German technical officials see industry 4.0, Internet of things and big data

Source：Updated：2017-07-18 09:11:26

The Klingelnberg Group, which dates back to 1863, has been passed down to the seventh generation of the family. Mr. Klingberg acquired Oerlikon Geartec AG in 1993. In 1997, it successfully cracked the stem cutting technology of spiral bevel gear; In 2008, the world's most advanced large-scale bevel gear production equipment was put into operation; In 2012, it acquired the core business of H? Fler Maschinenbau GmbH. 
 
Today, klingberg has become the world's leading r&d and manufacturing company for bevel gears, cylindrical gears, gear measurement and customized high-precision gear parts. Klingberg's lead comes from refined products and service quality, absolute reliability and strict delivery time. 
 
Dr Hartmuth Muller, who has worked in the technical innovation department since 1990, is now the chief technology officer of the German stealth champion. 
 
Connected Industry: 
 
Dr Muller, industry 4.0 is considered to be the biggest technology trend in the international industry today, but every industry has its own views on this philosophy. So what do you think is the common denominator of industry 4.0? 
 
Hartmuth Muller: 
 
Our group thinks that industry 4.0 is an inevitable trend, and the whole industrial area is focused on this, but they are very different in different industries. Therefore, the key words to describe this trend are different. For us, the core idea of industry 4.0 is the cyber physical system (CPS). 
 
Connected Industry: 
 
Do you think the concept of "industry 4.0" is really relevant? Is it really a trend in the future? 
 
Hartmuth Muller: 
 
Industry 4.0 was recognized as the fourth industrial revolution. What I'm seeing right now is just something transformative, something that has been used to try to integrate the digital world with the real world. 
 
Connected Industry: 
 
Let's talk about the cyber physical system. What is KLINGELNBERG's priority for the system? 
 
Hartmuth Muller: 
 
We are a company specialized in machine tool production and precision measurement technology, and the gears carry the most important mechanical properties. Through our equipment, customers can manufacture gears for vehicle and industrial use. These gears can be positive gears or bevel gears. It is very high quality, from the design process to the finished gear, the cost is very high. 
 
Through our approach, users can get digital twins who are producing parts, including digital images of geometric shapes. On this basis, we can adjust the digital model of the product and the actual production process, thus establishing the process quality control ring. The concept that it is based on is the syber physical production system (CPPS). 
 
Connected Industry: 
 
How does CPPS change the client process? 
 
Hartmuth Muller: our field is the machining of parts. The traditional process begins with the design link based on CAD digital modeling. The CAD model in production is only used to generate the mechanical motion model under the specified tool, which will be implemented in the preparation of CAM process and NC programming. The basis of production is the NC data for processing machines, the given tolerances and the drawings generated by the CAD model. As for the correction of the errors in the production process, the correction of the errors will need to be solved by the professional NC programmer or the new NC data to make up for the error. 
 
For gears, our understanding of the customer is different: we combine the design process with the continuous production simulation. So instead of focusing on the CAD model, we're working on the entire working rules of a gear. Based on the CPPS we developed, digital twins not only include geometric images, but also the production steps of geometry and motion description between tools and components, known as kinematics. Therefore, the CAD - CAM process is completely unnecessary because the NC gear processing data has been generated during the generation process. 
 
Connected Industry: 
 
So you think industry 4.0 can be a better way to optimize your processes. But at the moment, there are technologies that can be more subversive, like 3D printing. What do you think? 
 
Hartmuth Muller: 
 
In the CPP system, there is almost a lot of data that is needed to be used for process optimization in the future. 
 
For 3D printing, in many application scenarios it really does apply. Especially in the rapid prototyping, or through the cutting process impossible to achieve, has complex internal structure series product production. 
 
But 3D printing is no longer a good option as long as you are able to make a product that is machined and can reach a certain level. Economies of scale cannot be printed in 3D. 
 
Connected Industry: 
 
Back to CPPS, we further explore: what role do you think the Internet of things plays? 
 
Hartmuth Muller: 
 
The Internet of things (IOT) refers to the network of higher-level objects interacting with each other, and has the potential to transcend the process. For example, we can not only implement machine monitoring, but also monitor and plan all other resources in real time. 
 
In the field of machine tool maintenance, preventive maintenance is the exact and effective method. In this case, based on the statistical information, we can detect the wear condition of each component. In this way, we can replace the badly worn components, but often such parts are not yet available. 
 
With the development of the Internet of things, people can take a big step forward from preventive maintenance to predictive maintenance. Through predictive maintenance, the wear state of the machine parts is detected by the sensor and recorded in the digital system. Now that we know the fraying state, we can replace it in time when the parts are really fast. Therefore, people can use resources more effectively through the Internet of things, so that they can be more cost-effective. 
 
Connected Industry: 
 
As you mentioned, a lot of data is generated in industry 4.0. How do you view the development of big data? 
 
Hartmuth Muller: 
 
Yes, it produces a lot of data, but often people lose data very quickly. Therefore, I am a supporter of "small data analysis", which supports relatively controllable data set processing. First, big data sounds like a lot of data, but data assertions are stupid. It requires a smart mind to combine data with existing knowledge and benefit from it. 
 
We are now using machine data to determine the cause and effect of the anomalies. The lookup of the association pattern is beneficial to the determination of causality, that is, the operator or environmental factor causes some anomalies. 
 
We can do data analysis, thanks to artificial intelligence, we are far from artificial intelligence. The academic environment is certainly important, but the practical understanding of machine tools is still not mature enough.