Smart Factory Transformation for Heavy Engineering

Introduction

Heavy engineering plants often struggle with the dark data of non-automated equipment like manual welding machines and portable milling tools. This case study details the deployment of an IoT-driven Smart Factory framework designed to bring visibility to manual fabrication processes.

Problem Statement

The primary difficulty in heavy fabrication is the lack of digitised material flow and machine utilization data. Unlike CNC shops, welding and assembly are often manual, making it difficult to track useful parameters like:

Actual ‘Arc-on’ time versus idle time.

The physical location and utilisation of portable assets.

Workforce safety and allocation in large, multi-bay facilities.

Technical Solution

Zwilling’s solution is built on a modular AIoT (AI + IoT) stack that monitors machines,

processes, and people.

A digital twin system collects data from current clamps and vibration sensors, connects via communication gateways and cloud aggregation, processes it for diagnostics and safety, and generates predictive maintenance alerts and worker movement heatmaps.

A. Machine Boost (Diagnostic Layer):

Hardware sensors (Current and Vibration) are retrofitted onto legacy and manual machines. By analyzing current signatures, the AI can distinguish between:

· Machine Idle (Power on, no work).

· Active Work (Arc-on time for welders).

· Anomalous behavior (Voltage spikes or excessive vibration indicating imminent motor failure).

B. Process Boost (Flow Layer):

This module visualizes the movement of large sub-assemblies through the plant. It identifies bottlenecks where components sit idle for extended periods, enabling data-driven rescheduling of workflows.

C. Manpower Boost (Safety & Productivity):

Using zone-based cameras to monitor workforce. Technical features include:

· Geofencing: Alerting supervisors if personnel enter high-risk zones (e.g., under crane operations).

· Activity Mapping: Correlating manpower presence with machine uptime to optimize shift handovers.

Outcomes

· Asset Optimization: Real-time tracking of 100+ critical assets including welders and portable milling machines.

· Maintenance: Transition from ‘run-to-fail’ to predictive maintenance based on actual usage hours rather than calendar days.