axcontrol, Author at AX Control, Inc.

The Difference Between Rotary and Linear Encoders

According to a recently released MarketWatch analysis⁽¹⁾, the global Motion Control Encoders market is predicted to grow at an annual rate of 4.6%, exceeding $2.1 billion by 2026. Industries like chemical manufacturing, industrial automation, aerospace, and consumer electronics will all be impacted by this growth. Some of the key motion control manufacturers mentioned in this analysis include

Renishaw
Dynapar
BEI Sensors
Baumer Group
Hengstler
Broadcom
Tokyo Sokuteikizai

Non Contact Linear Encoder — A non-contact absolute linear encoder.

Robotics, automation, sorting, imaging, and plotting applications use encoders. The sensing devices provide feedback to determine direction, position, count, or speed. Absolute and incremental encoders can both achieve these ends, but operate differently using different methods of implementation.

In a nutshell, an absolute encoder provides unique position values based upon a digital value of an object’s angular position or motion. These values occur between a stationary pickup device (a zero point) and a moving encoding device, for example, a slotted disc on the shaft of a rotary encoder.

An image of an ElectroCraft BRU-500 series drive. — The BRU-500 brushless drive series works with both absolute and incremental encoders.

Incremental encoders, on the other hand, offer an output signal based upon two square waves that each correspond to a unit of travel read by a photodiode as the load moves. Because incremental encoders begin their count at zero at startup or during a power disruption, they offer no safeguards or reference point regarding position before these events.

Rotary vs Linear Encoders

It’s important to realize the terms “absolute” and “incremental” refer to movement identification. The terms “rotary” and “linear” are more about geometries. It is important to recognize rotary and linear encoders may be either absolute or incremental.

Linear

A linear encoder, as the name implies, measures motion along a line or path. It is designed with a coded strip, or scale, and a sensing head. Incremental linear encoders zero out after any kind of reset. For example, a loss of power or power-down will cause a reset. On the other hand, absolute linear encoders offer their exact position by creating a unique position signal. This position signal is converted into a digital or analog signal for readout purposes. But remember that linear encoders may be optical or magnetic.

For a long time, optical linear encoders were the only option for resolutions below five microns. They offer high resolution and accuracy and can be used in areas with high magnetic fields.

However, optical encoders are mechanically fragile due to the glass optical disk. This can shatter due to impact or vibration. Shock and vibration can also negatively affect internal positioning.

Magnetic linear encoders are less affected by shock and vibration. However, these units use magnetic reader heads combined with a magnetic scale to determine position. This feature creates a sensitivity to anything interfering with the magnetic field such as iron, steel, or nearby magnetic fields.

Rotary

Rotary encoders are used to track rotational motion, including the shaft movement of a number of industrial and commercial devices. Incremental rotary encoders tend to be less complex and more cost-effective than similar absolute encoders, and are adequate for simple pulse counting or for frequency monitoring applications where the resolution is no more than 50,000 PPR. Absolute rotary encoders offer a higher resolution of up to 65,536 pulses per revolution. These encoders also offer multiple interface options including parallel, serial, Ethernet, Fieldbus, and analog.

(1) https://www.industryresearch.co/global-motion-control-encoders-market-15934813

Ladder Logic: Here are the Basics

What is Ladder Logic? A Definition

Ladder logic stems from the history of relays. At one time, relays were the primary control for most automatic systems. These electromechanical devices consisted of coils and contacts that they moved. Energized coils moved their contacts from their resting position to their active position (either closed to open or open to closed.)

Close up image of a real ladder and a light bulb. Visual representation of the inspiration basic ladder logic programming can allow for. — Ladder logic is shaped like an actual ladder, and is read from top to bottom/left to right.

In practical application, a ladder diagram showed how to wire relays together. This diagram looked like a drawing of a standard household ladder with uprights and rungs. Modern-day ladder logic still follows these conventions. Relays did (and still do) their job well, but can be cumbersome due to the sheer size of multiple relays wired together. This is where programmable logic controllers using ladder logic can be advantageous: able to do the same kind of job but in significantly less space.

What is Machine Learning?

What is Machine Learning, Simply?

Machine learning, or rather the idea machines can learn to ‘do’ without an explicit set of instructions (programming), has been the basis of many movies where humans end up getting the short end of the deal. But is machine learning truly that dire?

Unlikely. Machine learning, which is a subcategory of artificial intelligence, is simply a way for machines to imitate intelligent human behavior. It’s a type of data analysis that allows programs to learn via experience in order to complete complex tasks, much like humans problem-solve. This type of learning typically breaks down into two specific types: deep learning and reinforcement learning. But what’s the difference?

Deep Learning

Deep learning is essentially what you see in any young child as they start to understand that while chickens are birds, not all large birds are chickens. It is based upon the ability to classify both the common features (in this case: feathers, beaks, wings, etc) as well as the uncommon features that separate each grouping from each other (sound, size, feather pattern, beak length). This kind of hierarchical feature learning stacks multiple layers of learning nodes as observed data from one layer produces new outputs that are then fed to a higher level.

In deep learning, the machine begins with raw data that must then be sorted into relevant and irrelevant subsets. The machine, exposed to more data, improves over time. This is similiar to how a baby learns.

Reinforcement Learning

Meanwhile, reinforcement learning relies more on trying out slight variations of a problem. As results occur (favorable and unfavorable) data sets change until the best outcome emerges. This is reminiscent of “The Good Place” as Michael tries to create a better version of his neighborhood.

Reinforcement learning uses a closed-loop algorithm where each action receives feedback in a trial-in-error process until the best action is determined.