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Smart manufacturing: Industry 4.0

Smart manufacturing Industry
In the second decade of the 21st century, manufacturing is overcoming a new set of significant changes that are foreseen as game changers not only for the industrial sectors, but also for the society as a whole. These changes are linked to technological developments and their application to new areas of business activity. They are usually identified as the fourth industrial revolution (Industry 4.0) and are already starting to re-shape how products are designed, manufacturing processes are driven and product quality is monitored (and perceived by the customer).
This aim of many new approaches to manufacturing is to exploit new technological developments in sensors, machine to machine interaction, and system integration applied to a typical batch process, namely that of film metallization. In many cases, the consequences of the industry 4.0 projects in particular sectors are directly applicable to other industries, and in a more general form to many other industrial process.

Evolution of Material and Information Flows in the smart Factory

The process flow in Figure 5 is an evolution of a classical process diagramme, showing the material and data flow around a typical industrial process.

Information Flow

There are basically two information channels:

  • Input: This channel is used to load the specification of the process into each step. This means loading parameters into a particular machine or providing the control plan for the operator in the process.
  • Output: This channel collects all process-generated data. This includes development of tools for detecting defects, characterization data (dimensional), and quality data.

Both pipelines of information, coexisting with the traditional flow of material, configure the basic network for the development of a 4.0 industry:

  • Material Flow. The aim is to fully link material and data flow. The process should drive the material automatically through the process steps, collecting and providing all necessary outputs, inputs, feedback and prognoses. This step may also include a lean review of the existing process and process steps.
  • Data Input: The process needs to be reengineered to automatically set-up machines, automatically execute control plans, based on the knowledge of the product to be manufactured. This requires standardization of the recognition and identification of the product.
  • Development of analysis and inspecting tools. The control and knowledge of several parts of the process need to be improved in order to increase the control on the process and its output. In particular, an automatic inspection system needs to be developed, at least in the slitting part of the process. Besides, all machine data and further measurements need to be collected and integrated.
  • Machine (legacy systems) integration. Current processes consist of existing machines and owned software. These independent systems need to be integrated to receive data and provide outputs.
  • Data Generation: The process will be able to generate all kind of data about key process parameters and measurements
  • Business intelligence. All the information gathered in the previous steps needs to be analyzed along the different steps of the process in order to provide feedback of the previous steps and also to provide a prognosis for adapting the future course.

Towards smart processes and products

Ultimately, a key element of the success of smart manufacturing strategies is the development of efficient new processes that integrate systems and elements into a new reality involving the alignment of men and machines. This effort will definitely be combined with the consecution of new connected and smart products, but also with the servitization of many traditional business models and the development of the kind of products still to be conceived.

By Dr. Ramón García Rojo.

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