The networking of the process and tolerance models with the described functionality is only possible, if the data exchange between the models is structured and standardized. It is also necessary to connect the process data with the product data to obtain all information for the different calculations. In the presented research work, a product data model was developed and used to interlink the data exchange. The product data model was realized by using the XML format with an integrated part for pure geometrical data in the DXF-format. In the first step, the data model contains the target profile definition with the geometry, materials, qualities and tolerances. This information is the input data for the first calculation with the process models, which are integrated into CAM-modules for the process steps. The output data representing the output of the process step is transferred back into the product data model and again the input for the next step (Fig. 7).
Figure 7: Networking of the process chain with a product data model.
This structured methodology ensures that at any point of the process chain, the product data model contains a complete definition of the profile in this production stage including the process information for the manufacturing.
5 CONCLUSION
The interdependencies in the process chain of profiling show representative, for other interlinked process chains, the challenges for the integration of information technology to automatize the process design, setup and to achieve the objective targets of “Industry 4.0”. The development of valid process models to create a virtual counterpart is a necessary first step.
Furthermore, the networking of the process models by means of a product data model provides a proper way to make the required information available and structures the data exchange between the different models. The generation of the process data before the real production requires the consideration of systematic and stochastic errors of the individual process steps. The statistical approach for the stochastic influences has shown that only a probability related allowance of the profile geometry enhanced the virtual counterpart of the production to handle and generate realistic data.
The presented methodology of the research results compose a foundation to upgrade the elements of a process chain to CPS and get valid process data for the manufacturing.
6 EPILOGUE
The term “Industry 4.0” can lead to some misconceptions regarding the technical history of humanity and statement of the fourth industrial revolution. Historically it is already difficult to say that the first industrial revolution took place at the end of the 18th century. The mechanization of work at an industrial level was already established back in the Roman Empire. The milling plant of Barbegal (3rd to 5th century A.D.) for example was built from two, ever consisting of eight single mills, mill chains arranged in a cascade and were able to produce around 4.5 tons of flour a day [10].
Furthermore, all considerably developments, of the first industrial revolution under the concept of “Industry 4.0”, were done in the 12th Century with the development of the crank drive by al-Dschazari [11]. The Chinese history of technology is not even considered. This shows that the in the term “Industry 4.0”, the chosen numeration is arbitrary.
However, it is a question what forces an industrial revolution or an invention and introduces a new technology. Therefore, looking at the history of technology let one assume that for an implementation of a new technology, the need (“pull”) is more important than the development (“push”) of that technology. Nikolai Kondratieff had the theory (also called Kondratieff cycles) that after a basis innovation (mostly out of distress) was born, it has a boost for the next 50 to 60 years. When no production improvements can be made out of this innovation, it comes to regression or crash (Fig. 8). [12]
Figure 8: Industry 4.0 vs. Kondratieff cycles.
In addition, some aspects of “Industry 4.0” seem to be not revolutionary.
Penzias under the term “Holon” and Warnecke under the term “Fraktale Fabrik” already introduced the decentralized structure of the “Smart Factory”
with the ability of self-control and self-optimization [13].
All in all, “Industry 4.0” may also be considered as an evolution in production using the basic innovation “computer”.
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