Practical Training on Smart Manufacturing
Based on Project Case
 

This article is written at the invitation of a magazine, according to the genre of officially published papers.

I.  Preface

At present, the training of Smart Manufacturing is weak in two aspects: (1) there are few on-site cases: many trainings still focus on the explanation of the importance of Smart Manufacturing and some actions of major companies in Smart Manufacturing, that is, "what have you done"? Our training is based on our own Smart Manufacturing cases and focuses on explaining "how to do" in Smart Manufacturing. (2) There is less involved in engineering modeling, machine learning and intelligent system development: these three points are the key differences between Smart Manufacturing and other manufacturing, such as digital manufacturing. In the specific historical stage of Smart Manufacturing, although basic automation and digitization are still of great help to the manufacturing industry, these are not intelligent after all, but only the indispensable foundation of intelligent. Our training focuses on the three main fields of Smart Manufacturing, although we also make some brief introductions to other fields, such as MES.
At present, the foundation of Smart Manufacturing technology is relatively weak. In the transformation from mechanization to automation, digitization and intelligence, make-up courses account for a large proportion. At present, the development stage of the enterprise mainly lies in automation and digitization. In terms of intellectualization mainly characterized by engineering problem modeling, machine learning and intelligent system development, at present, it is mostly concentrated in the periphery of Smart Manufacturing, such as intelligent warehousing. At present, the optimization of the core areas of the manufacturing process is mainly focused on several platform developers.
However, Smart Manufacturing has developed rapidly. Many countries' governments have given greater support to Smart Manufacturing. It can be predicted that in the next two to three years, the Smart Manufacturing environment will be significantly improved. From the high-quality data required for model-based machine learning and intelligent system development, it will gradually be better guaranteed. At present, the team is vigorously carrying out Smart Manufacturing training while installing the developed mature defect early warning system for the manufacturing execution system MES to provide production guidance and reduce defective products; At the same time, when the conditions are ripe, develop some equipment software (equipment version of intelligent system).

Since there are few practical cases in engineering modeling, machine learning and intelligent system development required for manufacturing process optimization in, the team also relies on the cases completed in Germany, the United States and South Korea, in addition to the cases of the team in large enterprises in China (BYD and Nisco, etc.). Case based teaching goes deep into the level of "how to do", which is a step closer than that in the market. In the next few years, many countries will enter the main battlefield of "how to do" in Smart Manufacturing, so it is time to provide training in this field!

II.  Case and Resource Based Training

The main trainer completed a large-scale project with a total investment of DM 1 million from the national DFG German scientific research and Technology Association when he was doing a doctoral project in Germany in his early years, and wrote a book. On the eve of graduation, the main trainer was employed by the senior engineer of Morgan construction company (now Siemens) in the United States to be in charge of its model development. The main trainer has developed three groups of more than 100 sets of models by using a large amount of data accumulated by the company for more than 100 years. After that, the main trainer took these models to the front line of the factory to develop Smart Manufacturing system; As a software engineer / Software Consultant, he completed 30 computer courses for eight years, developed 50000 pages of large websites and dozens of apps, and achieved breakthroughs in a series of computer theories such as object-oriented programming, SOA and enterprise windows, reaching the level of doctor of Computer Science in the United States.
Considering the weakness of current technical foundation and the desire for Smart Manufacturing technology, we decided to use the team's more than 200 on-site cases of Smart Manufacturing in Germany, the United States, South Korea, and other countries, as well as the million pages of data collected in various countries around the world to carry out a series of technical training to improve the level of Smart Manufacturing in manufacturing industry.
Our team has been using Smart Manufacturing to solve manufacturing problems for decades. Even before the term of Smart Manufacturing became popular all over the world, we have been doing data acquisition, engineering modeling, machine learning and intelligent system architecture development in high-end manufacturing, and generating the best parameter combination in the production process through intelligent software, Transfer to basic automation. In the early years, it was mainly in Europe and America, and then in Asia, especially in some large companies.

Table 1: Experience of the main trainer as German engineering Ph.D. and US software Ph.D.

 Table 2: items completed by the main trainer

Table 3: projects completed by American Metal Pass (including projects guided or participated by the main trainer)

 At the same time, the team has collected and sorted out a large number of Smart Manufacturing related materials in major libraries in Germany, the United States and Canada in the past 30 years, amounting to millions of pages! The case training of integrating these materials into a large number of Smart Manufacturing projects is an excellent learning opportunity for students! This enables students to really learn how to do Smart Manufacturing. Imagine a speaker who doesn't know how to manufacture intelligently. Naturally, he can't teach students; If the technical materials on hand are not comprehensive enough, what students can learn will be greatly reduced!

