuming, and conventional robotic solutions are not flexible enough to handle the work. In this respect, our depalletizing solution has succeeded in increasing the accuracy and speed of automated unloading operations compared to conventional solutions.
For example, with conventional robotic systems solutions, all package sizes and shapes must be registered before being picked. If packages of unregistered shapes make it into the workflow, they cannot be processed. Our depalletizing solution however requires only the smallest and largest sizes to be registered, and all packages can be processed. In conventional robotic systems the robot needs to know the correct size and shape of the product being handled. A camera is required in order to recognize and confirm the product size and shape. If it’s confirmed as correct the robot arm will pick the product, to teach the system this is a time-consuming process.
Our depalletizing solution requires no prior product registration other than the minimum and maximum dimensions, significantly reducing teaching time.Kippei Matsuda
The main reason why Kawasaki Robotics’ depalletization solution has been successful is that we pair low-cost camera hardware with our robust AI software to process complex product images without a bunch of ad-ons. We achieved a high-performing and easy-to-use solution by focusing on developing a depalletizing system, and by narrowing down the functions, we achieved better cost performance than other companies’ products, making it easier for companies to introduce the product.
Mr. Himekawa, leader of the product development (belongs to General-Purpose System Section 2, General-Purpose System Department, Robot Division)

How will depalletizing and AI solutions continue to develop?

I believe that the efficiency of depalletizing solutions will increase as more and more data is collected and AI learning progresses. For example, one of the difficulties in developing a depalletizing solution was the strings and tapes on the surface of packages. When AI sees them, it may mistake the strings for the boundaries of the cardboard. It may then decide that the box is smaller than it actually is and take it by mistake. However, if the system is used at various sites in the future and data is collected, it will be able to learn multiple variations of packages. Then, even if a box has strings, tape, or stickers, the robot can unload it appropriately based on its past experience without being misled. Collecting a lot of good-quality data for the robot will be necessary. I also think that AI, called the infrastructure model, will be key. One example is ChatGPT, which has been attracting much attention in recent years. Since the underlying model is trained based on a large amount of data, it has a very high recognition capability, a kind of common sense. Fundamental models can handle a variety of information, including text, images, and sound, and have the potential to dramatically expand the use of robots, not only in logistics. In the future, I would like to expand the use of AI by making good use of data and the basic model.

Click here for Depalletizing Solution product page(Japanese)

]]> Kawasaki Robotics //treeforeurope.com/eu-africa/blog/story_1/ Mon, 06 Dec 2021 12:00:00 +0000 urn:uuid:9e8e0fb9-da44-43c4-a0bd-dc5d9d953808 Illustration of industrial robots: duAro and R series But when it comes to actually implementing a robot, a lot of questions arise. “What tasks can you leave to the robot?” “What steps do I need to take?” “Who should I talk to in the first place?” Here are some of the best ways to take industrial robots from concept to reality.

Consult with Professionals

You’re starting at square one: No robots have been introduced yet. You are interested in automation, but not sure which tasks to automate. In the beginning, it can be difficult to judge whether a particular task can, or should, be automated or not. In this situation, there are resources to give you peace of mind when introducing a new robot system. In many cases, a specialized engineering firm called Robotic System Integrator (robot SIer) is responsible for planning, designing, and deploying robotic systems. In general, the SIer interacts between the user and the robot manufacturer, acting as a connection between them, and leads the way to the system installation. Kawasaki is a valuable robot maker that can even play the role of a robot system integrator.
Illustration of a situation where you are interested in automation but don't know which tasks to automate.
Let’s take a look at the flow from the introduction of robots to installation. (An example of a basic flow is shown in Fig. 1.) In the early stages, the system integrator will conduct preliminary meetings and field observations to gain a better understanding. It’s important to know what the end-users are looking for and what’s going on in the industry, as well as basic requirements like budget, schedule, cycle time requirements, specs, variety, workspace, etc. Building a robotic system is a collaborative effort between the end-user, system integrator, and robot manufacturer, and a thorough understanding of the requirements is essential for success.

Create a robot-conducive environment

Next, you need to understand whether or not automation should be used. Robots are better at some tasks than others. For example, it is easy to accomplish repetitive tasks that require high degrees of accuracy, or dull, dirty, and dangerous jobs that aren’t ideal for humans to execute. But when it comes to complex applications requiring human senses such as sight, delicate touch, smell, and taste, extra equipment and sensors may be required, which can make a system more complicated and expensive. It is important to consider whether a robot is really suitable for the process you’re thinking of automating and whether a robot can demonstrate its power, productivity, and cost-effectiveness. Even in the common case of partial improvement of production processes, the first step is to sort out the tasks that should be performed by humans and the processes that should be performed by robots, taking into account the above viewpoints.

