Web-based Architecture
Browser-based user interfaces for production, metallurgy, quality and management.
foundry|365 uses the proven meltshop|365 platform architecture and extends it with foundry-specific process automation and metallurgical functionality.
Browser-based user interfaces for production, metallurgy, quality and management.
Real-time dashboards for availability, productivity, quality and energy consumption.
Complete traceability for heats, ladles, molds, materials and quality results.
Interfaces to ERP, laboratory systems, OPC UA and industrial automation systems.
Production reports, quality reports, energy reports and management KPIs.
Combination of metallurgy, process models and AI-supported optimization.
Web-based architecture for cloud, on-premises or hybrid industrial environments.
Central production database for process tracking, traceability, quality management, reporting and KPI analytics.
Responsive browser-based clients without local desktop installation.
Integration with PLCs, automation systems, scales, laboratory devices and sensors.
ProcessVars provides communication middleware, process variable management and industrial web application functionality used by meltshop|365 and foundry|365.
| OPC UA / OPC Classic | Industrial communication and PLC connectivity |
| Process Variables | Central variable management and historical tracking |
| C# Scripting | Automation logic and calculations |
| Alarm & Notifications | Email and popup notifications for process events |
| Web Applications | Browser-based industrial production applications |
foundry|365 is built for foundries that want to replace isolated tools, legacy visualization systems and manual Excel calculations with an integrated production platform.
Orders, heats, batches, ladles, molds, materials, analyses, equipment and process events in one consistent database.
ERP, LIMS, PLC, OPC UA, scale systems, lab devices, energy meters and reporting platforms can be connected.
Responsive browser-based user interfaces for production, metallurgy, quality, maintenance and management.
The platform supports cupola, induction and duplex operations as well as downstream metallurgical treatment, pouring, quality documentation and traceability.
foundry|365 combines metallurgical know-how, physical models and machine learning. The goal is not AI as a black box, but decision support that operators and metallurgists can trust.
Production events, material data, analyses, temperatures, energy, delays and quality results.
Mass balance, yield, temperature loss, chemistry correction, treatment and pouring constraints.
Recommend material mix, alloy corrections, treatment windows and quality-relevant process actions.
| Raw material cost | Scrap, returns, pig iron, alloys, inoculants and carburizers |
| Energy consumption | Melting energy, holding strategy, route losses and delays |
| Quality stability | Chemistry targets, Mg treatment, inoculation, nodularity and defect prevention |
| Traceability | Heat, batch, ladle, mold, material and lab result traceability |
| OEE and KPIs | Availability, productivity, quality, route performance and downtime causes |
foundry|365 is not only a documentation system. The platform is designed to actively guide, automate and optimize foundry workflows — from raw material selection to pouring and ERP feedback.
Automatic calculation of scrap, returns, alloys, inoculants and carburizers based on target chemistry, stock, yield, cost and production constraints.
Support for charge layers, coke rate, additions, tapping, temperature, chemistry, energy and operational deviations in cupola operation.
Guidance for charging, melting, correction, holding, tapping and energy-related process decisions in induction furnace operation.
Digital workflows for ladles, molds, pouring sequence, treatment windows, traceability, quality release and production feedback.
foundry|365 combines foundry MES functionality, process automation and AI-supported optimization for melting and casting operations.
Production tracking, traceability, KPI/OEE reporting, quality documentation and real-time process transparency.
Automated charge optimization for scrap, pig iron, returns, alloys and inoculants with chemistry and cost constraints.
Digital support for cupola furnace operation including coke rate, tapping, temperature and chemistry control.
AI-supported melting optimization, holding strategy and energy efficiency for induction furnace operations.
Digital workflows for treatment, pouring sequence, ladle handling, traceability and quality release.
Combination of metallurgical process models, AI and production data for robust process prediction and optimization.
Materials, grades, target analyses, recipes, furnaces, ladles, molds, equipment and tolerances.
Cost-optimized batch calculation with chemistry, stock, yield, process and production constraints.
Cupola, induction furnace and duplex route support with process tracking and operator guidance.
Inoculation, magnesium treatment, ladle handling, pouring documentation and thermal windows.
Target analysis, sample management, lab integration, quality decisions and release workflows.
OEE, energy, material consumption, deviations, quality costs and management dashboards.
foundry|365 can be introduced as a focused modernization project — for example charge calculation and reporting — and later extended into a complete foundry execution and optimization platform.
Deployment options aligned with production IT, security and availability requirements.
Replace old servers, PowerBuilder applications, isolated databases and manual Excel-based workflows.
Based on GRIPS experience in production IT, process automation and metallurgy-driven optimization.
We review your process route, existing systems, pain points and the most realistic starting point for measurable savings.
A Foundry MES system connects production, metallurgy, quality, traceability, KPI/OEE reporting and process automation in one integrated platform.
AI can support charge calculation, temperature prediction, chemistry correction, energy optimization and quality stabilization.
Hybrid digital twins combine metallurgical process models with AI and production data to improve prediction accuracy and operational decisions.