Table 4: main technical data used in training

 III.  Engineering Modeling, Machine Learning and Intelligent System Agent Technology

At present, Smart Manufacturing (but still not the Intelligent Manufacturing) is mainly at the beginning and end of the manufacturing process, such as intelligent warehousing, incoming materials and finished product transportation, etc., while the main process of Smart Manufacturing, such as the optimization of production process, is not much involved. We believe that the main body of Smart Manufacturing should first be engineering modeling, which models the main process parameters and main product problems in the production process, so as to facilitate the optimization and solution; The second is machine learning. Based on the field data, the model obtained from engineering modeling is optimized by machine learning to make the model fully bound with the production line; The third is the intelligent system architecture and development. We should integrate a large number of field engineering models and a large number of field scene use cases into the intelligent system. The so-called scenario use cases mainly refer to all on-site problems to be solved by all on-site engineers. Theoretically speaking, the production based on intelligent system makes the field no longer need to solve these problems manually. Of course, these engineers / technicians will go to the background to continuously optimize the intelligent system; The optimized system will be operated by field personnel to complete production. At the same time, managers pay attention to observing the production results and possible problems of this system. In this way, everyone works hard to make continuous optimization of production! This kind of control system is what we call Smart Manufacturing system. The optimization of the head and tail of Smart Manufacturing is also important, but the core should be the optimization of Smart Manufacturing in the production process.

IV.  Three Attempts to Explore The Basis of Smart Manufacturing of Chinese Enterprises and Students

1. Field project
After several years of cooperation with a large enterprise, although the first, second and third phases of the project have been completed, it is also obvious that it is difficult to provide high-quality data at the level of Smart Manufacturing on site, although these data basically meet the digital manufacturing represented by data board. For example, the MES system of the enterprise has no tool information. Therefore, the relevant data about the use of tools have to be found in the film detector database, and the film detector is often not connected to the production line; Originally, this was a small problem, but the site said that it must be solved by a third-party network maintenance provider, which is always delayed for a long time, and the supplier of the film detector is very reluctant for us to obtain data from the film detector. Although every company claims to have a large amount of data on the table, there is little data that really meets the needs of high-quality Smart Manufacturing, and it often takes a long time! In this way, the cost of intelligent system suppliers is huge. Enterprises do not have a clear punishment system for the problem of missing data on site, resulting in poor integrity of on-site data. Small businesses often simply cannot collect the high-quality data they need. I feel that the data problem is the biggest obstacle to Smart Manufacturing, especially in some developing countries!

2. "How to do" after five basic trainings
First of all, we have conducted a primary training of ten lectures and five courses. The focus of this training is to teach students what to do in Smart Manufacturing? Because we found that a series of losses caused by misunderstandings in the Chinese industry are quite large. For example, when building the Smart Manufacturing production line, because it is not clear what is Smart Manufacturing, we will only build the production lines of basic automatic control, MES and ERP, mistakenly believe that it is an Smart Manufacturing production line and lack the core intelligent system of Smart Manufacturing. This kind of production line can only be operated by engineers, because engineers have the intelligence of production process, but operators, due to limited intelligence, are difficult to turn a large amount of data into intelligence in operation, which is quite difficult to operate. Because there are many problems in this kind of intelligent production line, the benefits are slow, and there is even a dilemma of fast death if you invest and slow death if you don't invest! Therefore, our first stage of training focuses on letting the trainees know what Smart Manufacturing should "do"?
However, in the second stage, when we want to teach students "how to do", we find that there are many problems to be solved. Recalling the journey I have traveled, I have experienced for decades! In fact, Smart Manufacturing needs to have a strong background in a series of fields such as process, product, equipment, automation, data acquisition, model and software. It is best to have actually worked on site! It can be judged from this that it is difficult for students to reach such a high level quickly only by a few courses. To this end, we have decided to hold dozens of lectures on Smart Manufacturing in the next years.

3. Recruitment status
Recently, the team issued an advertisement for dozens of Smart Manufacturing technical directors and Smart Manufacturing experts, clearly requiring the recruitment of personnel with engineering modeling, machine learning and intelligent system architecture development background to focus on the optimization of manufacturing process.

