Various digitization activities allow casting foundries to make their working processes more efficient concerning costs and productivity.
Fremont, CA: The terms Industry 4.0 and digitization, which are popularly mentioned in the media, stand for the networking of machines and processes with data and communication technologies that channel and process large amounts of information at high speed. The Internet plays a vital role here. Be it a revolutionary or an evolutionary process – every company must find its path to Industry 4.0. Thus, also die-casting foundries are bombarded with the question of which digitization activities makes individual sense to work more efficiently on the cost, to stay competitive, and to be able to provide new services and added values.
The suppliers of the die casting industry provide digital technology that makes the most of machinery performance. According to a strategist, many of the company’s customers are interested in digitization activities that can reduce unplanned downtime – and the financial losses linked to it. The data analysis can be done in the cloud made available or in the user’s network. Additionally, the universally applicable measuring and monitoring system is also based on sensors that are installed at specific points of the die casting cell and measure various functions such as the clamping cylinder pressure, the plunger movements, the movements of the mold during the injection process and the desiring of die casting tools. Via the clamping cylinder pressure, for instance, it is possible to control the clamping force and to keep it constant with the result that the mold and machine load is uniform. Machine and process data gathered by sensors during production not only provides indications of the technical condition of production systems but ultimately helps to optimize and sustainably increase the Overall Equipment Effectiveness (OEE).
Holistic thinking refers to be able to utilize digital technologies in a die casting foundry optimally. This includes transforming data into knowledge, understanding interrelationships, and using the findings for the manufacturing process.