Effective process control and automation technologies link thermal processing equipment such as ovens and furnaces with the operator and the supply-and-delivery chain — in a seamless network of information exchange.
The industrial furnace of tomorrow is a smarter machine: safer, better connected, more flexible and more efficient. Based upon smart, connected products, the industrial furnace of tomorrow maximizes efficiency through preventive maintenance and intuitive collaboration with users while minimizing its environmental footprint and total cost of ownership.
Two process control trends will play key roles in the evolution of industrial ovens and furnaces — and many other process systems:
- Industry 4.0, the German-based approach for smart manufacturing.
- Industrial Internet of Things (IIoT), an internet-based industrial network of networks connecting assets. With IIoT, integrated automation accessible via remote- and cloud-based computing services — along with enterprise-wide analytics — is possible.
These trends and the underlying technologies that support them are affecting many sectors from retail to medical to industrial. This article will focus on how industrial ovens and furnaces will be affected.
Some obvious similarities and differences exist between IIoT and smart manufacturing (Industry 4.0) as well as areas of convergence. Smart manufacturing — or Industry 4.0 initiatives, as they are sometimes known — are focused on expanding manufacturing flexibility, increasing automation levels and implementing digitization. This is not so much the next industrial revolution but an evolution. In the long run, this will reshape complete factories and the way they operate. Such evolution requires embracing a multitude of technologies and ideas that will have a massive impact on end users and OEMs. This will take some time, and IIoT, with all its connected devices, will act as a key enabler.
The IIoT vision of the world is one where smart, connected assets (the things) with varying levels of intelligent functionality operate as part of a larger system. This functionality is tailored to the process needs and ranges from simple sensing and actuating to control, optimization and full autonomous operation.
These systems are based on open and standard internet and cloud technologies that enable secure access to devices and information. This access allows entities to mine process data analytics and exploit the insights gained, along with mobility technologies, to drive greater business value. IIoT solutions that employ a wrap-and-reuse approach rather than a rip-and-replace plan provide greater business control. In addition, this measured approach helps drive the evolution toward a smart manufacturing enterprise that is more efficient, safer and sustainable.
While the long-term effects of IIoT are at times difficult to predict, three distinct operational environments set the stage for the smart1 manufacturing enterprise to emerge. They are:
- Smart enterprise control.
- Asset performance management.
- Augmented operators.
Smart Enterprise Control. IIoT technologies will enable tight integration of smart, connected machines and manufacturing assets with the wider enterprise. This will facilitate more flexible and efficient — and hence, profitable — manufacturing. Smart enterprise control can be viewed as a mid-to-long-term trend. It is complex to implement and will require the creation of new standards to facilitate the convergence of information technology (IT) and operations technology (OT) systems.
Asset Performance Management. Deployment of cost-effective wireless sensors, easy cloud connectivity (including WAN) and data analytics will improve asset performance. These tools allow data to be gathered easily from the field and converted into actionable information in real time. This will result in better business decisions and forward-looking decision-making processes.
Augmented Operators. Future employees will use mobile devices, data analytics, augmented reality and transparent connectivity to increase productivity. As fewer skilled workers are left behind to man core operations due to a rapid increase in baby boomer retirement, younger replacement plant workers will need information at their fingertips. That information will be delivered in a real-time format that is familiar to them. Thus, the plant evolves to be more user-centric and less machine-centric.
Smart Industrial Ovens and Furnaces
As part of a larger class of smart machines1, industrial ovens and furnaces will incorporate increasing levels of machine intelligence to accommodate the need for predictive planning and flexible business assets.
The term “smart machine” implies a machine that is better connected, more flexible, more efficient and safe. It can quickly respond to new demands. Based upon a collection of smart, connected products, it maximizes efficiency through intuitive collaboration with its users. A smart machine also is capable of participating in predictive maintenance practices while minimizing its own environmental footprint and total cost of ownership.
Efficiency and Self-Awareness. With the use of sensors and the intrinsic knowledge regarding its own capabilities and features, a smart machine will be able to monitor its own key components as well as environmental conditions. Embedded intelligence will correlate upstream and downstream behavior and adapt its own parameters within given business rules. By providing relevant information to several classes of users — operators, connected data consumers at the OEM, and the end user — the smart machine will allow manufacturing lines to produce in a more reliable, flexible and efficient manner. Such optimization can be implemented with respect to energy, time, overall equipment effectiveness (OEE), load shedding, quality or other parameters via upstream systems that provide setpoints based on analytics.