V.  Main Contents of Annual Training

At present, there is not sufficient training in the subject of Smart Manufacturing (manufacturing process optimization). This training focuses on this aspect and omits the fields before and after the manufacturing process. Firstly, engineering modeling is carried out for the on-site process, equipment and product quality of the enterprise; Secondly, machine learning is carried out based on the built model and the online data collected on site; Thirdly, as a key point, the architecture and development of intelligent system are carried out based on the above two stages. The intelligent system contains about 1 / 3 of the engineering models and about 2 / 3 of the use cases and user scenarios. There are many courses in each of these areas. The technical points of all lectures are based on the completed Smart Manufacturing projects. However, in addition to the above three parts, each benchmark project and other projects are listed in a separate group, which is called the field problem analysis of Smart Manufacturing. In addition, a large number of courses belong to the background foundation of Smart Manufacturing and do not belong to any of the above groups, which can be included in the overview and requirements group.

Table 5: course content details

 VI.  Introduction to Smart Manufacturing Training Benchmarking Project

The thermal and mechanical finite element artificial intelligence model completed by the main trainer in Germany in the early years created a precedent for the application of this model technology in the final shape prediction and production process optimization of large and complex sections at the factory level. In the model development of Morgan construction company (now Siemens), three groups of more than 100 models have been completed, including material deformation model series, material force energy and power demand model series, and material microstructure and mechanical property prediction model series. After that, he took the model to the front line of the plant to develop intelligent system, and led the development of three secondary systems (Intelligent System) for metal smelting electric furnace, refining furnace and continuous casting for kescat company. See Table 6.

Table 6: Benchmarking items

After that, based on the technical foundation and solid model background of German engineering and US software Ph.D., the main trainer served as a technical consultant for intelligent system development in various countries all over the world, and successively completed large-scale international benchmarking projects such as EOS of the United States, POSCO of South Korea, NISCO and BYD of China, as well as Tesla of the United States.

EOS's traditional high-end material project: in the production process of hard and thin products, there were defective products every day. During the return visit half a year after completion, I was told that there had been no similar defective products in the past half a year.

Figure 1: BYD Lithium Battery burr length is predicted and measured by model (there are more than ten influencing factors, including tool quality and use, process parameters and incoming material quality, etc.)

BYD lithium battery Smart Manufacturing related projects (see above picture): burr defect is the main factor causing lithium battery fire (Samsung mobile phone lithium battery explosion, BYD lithium battery incident). At the beginning of the cooperation, the factory used the pole piece slitting burr prediction model which is very difficult to model to strictly investigate the model level of the team. It is required that the hit rate of the model is 85%, and the team has reached 98%; At present, the second phase of the project (burr early warning) and the third phase of the project (knife notch measurement) have been completed. In view of the weak data acquisition ability of some countries, the soft sensing technology with high difficulty in the industry has been successfully applied. It is difficult to predict the parameters directly by using the soft measurement model; This defect early warning system is based on BYD MES and quality inspection data. According to the evaluation of BYD's internal cooperation in this project, at present, only Metal Pass can provide this process / product model system combining equipment and automation. Figure 1 shows the predicted burr length based on the operating parameters of lithium battery electrode. Compared with the measured burr length, the hit rate is more than 98%!

Electronic manufacturing related projects: more than ten enterprises such as Skyworth, TCL and Guangye. In addition, there are still a large number of online information projects: In Industry 4.0 Metaverse site Meta4-0.com, Dozens of apps, tens of thousands of pages of data and dozens of categories of technical materials (mainly in earlier www.metapass.com).

VII.  Training as An Important Way to Gather Talents

Smart Manufacturing talents need strong technical background not only in the fields of engineering process, products, equipment and automation, but also in the fields of software engineering (Architecture and development) and data acquisition. If you need to have working experience in relevant fields, at least 20 years of work experiences are required for a person to be  mature. Therefore, real Smart Manufacturing talents are in great demand. Our training focuses on gathering these multi-disciplinary high-quality talents together and further improving their technical level to meet the needs of Smart Manufacturing of our enterprise and related enterprises.

Due to the huge cost involved in the international projects and international resource collection used in the training, we only take 1 / 10 of the actual cost as the training cost, and if the trainees are at their own expense, it can be halved.

VIII.  General Overview

A full year of Smart Manufacturing technology training is being carried out. The training is based on more than 200 Smart Manufacturing projects completed by myself and my team in Germany, the United States, China and South Korea, as well as millions of industrial technical information collected in major libraries such as in Germany, in USA and in Canada. The purpose of training is mainly to gather talents and further improve their skills.

Training

  General, Paper, Courses, Course overview, Resources,
  Trainer, Experience, Study method, project, Case-based

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