Industry 4.0 is a term applied to a group of rapid transformations in the engineering sector. It is connected machines generating significant 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.
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 the management of resources.
Artelnics offers you the most advanced analytics techniques to go one step ahead and make useful predictions. Adopt this technique and be ready to be more productive. Some applications are listed below:
Predicting solar energy production
By applying machine learning techniques, we derive prediction models for the energy produced by a solar plant. To do that, we analyze historical data with both climatological variables and the corresponding generated power.
Performance optimization is necessary to ensure that businesses remain competitive and provide 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. Some solutions are listed 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.
Predictive maintenance reduces the costs of engineering companies by minimizing the potential risks of their technical equipment. The challenge is to determine the condition of in-service equipment 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 vast experience and disruptive technology. Some applications of predictive maintenance are:
Diagnose faults in ultrasonic flowmeters
By applying machine learning techniques, we derive prediction models for knowing which are the relevant factors that cause failures in the system, an ultrasonic flowmeter. To do that, we analyze a database with the characteristics of a liquid ultrasonic flowmeter and state (healthy or unhealthy). The input variables are flatness ratio, symmetry, flow velocity, gain, etc.
Quality improvement in engineering is closely linked to compliance with legal and business requirements. It analyzes the most important factors defining a product to act over them most efficiently. Indeed, quality deficiencies mean less competitiveness in the global market place.
As far as quality attributes are concerned, advanced analytics techniques play an essential role here. The quality improvement allows improving products to suit market requirements. Some uses of our technology to improve quality are:
Improving the 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 force from these components.