Success stories

Predictive maintenance and early identification of defectology

Our client develops integrated solutions for the automotive sector. Within this environment, they are involved in extrusion, forging, as well as machining of various parts.

01. Challenge

The reduction of defects and the avoidance of uncontrolled stoppages is a constant in all industries. In metallurgy, it is even more necessary to minimise these factors, as line stoppage entails a huge production cost. This is due to the need to wait for the equipment to cool down, as well as the energy cost of reheating the furnace.

On the other hand, manufactured parts are costly to produce and, consequently, defective parts have a significant impact on the operating account.

Defects in these parts are not, in most cases, caused by a problem in the mould or a particular variable, but are the result of a combination of several factors. Based on changing variables (temperature, pressure, rate, number of cycles, etc.), they can cause defects that are not noticeable until later processes. This would force the discarding of a huge number of produced parts

To minimise these circumstances, it was clear to our client that he had to move towards Industry 4.0. For this reason, they contacted Datision, being certified as Industry 4.0 Advisor by Acció, so that we could help them in their digital transformation process.

02. Solution

Based on the exhaustive analysis of the state of the situation and the strategy defined, the need to install a set of sensors was established to provide continuous data on the process. Sensors that analyse both the operation of production equipment and of the product itself in its different stages of transformation.

Thanks to this data captured by the new sensors, as well as the data from the various PLCs, the system continuously analyzes the information provided. It looks for correlations between production values and their final result, which is based on quality control history..

This allows the likelihood of the part being defective and the type of defect that may occur to be determined, allowing appropriate action to be taken and avoiding an increase in rejects.

On the other hand, the installed sensors are used to analyse the operation of the production equipment, thus developing a predictive maintenance system. It is able to detect any variation in the “normal” operation of the equipment, showing the maintenance manager any such changes.

Based on the information indicated by the manager, if the variations are possible failures, the system learns which pattern of information can determine a possible problem and indicates the percentage risk of equipment downtime.

03. Current scenario

Maintenance work has been facilitated by continuous information from the installed sensors and the use of the developed control panel. The system has reduced the number of uncontrolled stoppages. Moreover, those that still occur can be resolved in less time.

On the other hand, the percentage of defective parts has been significantly reduced, and the variables that produced them are now known.. This increases the technicians’ knowledge of the production process.
In short, the plant is much more productive and the loss has been significantly reduced. The installation of this system in other lines and plants of the company is planned.

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