Quality control is fundamental in every industry, but in manufacturing it’s hyper-critical. Volatile market demand, high material and production costs, alongside the mission-critical nature of end products, impel manufacturers to pursue nothing but first-rate quality and a minimal rejection rate. With the Internet of Things (IoT) gradually hitting its stride across the manufacturing industry, quality management is an area with transformational opportunities.

The Quality Management Challenge at a Glance

Effective quality management relies on the ability to constantly monitor and control a host of machine and process parameters that impact product quality. To ensure product properties are consistent and up-to-par, equipment recalibration is constantly performed as process drifts and other changes in the production line crop up. Yet, with the growing complexity of tooling systems and manufacturing processes, many process variables are left unattended due to the limits of bulky wired networks.

While ideal for high-throughput, time-sensitive automation tasks, wired communications lack the flexibility and affordability needed to capture telemetry data at scale and beyond the machine level. Typically, factors like environmental conditions, despite their major influence on quality variability, are often not studied and controlled. For example, in auto manufacturing, unfavorably low room temperature can reduce the quality of 3D printed components by causing them to cool too quickly.

What’s more, designed in the last century, the majority of wired-driven industrial systems aren’t intended for data exchange beyond the factory floor. This creates disconnected islands of data that aren’t available to enhance production efficiency and throughput. Instead, process optimization and quality management often depend on reactive, manual post-production inspection. Besides expensive human intervention, this introduces significant quality variability and associated costs, while making it challenging to trace the root cause of quality issues.

Enter Industry 4.0: Proactive Quality Management

Figure 2. Quality management applications. (Image courtesy of BehrTech)

The pressing quest for improved process visibility speaks to the tremendous potential of IoT and its counterpart, Industry 4.0, for proactive quality management.

Wireless IoT networks capture a large number of granular critical datapoints along the production line. For example, pressure, vibration, temperature and humidity. With potentially thousands of sensors installed onsite, data is collected as frequently as every 10 – 20 seconds and sent via a base station to the user’s preferred backend system, whether on-premises or in the cloud. Using a remote IoT platform, all sensor data is consolidated for realtime monitoring, actionable insights, and process automation. Alerts can be triggered immediately when any off-spec conditions among running equipment and processes arise. This offers manufacturers unprecedented control over their operations and product outputs. Beyond reactive, end-of-run quality inspection, IoT data empowers a proactive quality assurance approach to diagnose and prevent defects much earlier in the process for peak production throughput and repeatability. This also leads to reduced costs and waste. Concurrently, it provides valuable insights for achieving and maintaining best practices.

Five Leading Applications for Proactive Quality Management:

1. Condition Monitoring and Predictive Maintenance

IoT sensors capture and communicate key health and operational metrics like pressure, vibration, temperature, humidity, and voltage of numerous machines and equipment across the entire industry complex (condition monitoring). Besides generating an insightful picture of current production processes and asset performance, these massive data flows power analytical models to proactively predict an impending issue and schedule demand-based inspection and repair (predictive maintenance). For example, high humidity in the gearbox diminishes the performance of rotary components, resulting in corrosion, impaired product quality, or even machine breakdown. Excessive vibration of motors and pumps suggest possible mounting defects, shaft misalignment, and bearing wear. With predictive maintenance, failures can be prevented ahead of time, thereby maximizing asset utilization and reducing costly losses due to downtime.

2. Environmental Monitoring

Ambient conditions can play a significant role in production and quality management. With the help of environmental sensors that measure temperature, humidity, and air quality, plant operators can remotely monitor and control optimal environments for various factory-wide processes from their command center. For instance, maintaining ideal air pressure differential, prevents dust infiltration in the manufacturing area, thereby securing product quality in the pharmaceutical and microelectronics industries. Gluing and painting processes in automotive production can be improved with optimal humidity level. Likewise, accurate temperature monitoring of processing and storage facilities can ensure product safety in the food industry.

3. Asset Tracking and Management

IoT sensors attached to individual assets such as tools, machinery, and vehicles, capture and report detailed information about current conditions, as well as where and how they are being used. By having a holistic, real-time picture of cross-site assets, operators can quickly pinpoint underutilized equipment, diagnose impending issues and bottlenecks, and easily mobilize tools and parts. Ultimately, the application of IoT for asset management enables organizations to optimize maintenance activities and asset useful life, while eliminating error-prone manual records and excessive orders.