After narrowing down the processes to be automated by the robot, work elements are disassembled in a way that makes sense for the robot. For example, a person might think of a task the following way: Remove the screw and place it on the product on the jig. When you finish tightening, put the finished product in the next box. But in the case of a robot, it is necessary to subdivide each task:
Step 1: Remove the screw
Step 2: Place the product on the jig
Step 3: Place screw in the designated location
Step 4: Tighten screw
Step 5: Pick the finished product
Step 6: Place finished product in box

An illustration of how to break down the work elements so that they are easy for a robot to understand
At this point, it’s easy to overlook the details of the human worker’s movements. For example, turning the parts inside out when placing them on a rack, visually checking the product for foreign objects, or tapping the surface to check the sealing performance are all simple but important movements. Once a robot is made to do the detailed work that the workers are doing almost subconsciously, it is necessary to construct a system not only for programming each and every operation but also for linking the tools used and the processes before and after.

At the same time, it is essential to create an environment in which robots can operate.
For example, if there is no storage space for equipment needed before and after the automated process, there is no problem in the operating space of the robot itself, but the process could be delayed. It is very important to design while imagining not only an automated process but also keeping in mind a realistic process flow such as whether this equipment is smoothly linked to the tasks that come before and directly follow that process. This requires a macroscopic perspective with a bird’s-eye view of how to smoothly pass complex elements such as workers, robots, parts, products, space, and time from upstream to downstream.

A macro-level diagram showing how to smoothly transfer complex elements such as workers, robots, parts, products, space, and time from upstream to downstream.
Figure 1. Robot introduction process chart

Follow Up After Install

Once the details of the system are established from start to finish, a risk assessment is conducted based on the basic design. Once the safety of the robot is confirmed, it goes into the manufacturing and programming of the robot system. After the design drawing of the entire robot system is completed, it goes through manufacturing, testing, delivery, and installation, and then proceeds to the phase of production operation. But even with a successful deployment, the job of a robot manufacturer or system integrator isn’t over. The company has a long relationship with the end-users that use the system, including regular inspections, customer support, and assistance if failures occur. Kawasaki Heavy Industries has a dedicated call center to answer any questions end users have after installation. There is also a 24-hour help desk for problems that arise outside of business hours. Another reason for Kawasaki’s popularity among users is its extensive follow-up and customer service. Kawasaki Heavy Industries’ after-sales service team was established more than 30 years ago. And in 1986, it established Kawasaki Robot Service Co., Ltd. (formerly Kawasaki Robotics, Ltd.), a company specializing in maintenance and after-sales service. Behind the Kawasaki robots, there is always a team of experts close to the robot’s life, from installation to operation, maintenance, and renewal.
Conceptual image of a robot replacing humans
There are many reasons why customers choose Kawasaki as a partner in the introduction of robot systems. One of the reasons for this is that, as a company that started as a manufacturer and has deep roots in this industry, it is fully equipped to support the introduction of robots to customers. For example, the Nishi-Kobe Plant has one of the largest robot showrooms in Japan. There are vertically articulated robots, parallel link robots, clean robots, and even duAro cobots and Successor systems. In the showroom, you will see work environments replicated for each robot type, such as welding, painting, and sorting lunch boxes on a production line. Many users want to implement robots to help combat labor shortages. Or, they may want to increase production efficiency and diversify their product line. Another common reason is to prevent human error and improve product quality, or protect workers from harsh and dangerous work. Every company has its own incentive to think about robots. Industrial robots are certainly the best solution for these problems, but replacing humans and robots is not enough. There’s no such thing as a robot without a professional standing by and supporting from the beginning to the end. Automation systems can only run smoothly when they have both robot system integrators and robot manufacturers.

[Columns]
Introducing Industrial Robots Faster!
Start of K-AddOn operation

Industrial robots cannot work by themselves. They need to connect to peripheral equipment such as grippers and vision systems so the whole system can work. In order to smoothly connect devices made by the various manufacturers, it is necessary to connect and link the respective software types. The K-AddOn platform was launched by Kawasaki to speed up the time it takes to connect the robot to its peripheral equipment and help ensure a smooth deployment. By opening the interface of industrial and collaborative robots made by Kawasaki Heavy Industries to peripheral equipment manufacturers, the robot system integrator and end-user can reduce the verification cost of equipment connection required at the time of installation.
A diagram of K-AddOn, a platform launched by Kawasaki Heavy Industries to shorten the connection time between robots and peripheral devices and support smooth implementation.

K-AddOn

Ready to take the next step toward automation?

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