The heat treatment industry is navigating a transformative period marked by digitalisation and Industry 4.0 innovation, reshaping its approach to operational efficiency and precision. With increasing demands for stringent standards and energy efficiency, facilities are turning to advanced, data-driven insights and intelligent automation to optimise performance across the thermal process. However, many heat treatment providers are struggling to keep pace with these technological advancements. Here, Peter Sherwin, global business development manager of heat treatment for industrial technology company Watlow, discusses how heat treaters can gain the benefits of data-driven integration throughout the entire thermal loop.
The heat treatment industry is essential to global manufacturing. But the volatile landscape of the industry has led to many changes over time.
In the 1980s, globalisation was the focus, while the 1990s saw automation take over. Moving into the 2000s, energy transition was yet another factor for the industry to contend with, while in the 2010s, the industry needed to address changes to global heat treatment regulations and workforce reorganisation. As we moved into the 2020s, a new era in manufacturing dawned with intelligent systems, incorporating the revolutionary concepts of the Industrial Internet of Things (IIoT) and Industry 4.0 smart manufacturing.
These changes have led to a point where digital transformation is no longer optional but essential. The advent of Industry 4.0, smart technologies and interconnected systems are reshaping how heat treatment plants operate. The push towards digitalisation is driven by stringent standards like AMS2750, CQI-9, and ISO 20431:2023, which mandate precise temperature control and detailed process documentation. To stay competitive and meet these standards, the industry must adopt digital strategies that enhance operational efficiency and quality control.
The untapped potential of data
Transitioning from paper-based records to digital platforms has exponentially increased the data available for improving operations. However, the heat treatment industry faces a significant challenge: much of this data remains underutilised due to difficulties in integration and analysis. The key is not just in data collection, but in translating this data into actionable insights.
Micro drifts in processes, often unnoticed over time, can be detected through detailed data trends, enabling predictive maintenance and timely interventions. By harnessing this data, heat treaters can predict when to focus on specific actions, improving decision-making and operational efficiency.
Modern systems provide data integration, advanced analysis, and data export, enabling the data collected to be more effectively utilised to give deeper insights into processes and allowing artificial intelligence (AI) and machine learning (ML) algorithms to be employed.
Integrating advanced technologies
Advanced power controllers such as silicon controlled rectifiers (SCRs) from Watlow, along with precise process controllers and modern temperature sensors, are essential elements to heat treatment processes. Using SCRs that provide predictive load management and energy optimisation, means energy costs can be significantly reduced, and peak loads managed efficiently without incurring tariff penalties. Sensors offer precise temperature measurements and feedback, connected to accurate proportional–integral–derivative (PID) controllers to ensure batch temperature profiles are consistently and precisely met. Additionally, advanced data management systems are crucial for recording process parameters, ensuring compliance with standards, and providing valuable insights for continuous improvement.
Overcoming integration challenges
Despite the benefits, the industry still struggles with integrating various components of the thermal loop. This lack of integration can lead to inconsistent quality, operational inefficiencies and higher energy consumption. A holistic approach to data integration, where every segment of the thermal loop works seamlessly together, is becoming imperative for heat treaters to maximise equipment and energy utilisation and remain competitive for the future.
AI and advanced analytics play a transformative role in this data-driven landscape. By analysing patterns and trends within collected data, AI can predict equipment failures, schedule maintenance, and optimise energy consumption. This proactive approach not only reduces downtime but also enhances overall equipment effectiveness (OEE) by meticulously tracking availability, performance and quality. ML models can process historical and real-time data to make informed predictions about system performance, enabling operators to make proactive adjustments. Through trending and understanding data, issues can be detected and remedial actions can be taken before they either affect the quality of a batch or impact the longevity of equipment.
The heat treatment industry must embrace Industry 4.0 technologies to remain competitive in a rapidly advancing market. As the industry evolves, a merged design between heaters, sensors, PID controls and power controllers within an Industry 4.0 framework will ensure rapid system response and tight control of process parameters. The future of the heat treatment industry is not just bright — it is data-driven.
To learn more about Watlow’s solutions, visit watlow.com