Figure 3. Comparison of wireless technologies. (Image courtesy of BehrTech)

4. Remote Pipeline and Tank Monitoring

Tanks and pipelines are critical assets in many process industries. Overflow or leakage of chemical products and gases not only leads to production losses but also causes serious damage to the environment and threatens public safety. Implementing level, vibration, flow rate, and pressure sensors, businesses can keep an eye on the structural health of their widely distributed tanks and pipelines round the clock, while simultaneously reducing manual checks. Alerts are issued about potential spills, leaks, or ruptures that could lead to disasters. Alerts about low levels of material in tanks can also be issued for timely refilling to improve productivity.

5. Facility Management

IoT enables digitized management and protection of critical plant facilities. IoT-enabled elevators, smoke detectors, fire alarms, and other facility resources across the entire factory can periodically send data on their battery health or “alive” status. This helps manufacturers cut down on time-consuming manual inspection, while being able to quickly respond to any issues that could interrupt the production line.

Future-Proof Wireless Connectivity for Quality 4.0

With data acquisition an inherent challenge in most industrial environments, IoT deployments can often appear to be overwhelmingly complex, expensive, and intimidating. It is predicted that there will be 36.8 billion active IIoT devices by 2025, up from 17.7 billion today. As more companies look to capitalize on new IoT applications, it’s important to consider the long-term reliability, integrability, and manageability of the communication network as it scales to accommodate thousands of connected endpoints. The reality is, it all boils down to choosing the right IoT connectivity for the right business case.

Wireless instrumentation isn’t necessarily new to manufacturing, but crucial requirements in terms of range, power, and ease of integration limit the viable options. For example, industrial monitoring applications could require millions of messages a day to be sent from thousands of sensors. This demands a highly scalable and power efficient solution to avoid frequent battery replacement and disposal that can quickly inflate total cost of ownership. Likewise, vast, structurally dense industrial facilities require reliable wireless communication that can travel a long distance and negotiate physical obstructions. The traditional design of manufacturing facilities also creates challenges. Wireless solutions must be able to integrate with legacy equipment such as PLCs to break down data silos and provide access to previously inaccessible information.

Legacy wireless technologies can’t keep up with the range, power, and cost requirements in IoT sensor networks. Traditional cellular connectivity (e.g. 3G, LTE, etc.) and wireless local area networks (Wi-Fi) are too expensive and power hungry for transmitting small amounts of data from a large number of sensor devices. Other solutions like Bluetooth, Zigbee, and Z-Wave have highly constrained physical range; and even though many of them employ a mesh topology to extend their coverage, multi-hop relaying is power-consuming, while entailing complex network planning and management. As such, mesh networks are suitable for medium-range applications at best.

Low-power wide area networks (LPWAN) are unique in that they overcome these pitfalls and deliver an efficient, affordable and easy-to-deploy solution for massive-scale IoT networks. The appeal of LPWAN is derived from its two signature features: long range and low power consumption. While Wi-Fi and Bluetooth can only communicate over tens or a hundred meters at best, an LPWAN is able to transmit signals up to 15 km in rural areas and up to 5 km in urban, structurally dense areas. On top of that, lightweight, power-optimized protocols reduce transceiver costs while enabling a very long battery life for sensor nodes.

It's important to note, however, that quality-of-service varies across LPWAN technologies. This is mainly due to two reasons – their operations in the license free spectrum and the use of simple asynchronous communication, typically pure ALOHA (a node accesses the channel and sends a message whenever there is data to send). While bringing significant power benefits, uncoordinated transmissions in asynchronous networks greatly increase the chance of packet collisions and data loss. As wireless IoT deployments and radio traffic in the license-free sub-GHz bands rapidly grow, legacy LPWANs potentially come with serious quality of service (QoS) and scalability challenges caused by co-channel interference. In the same regard, the standardization and reliable mobility support are other critical factors not to be overlooked.

Wrapping Up

The ability to identify hidden patterns, predict future issues, forecast usage and costs, and derive insights from IoT sensor data will reshape the industrial process forever. While the sector has been adopting communication technology for some time, new wireless connectivity like LPWAN is helping to bring vastly more data points online at a much lower price tag. Amidst compounding industry challenges, IoT implementation can be a turning point to take quality management and operational efficiency to the next level and stay on top of the competition.

This article was written by Wolfgang Thieme, Chief Product Officer, BehrTech (North York, ON, Canada), For more information, contact Mr. Thieme at wthieme@ behrtech.com or visit here .


Sensor Technology Magazine

This article first appeared in the March, 2021 issue of Sensor Technology Magazine.

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