Specific heat treatment applications include:
- The addition of environmental sensors in control panels to monitor control instrument terminal temperatures. It has been demonstrated that controlling this temperature — via cabinet fans or air-conditioning units — can significantly improve calibration drift performance.
- The ability to use process material specifications to refine operating cycle parameters. For instance, embedding auto-calibration technology into sensors in online carburizing applications can significantly improve the overall process accuracy.
- The discrete monitoring of quality (defect information), maintenance (downtime periods) and production activities (turnaround times). In turn, quality, maintenance and production can be improved by utilizing IIoT solutions to coordinate and combine these separate activities into a single OEE key performance indicator, facilitating a better understanding of the overall plant performance.
- The use of power sequencing across an Ethernet backbone to enable automated load-balancing and load-shedding techniques via SCRs.
Silicon-controlled rectifier (SCR) power controllers, or thyristors, were developed to provide a precise method of electrical switching in the control of power circuits in heating applications. They also were designed to overcome the limitations and lifetime issues of mechanical contactors.
SCR technology now is being used to take an isolated controller and provide a system approach to managed power demand. A number of SCR devices will fire randomly at what could be many times per second. If the units fire at the same time, then the load demand increases. By using power sequencing across an Ethernet backbone to enable automated load-balancing and load-shedding techniques, it is possible to order the firing pattern and reduce the ultimate peak-load demand. These smart, connected devices can be used for single- or multi-zoned equipment and also can be leveraged across multi-equipment cells.
This level of machine monitoring also facilitates preventive maintenance supported by the supplier or OEM, helping to avoid component failure and associated downtime or damage to the machine or components. It also allows for maintenance to be scheduled — minimizing the impact on production — while increasing business opportunities for value-added services.
Efficiency and Data Management. Smart machines must have the appropriate level of intelligence to assess data quickly and in a decentralized fashion. Routing all data to a central control for analysis will quickly lead to delays as it is a non-scalable structure. Sensors, components and machinery with the intelligence to only share data that falls outside of set parameters will lead to better overall data management. Improving the level of data shared with the broader network/community will accelerate decision making and reduce backlogs that could lead to critical information being delayed or missed altogether.
It is common to have the control instrumentation for a furnace as close to the equipment as practically possible. This facilitates precise process control and data capture at the source to avoid potential data loss through remote-only solutions. The data then is shared with central systems using a robust store-and-forward system to protect the base data. The regulated aspects of heat treatment, driven by the aerospace (Nadcap/AMS2750E) and automotive industries (TS16949/CQI9 Issue3) dictate the requirements for tamper-resistant data capture and long-term storage.
An extension of this feature set in data-management systems provides security management and full audit trail functionality. These smart data-management systems and digital chart recorders have evolved into mini-productivity stations, allowing key parameters for thermocouple use, calibration tests and machine maintenance to be scheduled directly on the device.
The final storage of data also is an important consideration. To date, local hardware largely has been used to store production data, but this method can be time consuming and expensive to manage. Storing data in secure internet-based computing resources — more commonly known as the cloud — is increasingly becoming a viable option to help better manage data in a cost-effective manner for long-term storage. For instance, using the cloud, a company could employ retention periods of 30 years or more.
Process Data Safety and Security. Improvements in machine performance and lifetime cost reductions cannot be offset by reducing the safety or security of the machine or production line. With security built into their fundamental designs, smart machines will improve the safety of operators and minimize the security risk of increased networking. The ability to utilize a mix of safety components and controllers will allow machine builders to fit the solution to specific end-user application requirements, helping to improve overall performance and productivity.
Currently, data security is the leading inhibitor of end-user adoption of new networking technologies and work processes. The perceived risk of networking components and machinery in order to achieve production benefits is high.
Particularly with IIoT and increasing levels of connectivity, security needs to be considered at numerous levels. Security provision needs to be multilayer, incorporating hardware, software and services. Machine builders and automation-component vendors must ensure that end-users are aware of security vulnerabilities and can manage network infrastructure to minimize the risk of a breach. User cases and success stories can help educate end-users about the benefits of smart machines and how security can be maintained.
Any new smart machines will need to be compatible with the existing installations or machinery from multiple OEMs. End users want devices that can be installed within a short timeframe. Integration into the rest of the system must be easy.
Industry-hardened scalable data hubs for heat treatment are one way to take direct input — from wired sensors and digital signals from other local devices that capture data near the source — and provide a secure link to both plant-based business systems for internal analysis and storage in secure, remote data centers.
