Optimization of aluminium die-casting process through predictive maintenance and parameter traceability systems

Abstract

The purpose of this paper is to present the optimisation potential for aluminium die casting process through predictive maintenance and parameter traceability systems. Aluminium considered the metal of the future due to its physical and chemical properties, and this paper is specifically focused on the die-casting process, its potential failures and proposed solutions for reduction of defects. The methodology for optimisation is based is focused on the Total Traceability Management (TTM) software and its technical solutions for predictive maintenance. As a key function of the TTM, the predictive maintenance module is based on conditioning monitoring systems. Temperature, colour, vibration, force, chemical, ultrasound, light, laser, dimensional sensors, all these 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 and not only industry as stated in the new norms of AIAG (American Industrial Automotive 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.