The purpose of the paper is to present the connetion between the Total Traceability Management (TTM) software and the technical solutions for predictive maintenance. As a key function of the TTM, the predictive maintenance module is based on conditioning monitoring systems. Sensors for measurement of temperature, color, vibration, force, chemical composition, ultrasound waves, light, dimensional, are developing on the global market as part of the 4th industrial revolution, Industry 4.0. The TTM is combining the factory floor technologies with the informatics systems as ERP, customer portals, and MES, through a specific algorithm and based on PLC and sensorial hardware. The TTM is becoming a mandatory requirement for automotive industry as stated in the new norms of AIAG (Automotive Industry Action Group), VDA (German Association of the Automotive Industry), and JAMA Japan Automobile Manufacturers Association. The approach of this paper is a theoretical presentation of the practical experiments presenting the most modern solution in terms of software, sensorial installations, monitored equipment and the realized outputs. The TTM concept are not yet fully mature, various solutions being deployed on the market with specificities for diverse industries.
A cikk célja kettős. Egyrészt ismerteti, hogy miként lehet bevezetni a mechatronika oktatásba a csúszómód-szabályozás elméletét, másrészt egy egyszerű internetes szervohajtás kapcsán bemutatja, hogy a csúszómód-szabályozók egyik legfontosabb tulajdonságának, nevezetesen a robusztusságnak milyen korlátai lehetnek egy gyakorlati alkalmazásban. A klasszikus csúszómód-szabályozás esetén a szabályozás kezdetén általában jelentkezik egy ún. elérési fázis, amikor még nem alakul ki a csúszómód, így ebben a fázisban nem tapasztalható a robusztusság. A cikk egyik legfontosabb újdonsága, hogy módszereket vezet be az elérési fázis hatásának visszaszorítására és a bemutatott módszereket kísérletileg összehasonlítja a robusztusság szempontjából.
The high-performance feature of the Permanent Magnet Linear Synchronous Motor (PMLSM) makes it a reliable and valuable motor for use in the automotive industry, especially for electric vehicle (EVs) applications. This research proposes a bond graph approach in modeling the PMLSM as a multi-domain dynamical system.
However, A time-based simulation was performed using 20-sim software to simulate the dynamical behavior of the motor. An equivalent model of the motor was first obtained and then modeled and simulated using 20-sim software. The model of the PMLSM drive system was modeled separately and incorporated with PMLSM Motor equivalent model to form a global model.
Moreover, the motor drive system response was studied based on the sensor resolutions and the inverter switching frequency. The block diagram and the transfer function methods validated the bond graph model obtained. Two classical PIs such as continuous and discrete were implemented on the motor response to control the velocity of the motor.
The paper focuses on aero graphene and carbon nanotube (CNT) aerogel which will use in aircraft such as battery, engine, pitot probe, wings, fuselage, plane front glass which will also protect the aircraft from rain and wind because of the buoyant force.
There are several ways to make aero graphene, but the most common approach includes reducing a precursor graphene oxide solution to make a graphene hydrogel. Through freeze-drying, any solvent is removed from the pores and replaced with air. A new method for producing aero graphene has emerged: 3-D printing. This is a significant scientific achievement. It creates a resin by diffusing graphene in a gel. The graphene resin can be cured into a solid and then dried in a furnace using UV LED light. Aero-graphene coating into the fuselage, wings and front glasses of the cockpit will give a great impact on the next-generation aircraft. Making an aircraft with aero-graphene will give the aircraft a strong and light skeleton.