The Smart Mold, Bringing Industry 4.0 to the plastic injection tooling sector
Description of the Experiment:
Pernoud designs high-tech steel molds for plastic injection. Pernoud is a company oriented towards groundbreaking innovation, with a huge potential to constantly create new means to improve the process of plastic injection for their clients. The objective for Pernoud is to add intelligence on plastic injection steel molds to transform this mechanical system in a CPS one, with an expectation of improving quality and reducing costs and delays of the part produced. To reach this goal we need a feedback from the mold to know what happened during the production. So to access to this information we need to instrument the mold with different embedded devices on it but also to directly have a look on that information any time it could be needed.
Main objectives of the Experiment:
- Data acquisition with sensors adapted for moulding tools. This will enable monitoring environment conditions using a smart system and sensors for temperature and pressure. The monitoring capabilities will produce alerts for the operator to allow them to react to possible malfunctions which could lead to quality problems in the produced pieces.
- Driving electrical actuators: The movement of the mold can be performed by electrical actuator. Those actuators will achieve flexibility levels not possible with standard hydraulic actuators. In fact, in multi-version molds, which are able to produce several versions of a product, the electrical actuators will perform the mechanical movements that allow to switch a cavity version.
- Cloud data monitoring: to store the data acquired during the utilization of the smart tool, the smart system is linked to a cloud and this cloud will allow an access to the entire lifecycle of the tool from anywhere and at any time.
Main results expected to achieve in the Experiment:
- To reduce the number of defected parts by optimizing the operator’s time to react to deviations, thanks to the real-time monitoring capabilities. Today operators take 5 to 15 minutes to detect deviations, which means an average of 20 parts. With the deployed CPPS, this time should reduce to instant (if strong deviation) to 5 minutes (if low deviation), that means a maximum of 10 defected parts.
- To reduce the number of maintenance operations. This will allow to reduce the number of maintenance trips for an equipment during its life cycle. It also will permit to reduce the time to expertise the mold thanks to data sensors. It will, in final, reduce the unavailability time for the mold, that mean reduce the cost of unavailability of about 15%
- Improving the efficiency of the power chain; use electrical power only during action phases. The target is to reduce the cost of energy to 20%. Today’s injection machine are electrical and mold movement hydraulically, a hydraulic pomp is used to convert electrical power to hydraulic one and a cylinder block to convert in mechanical power. Total efficiency of about 79%. With the smart mold, this set is replaced by only a stepper motor with an efficiency of about 90 (95% with optimal utilization). That means 14% of energy saved. With the added of reduction powers used time, this number will rise to 20% and more. For a 1.000.000 part of production (120 part per our) that mean 8350 h of production, that mean (in France) 25 000 € of energy cost (for the mold) and this number will be reduced to 20 000€.
- Automatic switching version will reduce the time to change version. To day to change a version it takes about 5 to 15 minutes, and with the smart mold we will reduce it to less than a minute.
Sector/s of Application
- Main Sector of Application: The toolmaking sector for plastics injection, mainly manufacturers of steel molds.
- Potential Sector/s of Application: The toolmaking sectors of plastic transformation procedures other than injection could benefit from this experimentation; such as the processes for thermoforming, extrusion or blow molding.
|Demonstration level:||Level 0 - Level not specified|
Significant innovations, use case requirements and lessons learned - (No information available)
Added value and impact - (3) view
- Production operators: real-time data and alerts, both at the work cell level, enterprise and cloud
- Production managers and moldmakers: historical data analysis
The smart mold provides Human Machine Interaction at several levels:
Technologies and enablers - (6) view
Digitalisation pathways - (2) view
ICT performance characteristics - (2) view
Standards, standardisation and regulation - (2) view
Applications areas and sectors - (2) view
Business model aspects - (2) view
- No organisations linked to this result