Smart Ovens and Furnaces Provide Flexibility
The lifecycle of today’s machines does not allow monolithic or single-purpose design. The fast development driven by time-to-market constraints force OEMs to shift towards mechatronic design and modularity. This trend also continues in the software and application part of modern machines. Smart machines will benefit from templates of proven designs for products ranging from simple software functions to fully functional modules describing mechanics, electrical, motion, interfaces, features and behavior.
Reusable Design. Machine builders embrace concepts that are proven, reliable and validated. Modularity is one enabler where the paradigm to reuse software and hardware in a different context requires a new level of thinking. The concept of clear and strict interfaces with well-defined behavior that can be tested comes from the IT world. It is finding its space in automation — with some adaptation. The concept of furnace total validated documented architecture (TVDA) solutions will provide building blocks for furnace control panel design and become common in the future. This becomes another key smart-machine differentiator.
Connectivity. Smart machines will connect directly to the broader Ethernet-based network. This allows data sharing and production planning that goes far beyond the capabilities of traditional stand-alone machinery and automation. Smart machines will bridge the gap between IT and OT, making available production data that can be used in numerous management settings (e.g., stock control, operator scheduling, maintenance, energy management and product replacement). A basic requirement for this: standards to put values and parameters in a meaningful context and a common language.
Heat treatment quality standards helped drive the development of direct communication links between the control device and the recording instrument to eliminate errors created by conventional retransmission methods. Ethernet-based Modbus TCP was used for device-to-device communications to accurately transfer the control data to the process record. This instrument-to-instrument communication has been a feature of IIoT development over the past 10 years.
It is recognized that to increase the value attributed to automation projects, the links between the equipment, operator and supply-and-delivery chain need to be further developed. Workflow-type applications are leading the charge in this area. Below are some examples:
- Production applications include embedded regulatory IP such as the CQI9 process tables into software workflow solutions. This provides a link between the machines and all of the key decision makers in real time. Integration helps ensure that regulatory requirements are constantly achieved and that automatic alerts are raised for any standards breach on a batch-by-batch basis.
- Digital compliance and calibration solutions simplify planning, scheduling and deployment of instrument calibration tests to minimize oven or furnace downtime. The digital applications improve process efficiency and reduce the risk of human error when completing status checks by providing an easy-to-follow sequence of calibration steps. Calibration and compliance status is available online instantly to help ensure audit readiness. Digital storage also eliminates all the associated issues with storage of paper records. By using a smartphone to scan a QR code posted on the furnace, the user can find records via instant access to certificates.
- The calibration data (non-process data) provides real-time performance insights. Shared in real time with the OEM or supplier via secure data centers, the calibration data can be used to assess the health of the equipment control system. Drift performance and other data can be used to assess service requirements.
In conclusion, thermal processing ovens and furnaces historically were characterized by limited communication technology. New smart machines are using established communication protocols, IIoT devices and cloud computing resources to enable:
- Lifecycle cost reductions.
- Machine performance improvements.
- New ways to interact with all workers.
The new IIoT technologies and practices are evolving over time. Before a large-scale transition to smart machinery occurs, affected workers will require education, and executives will require a clear demonstration of return on investment. The new technologies will need to prove themselves over time in an industrial environment, and inhibitors such as security concerns will need to be overcome.
Digital Mobility and Smart Ovens
In ever greater numbers, machine operators and factory-floor engineers are embracing the use of mobile devices at work. Personnel no longer need to be in close proximity to a machine in order to monitor or manage performance. These devices provide operators with the flexibility to move around while still accessing machinery data. Also, machine engineers can diagnose problems and offer guidance remotely, which also speeds up implementation of a solution. This reduces downtime and losses from component failure.
Access to process data is not new: Instruments with web-server ability have been in general use in the heat treatment industry for the last 10 years. In addition, the ability to view data from multiple machines — either on the premises or remotely a secure VPN connection — has been a standard on sophisticated data-management systems. What has changed is that technology developments now allow secure, remote access via entry-level instrumentation.
Augmented-reality applications will provide users the ability to have a virtual view of their production environment. An example application is a virtual view of the inside of a furnace control cabinet and overlaying the status of devices (environmental condition, alarm status, etc.). This can lead to quicker diagnosis of any maintenance issues and, in certain cases, removes the need to halt production to access the inside of the control panel.
The skill-set required to design and operate an IIoT-based system are somewhat different from those needed to run a classical automation system. Retraining will be required for existing operators and maintenance staff to manage such systems. The good news is that the IIoT systems will use technologies that are familiar in everyday life. Young operators will have no problems adapting to this new approach.
The main challenge for automation suppliers will be to design and supply diagnostics/debug tools that can rapidly identify the root cause of problems. This will ensure that a malfunctioning or downed system can be restored quickly.