industry 4.0

INDUSTRY 4.0

Industry 4.0 is a term applied to a group of rapid transformations in the engineering sector. It is connected machines generating big amounts of data that can be analyzed to improve operations. We are experts in the use of advanced analytics for engineering. Artelnics offers a wide variety of solutions addressed to meet the needs of industry 4.0.

These solutions are described below.

  1. Output prediction.
  2. Performance optimization.
  3. Predictive maintenance.
  4. Quality improvement.

1. Output prediction



Output prediction has a strategic nature, since it provides business with valuable information for making decisions. Collecting and analyzing data can be used to make forecasts, reducing operational costs and optimizing management of resources.

Artelnics offers you the best advanced analytics techniques to go one step ahead and make useful predictions. Adopt this technique and be ready to be more effective.


PROTOTYPE DESIGN


SUPPLY ASSURANCE


RISK MODELLING

An application example is listed below:

Predicting solar energy production
By applying machine learning techniques, we derive prediction models for the energy to be produced by a solar plant. To do that, we analyze historical data with both climatological variables and the corresponding generated power.

2. Performance optimization



Performance optimization is necessary to ensure that businesses remain competitive and keep providing cutting edge solutions to everyday social problems. To achieve these objectives, companies can use modern data engineering to improve their standards.

Through these innovative solutions, Artelnics optimizes systems for higher efficiency and lower environmental impact.


REDUCE CONSUMPTION


INCREASE COMFORT


ABATE NOISE

An application example is described below:

Airfoil self noise prediction
Predict the noise generated by an airfoil from dimensions and flight conditions. The input variables are frequency, angle of attack, chord length, free stream velocity and suction side displacement thickness. The output variable is the scaled sound pressure level.

3. Predictive maintenance



Predictive maintenance reduces costs of engineering companies by minimizing potential risks of their technical equipment. The challenge is to determine the condition of in-service equipment in order to predict when maintenance should be performed and to prevent unexpected failures.

Artelnics is the best candidate for this kind of applications, due to its great experience and disruptive technology.


MINIMIZE DISRUPTIONS


COST SAVINGS


HIGHER SYSTEM RELIABILITY

An application example is listed below:

Predicting energy generated by wind turbines
By applying machine learning techniques, we derive prediction models for the energy to be produced by a wind power plant. To do that, we analyze historical data with both climatological variables and the corresponding generated power. The output variables are blade pitch position dev, rotor angular position dev, tower vibration dev, etc.

4. Quality improvement



Quality improvement in engineering is closely linked to compliance with legal and business requirements. It analyzes the most important factors defining a product in order to act over them in the most efficient way. Indeed, quality deficiencies mean less competitiveness in the global market place.

As far as quality attributes are concerned, advanced analytics techniques play an important role here. Quality improvement allows improving products in order to suit market requirements.


FIT QUALITY STANDARDS


INCREASE PROFIT


REDUCE FAILURES

An application example is listed below:

Improving quality of concrete
In this example a set of compressive strength tests has been performed in the laboratory. The concrete compressive strength is a highly nonlinear function of age and ingredients. The objective is to model the compressive strength from these components.