Summary
This AI REGIO project was undertaken by the Instrumentation & Smart Systems Business Area, part of the Industry and Transport Division of Tecnalia. The division is focused on the design, manufacturing and maintenance of industrial products and services, for the improvement of its clients’ competitiveness in the following strategic sectors: foundry and steelmaking, machine-tool, automotive, aerospace, aeronautics, railway, construction and de-manufacturing.
In more concrete terms, and tightly related to the project tasks, the Instrumentation & Smart Systems area focuses its activity in instrumentation and data acquisition systems for industrial machinery and goods; digital factory: production monitoring, optimization and optimal process control; condition-based monitoring solutions for maintenance and products lifecycle management; industrial safety and risk management systems; energy efficiency solutions focused on consumption prediction models and decision making support tools.
Mecanifran is a Spanish SME in the automotive sector focused on the machining process of nuts and screws. The industrial process is not very flexible and requires orders of high quantities to have revenues; so few new references are produced per year (4 or 5). These pieces are part of wheel bearings used in the automotive sector.
Mecanifran industrial process consists of 13 lines of numeric control, each of them with up to 3 lathes. At the end of the line, there is an automatic supervision machine in charge of evaluating the quality of the nuts/screws. The quality of these pieces is quite strict, and very much related to the status of the tooling used for the machining. Currently, the approach to maximize the quality of the produced pieces is quite simple and conservative, based on replacing the toolings quite soon before their performance is degrading.
These tooling change has important impact on the annual costs, so a maximization on the tooling usage, while maintaining the machining quality would impact considerably on the company accounts.
The standard procedure in the plant uses one operator to manage in parallel the tooling changes of the machines of several lines (2 usually), with no other help, which is a cause of stress for the operator when the limit pieces is reached by several machines at the same time or quite close (the tooling changes of several machines overlap).
Purpose of the experiment was to provide a support to the operator, with the main objective of maximizing the tooling usage time of every machine, while maintaining the machining quality, optimizing the machine control parameters configuration to maximize the tooling life while the quality of the production is maintained, and coordinate the tooling change in all the machines (this third objective would face the tooling change considering the impact of machines among themselves, instead of just one by one).
The operator can benefit from a holistic planning that considers all machines they manage. This planning can minimize these overlappings, and even increase the number of machines to be managed by that same operator. Three advantages would come from this global tooling change strategy: maximization of the number of machines managed by every operator, reduction of their stress when they must change toolings in several machines that reach the number of pieces threshold in the same time interval, and maximization of the availability of every machine (currently the stop time of one machine is impacted by its tooling change time, but also by the others’ machines, since the operator that manages them is the same).
Focusing on one line, savings come from a decrease of the cost of toolings (less toolings will be required), while at the same time the programmed stops decrease, which maximizes the machine availability (OEE increase).
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| Country: | ES |
| Address: | Parque Cientifico y Tecnologico de Bizkaia, Astondo Bidea, Edificio 700, Derio Bizkaia 48160 |
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Comment:
Tooling savings.
Operators savings. Less operators needed for machines management.
Increase of the production due to increase of the machine availability.
Increase of quality of pieces and tools life time.
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Manufacturing performance characteristics
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Online prediction of the maximum number of pieces to be produced by a specific machine according to the current status of the tooling and the readings of its control parameters.
Optimization of machine control parameters configuration to maximize the tooling life while the quality of the production is maintained.
Maximization of the tooling usage time of every machine, while maintaining the machining quality.
Optimization of machine control parameters configuration to maximize the tooling life while the quality of the production is maintained
Coordination of the tooling change in all the machines. This objective would face the tooling change considering the impact of machines among themselves, instead of just one by one.