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Enhancing Manufacturing Efficiency: OEE Measurement Through Sensors

Optimizing operational efficiency in manufacturing is crucial for businesses seeking to stay competitive. One powerful tool for measuring and enhancing manufacturing performance is overall equipment effectiveness (OEE). By leveraging sensor technology, manufacturers can gain valuable insights into their production processes, enabling them to identify areas for improvement, reduce downtime, and boost overall productivity.

What is OEE?

OEE is a metric for measuring the efficiency and productivity of a manufacturing process, including three key factors: availability, performance, and quality. Availability measures the percentage of time that equipment is available for production, while performance measures the speed at which the equipment runs. Quality measures the rate of products that meet the required quality standards. Combining these factors, OEE provides a comprehensive view of how well a manufacturing process performs and can help determine the need for improvements.

Sensors: the building blocks of OEE measurement

Sensors play an important role in helping manufacturers determine the effective use of equipment. Following are some key metrics that sensors can track:

    • Machine health monitoring: Sensors can continuously monitor the condition of machines, detecting anomalies and potential breakdowns before they escalate. Predictive maintenance, facilitated by sensor data, helps reduce unplanned downtime, increasing equipment availability.
    • Production tracking: Sensors can track production rates and cycle times, comparing them to target rates. This data empowers businesses to assess equipment performance and identify bottlenecks that hinder optimal efficiency.
    • Quality control: Implementing sensors for real-time quality inspection ensures the prompt identification and removal of defective products from the production line, enhancing the overall quality factor in the OEE calculation.
    • Downtime analysis: Sensors can log and categorize downtime events, providing valuable insights into the root causes of inefficiencies. With this knowledge, manufacturers can implement targeted improvements to reduce downtime and enhance availability.
    • Energy efficiency: Some advanced sensors can monitor energy consumption, allowing businesses to optimize energy usage and contribute to sustainability efforts.

Integrating sensors and OEE measurement

The integration of sensors into the manufacturing process might seem daunting, but it offers numerous benefits that far outweigh the initial investment:

    • Real-time insights: Sensors provide real-time data, enabling manufacturers to monitor performance, quality, and availability metrics continuously. This empowers businesses to take immediate action when issues arise, minimizing the impact on production.
    • Data-driven decision-making: By analyzing sensor-generated data, manufacturers can make informed decisions about process improvements, equipment upgrades, and workforce optimization to enhance OEE.
    • Continuous improvement: OEE measurement with sensors fosters a culture of continuous improvement within the organization. Regularly reviewing OEE data and setting improvement goals drives teams to work collaboratively towards boosting overall efficiency.
    • Increased competitiveness: Manufacturers leveraging sensor-driven OEE measurement gain a competitive edge by optimizing productivity, minimizing downtime, and producing high-quality products consistently.

Measuring OEE using sensors is crucial to achieving operational excellence in modern manufacturing. Using real-time sensor data, manufacturers can identify areas for improvement, reduce waste, and boost productivity. Integrating OEE and sensor technology streamlines production processes and encourages continuous improvement. This approach helps manufacturers stay ahead in the ever-changing industrial landscape.

Read the Automation Insights blog Improving Overall Equipment Effectiveness to learn about the focus areas for winning the biggest improvements in OEE.

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Capacitive, the Other Proximity Sensor

What is the first thing that comes to mind if someone says “proximity sensor?” My guess is the inductive sensor, and justly so because it is the most used sensor in automation today. There are other technologies that use the term proximity in describing the sensing mode, including diffuse or proximity photoelectric sensors that use the reflectivity of the object to change states and proximity mode of ultrasonic sensors that use high-frequency sound waves to detect objects. All these sensors detect objects that are in close proximity to the sensor without making physical contact. One of the most overlooked or forgotten proximity sensors on the market today is the capacitive sensor.

Capacitive sensors are suitable for solving numerous applications. These sensors can be used to detect objects, such as glass, wood, paper, plastic, or ceramic, regardless of material color, texture, or finish. The list goes on and on. Since capacitive sensors can detect virtually anything, they can detect levels of liquids including water, oil, glue, and so forth, and they can detect levels of solids like plastic granules, soap powder, sand, and just about anything else. Levels can be detected either directly, when the sensor touches the medium, or indirectly when it senses the medium through a non-metallic container wall.

Capacitive sensors overview

Like any other sensor, there are certain considerations to account for when applying capacitive, multipurpose sensors, including:

1 – Target

    • Capacitive sensors can detect virtually any material.
    • The target material’s dielectric constant determines the reduction factor of the sensor. Metal / Water > Wood > Plastic > Paper.
    • The target size must be equal to or larger than the sensor face.

2 – Sensing distance

    • The rated sensing distance, or what you see in a catalog, is based on a mild steel target that is the same size as the sensor face.
    • The effective sensing distance considers mounting, supply voltage, and temperature. It is adjusted by the integral potentiometer or other means.
    • Additional influences that affect the sensing distance are the sensor housing shape, sensor face size, and the mounting style of the sensor (flush, non-flush).

3 – Environment

    • Temperatures from 160 to 180°F require special considerations. The high-temperature version sensors should be used in applications above this value.
    • Wet or very humid applications can cause false positives if the dielectric strength of the target is low.
    • In most instances, dust or material buildup can be tuned out if the target dielectric is higher than the dust contamination.

4 – Mounting

    • Installing capacitive sensors is very similar to installing inductive sensors. Flush sensors can be installed flush to the surrounding material. The distance between the sensors is two times the diameter of the sensing distance.
    • Non-flush sensors must have a free area around the sensor at least one diameter of the sensor or the sensing distance.

5 – Connector

    • Quick disconnect – M8 or M12.
    • Potted cable.

6 – Sensor

    • The sensor sensing area or face must be smaller or equal to the target material.
    • Maximum sensing distance is measured on metal – reduction factor will influence all sensing distances.
    • Use flush versions to reduce the effects of the surrounding material. Some plastic sensors will have a reduced sensing range when embedded in metal. Use a flush stainless-steel body to get the full sensing range.

These are just a few things to keep in mind when applying capacitive sensors. There is not “a” capacitive sensor application – but there are many which can be solved cost-effectively and reliably with these sensors.

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Sensor Technology Drivers in Semiconductor Manufacturing

As in many industries, the degree of automation in semiconductor manufacturing is increasing.  The reasons for this are the same as in any industry striving to automate: increase throughput, reduce labor, and improve quality.

However, semiconductor manufacturing presents some unique technical challenges that differentiate it from conventional manufacturing in other industries.  Some of the factors driving sensor technology in automated semiconductor manufacturing include:

  • Small size.  The clean room environment, necessary for semiconductor processing, is very expensive per square foot.  There is constant pressure to reduce the size of the machines, and the sensors that go into them.
    • In fact, the high cost of clean room space is another motivator for reducing the role of humans in the process.  Not only do humans take up a lot of physical space, they represent about 75% of the particle contaminant sources in the clean room.
  • Advanced Process Control (APC).   APC is a method for shortening the time frame between collection of SPC (Statistical Process Control) data and the application of process corrections.  This means that rather than time-consuming external metrology, there is a drive for so-called “in-situ” metrology. There is a need to measure process variables in real-time or near-real time in order to close the APC loop in a shorter time frame.

Continue reading “Sensor Technology Drivers in Semiconductor Manufacturing”

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Online Sources of Sensor Technology News

There are many excellent industrial trade magazines that offer equally valuable online content.  If you are interested in the latest news and information about industrial sensor technology, be sure to subscribe or regularly follow these online publications: