Additive manufacturing (AM) is one use case for 3D printing technology. The other primary use case is rapid prototyping. Both use cases refer to the processes of synthesizing a three-dimensional object from successive layers of material. These objects can be of almost any shape or geometry and are produced from a 3D model or other data source. Additive Manufacturing is currently much less common than rapid prototyping due to the higher standards for quality and cost competitiveness. It is relatively expensive and time consumptive to produce a prototype using traditional manufacturing technologies. 3D printers can dramatically cut both the cost and time in many cases. And the quality of a prototype can generally be below that of a finished product. In contrast, traditional manufacturing technology is excellent at mass-producing finished products. For this reason, Additive Manufacturing is currently used primarily to produce customized products, small batches of replacement components, and designs that are particularly challenging for traditional manufacturing processes.

Advanced planning and scheduling (APS, also known as advanced manufacturing) refers to a manufacturing management process by which raw materials and production capacity are optimally allocated to meet demand. APS is especially well-suited to environments where simpler planning methods cannot adequately address complex trade-offs between competing priorities.

Asset Health Management refers to the process of analyzing the health of an asset as determined by operational requirements. The health of an asset in itself relates to the asset's utility, its need to be replaced, and its need for maintenance. It can be broken down into three key components: 1) Monitoring: Tracking the current operating status of the asset. 2) Diagnostic Analysis: Comparing real-time data to historical data in order to detect anomalies. 3) Prognostic Analysis: Identifying and prioritizing specific actions to maximize the remaining useful life of the asset based on analysis of real-time and historical data.

Autonomous robots are intelligent machines capable of performing tasks in the world independently of either direct human control or fixed programming. Examples range from autonomous drones, to industrial production robots, to your robotic vacuum cleaner. They combine expertise from the fields of Artificial Intelligence, robotics, and information science.The autonomous robot must have the ability to perceive its environment, analyze situational data in order to make decisions based on what it perceives, and then modify its actions based on these decisions. For example, the scope of autonomy could include starting, stopping, maneuvering around obstacles, communicating to obstacles, and using appendages to manipulate obstacles. There are few autonomous robots in operation today. Even most sophisticated, dynamic robots such as those used in an automotive factory perform according to static programming. And most autonomous robots are only semi-autonomous and will likely remain so even as more fundamental autonomy becomes technically feasible. For example, the Roomba vacuum cleaner does not move according to a pre-programmed route and can modify its route dynamically as its environment changes. However, it has a very limited degree of freedom that is determined by its programming.

Autonomous tranportation use control systems and technologies that collect and communicate information flowing between vehicles and any entity that may affect the vehicle. There are several subsets of autonomous transportation, including Vehicle-to-Vehicle communication (V2V) and Vehicle-to-Infrastructure (V2I). In all cases, the system would include sensors, such as lidar, that provide awareness of the surrounding environment. The main goals of autonomous transportation are to provide innovative services relating to different modes of transport and traffic management, to improve automated safety features in vehicles, and to better coordinate the flow of transport networks. Benefits include increasing road utilization efficiency, preventing accidents, and automating parking and tolling processes.

Autonomous transport systems provide unmanned, autonomous transfer of equipment, baggage, people, information or resources from point-to-point with minimal intervention. They can include the full range of transport vehicles, including trucks, buses, trains, metros, ships, and airplanes. They are most commonly deployed in controlled industries zones but are expected to soon be deployed in public areas with varying degrees of autonomy. We differentiate autonomous transport systems from autonomous vehicles. Whereas autonomous vehicles serve individual passengers (who may or may not own the vehicle), autonomous transport systems are interconnected fleets of vehicles owned by a business to service a particular need systematically. When discussing autonomous transport systems, the focus is on the interaction among vehicles in a sophisticated system that interfaces with ERP, MES, and other enterprise data management systems. The autonomy of the vehicle is one component of a larger interconnected system of autonomous and semi-autonomous activity with the objective of achieving business or organizational objectives, such as delivering the mail or moving soil from a mine to a processing facility.

A flexible form of human-machine interaction where the user is in direct contact with the robot while he is guiding and training it. A collaborative robot, or "cobot," is a robot that can safely and effectively interact with human workers while performing simple industrial tasks. However, end-effectors and other environmental conditions may create hazards, and as such risk assessments should be done before using any industrial motion-control application.

A digital thread describes the framework which connects data flows and produces a holistic view of an asset's data across its product lifecycle. This framework addresses protocols, security, and standards. Typically, the digital thread connects digital twins, digital models of physical assets, or groups of assets. A digital twin is the current representation of a product or system, mimicking a company’s machines, controls, workflows, and systems. The digital thread meanwhile is a record of the lifetime of a product or system, from its creation to its disposal.

Visualizing factory operations data is a challenge for many manufacturers today. One of the IIoT initiatives some manufacturers are pursuing today is providing real-time visibility in factory operations and the health of machines. The goal is to improve manufacturing efficiency. The challenge is in combining and correlating diverse data sources that greatly vary in nature, origin, and life cycle. Factory Operations Visibility and Intelligence (FOVI) is designed to collect sensor data generated on the factory floor, production-equipment logs, production plans and statistics, operator information, and to integrate all this and other related information in the cloud. In this way, it can be used to bring visibility to production facilities, analyze and predict outcomes, and support better decisions for improvements.

Fleet management is an administrative approach that allows companies to organize and coordinate work vehicles to improve efficiency, reduce costs, and provide compliance with government regulations. While most commonly used for vehicle tracking, fleet management includes other use cases such as mechanical diagnostics and driver behavior. Automated fleet management solutionsto connect vehicles and monitor driver activities, allowing managers to gain insight into fleet performance and driver behavior. This enables managers to know where vehicles and drivers are at all times, identify potential problems and mitigate risks before they become larger issues that can jeopardize client satisfaction, impact driver safety, or increase costs.

A flexible manufacturing system is a production method that is designed to easily adapt to changes in the type and quantity of the product being manufactured. Machines and computerized systems can be configured to manufacture a variety of parts and handle changing levels of production.

Inventory management solutions aim to automate the inventory management process and increase accuracy and reliability. Every individual inventory item that is to be tracked receives an RFID tag or other similar Tracking technology. Each tag has a unique identification number that contains encoded digital data about an inventory item, for example the model and batch number. Tags are scanned by RFID or other readers. Upon scanning, a reader extracts the tag's ID and transmits it to the cloud for processing. Along with the tag's ID, the cloud receives data about the reader’s location and the time of the reading. Based on this data, an application states the location of the item with the corresponding ID, visualizes the findings and displays real-time updates about inventory items’ movements to the solution users, allowing them to monitor the inventory using a smartphone or a laptop from anywhere, in real time. There are also secondary benefits of inventory management. For example, Machine Learning can forecast the amount of raw materials needed for the upcoming production cycle based on the data about the inventory quantity and location, and reorder them as needed. It can also help in matching demand with supply more accurately as inventory movement is also a representation of demand.

Manufacturing system automation integrates software and machinery so that manufacturing processes are run autonomously through computer programming. The goal of manufacturing system automation is to minimize the amount of human assistance needed in the manufacturing process. These systems provide constant feedback loops and adjust controlling parameteres in response to feedback from PLCs and Smart Sensors installed on machinery. Sensors are commonly embedded in new equipment or can be installed on legacy equipment. Automation has been achieved by various means including mechanical, hydraulic, pneumatic, electrical, electronic devices and computers, usually in combination. The benefit of automation includes a reduction of costs related to labor, electricity, water, gas, and scrap, as well as improvements to quality, accuracy, and precision. Manufacturing system automation can also reduce changeovertimes, thereby enabling small batch size production and mass customization.

Predictive maintenance is a technique that uses condition-monitoring sensors and Machine Learning or rules based algorithms to track the performance of equipment during normal operation and detect possible defects before they result in failure. Predictive Maintenance enables the reduction of both schedule-based maintenance and unplanned reactive maintenance by triggering maintenance calls based on the actual status of the equipment. IoT relies on Predictive Maintenance sensors to capture information, make sense of it, and identify any areas that need attention. Some examples of using Predictive Maintenance and Predictive Maintenance sensors include vibration analysis, oil analysis, thermal imaging, and equipment observation. Visit our condition-based maintenance page to learn more about these methods.

Security claims evaluation is an open and easily configurable cybersecurity platform for the evaluation of Endpoint, gateway, and other networked components’ security capabilities. In an industrial environment setting, monitoring of sensors provides a window into the system and operational efficiencies. Specifically, monitoring key parameters such as temperature, vibration, currents, and voltage provide the operator with insights into whether operations are normal, within normal failure mode, or whether there is an indication of a cybersecurity/security breach. Security claims evaluation provides a platform for users to evaluate whether data from the sensors under test is indicative of normal operation or abnormal operation in a non-invasive and non-intrusive manner. Furthermore, using Machine Learning in combination with real-time analytics capabilities, the sensor operation can be monitored and analyzed 24/7. Logging of abnormal events can be performed for further assessment and future remediation actions. Through running a pre-defined security test suite that encompasses pen testing, known vulnerabilities, and other testing methodologies, testbed users’ security claims can be evaluated at a single or multiple connection points – encompassing an Endpoint to a gateway to cloud assessment. A report based on the test results can be provided to users describing potential security weaknesses and proposed recommendations and remediation methods.

Tamper detection technologies enable a device to detect and initiate appropriate defensive actions against active attempts to compromise the device integrity or the data associated with the device. The tamper detection design can be implemented to sense different types of tampering, depending on the anticipated threats and risks. The solutions used for tamper detection typically include a suite of sensors specialized on a single threat type together with an alert mechanism, which can be audible or sent to a monitoring system. Typical threat types include physical penetration, hot or cold temperature extremes, input voltage variations, input frequency variations, and x-rays.

A time-sensitive network (TSN) is a set of Ethernet standards that will allow time-synchronized low latency streaming services through 802 networks. TSN focuses on creating a convergence between information technology (IT) and industrial operational technology (OT) by extending and adapting existing Ethernet standards. It adds the concept of time to networks so that messages can be delivered within a specific time frame. TSN technology aims to standardize features on OSI-Layer 2 in order that different protocols can share the same infrastructure. TSN as a communication system can achieve its full potential. The three basic components are: 1. Time synchronization: All devices that are participating in real-time communication need to have a common understanding of time 2. Scheduling and traffic shaping: All devices that are participating in real-time communication adhere to the same rules in processing and forwarding communication packets 3. Selection of communication paths, path reservations and fault-tolerance: All devices that are participating in real-time communication adhere to the same rules in selecting communication paths and in reserving bandwidth and time slots, possibly utilizing more than one simultaneous path to achieve fault-tolerance

Track and trace systems provide real-time or periodic updates for the current and historical locations of Containers, vehicles, or other property. Solutions can apply reckoning and reporting of the position of vehicles and Containers that store the tracked property of concern. For example, if it is known the one thousand objects are stored in a Container, it is more cost effective to track the Container than each individual object. However, high value individual objects can also be monitored directly. Wireless tags can be attached to objects with fixed reference points receiving wireless signals from tags to determine their location, as when a pallet is loaded onto a truck. Alternatively, GPS or another technology can track the object using satellite or cellular networks. Examples of real-time locating systems include Tracking products through an assembly line, locating pallets of merchandise in a warehouse, and Tracking Containers as they move across warehouses. The physical layer is usually some form of Radio Frequency communication, but some systems use optical or acoustic technology.

Vehicle Telematics enables the monitoring of location, movement, status, and behavior of a vehicle within a fleet. This is achieved through a combination of a GPS receiver and an electronic GSM device that is installed in each vehicle, which then communicates with the user and cloud-based software. Additional sensors and Actuators may be added to the system to enable additional functionality, such as vehicle remote control and driver status Tracking. Telematics systems provide analytics to determine the optimal route based on location and traffic information, the vehicle's condition, and operational cost prediction.

Visual quality detection automates the analysis of products on the production line or equipment in production facilities for quality control using machine vision. Machine vision is the technology and methods used to provide image-based automatic inspection. It is a system that uses visual computing technology to mechanically “see” the activities that take place one by one along the production line. The components of an automatic inspection system usually include lighting, a camera or other image acquiring device, a processor, software, and output devices. Machine vision surpasses human vision at the quantitative and qualitative measurement of a structured scene because of its speed, accuracy, and repeatability. A machine vision system can easily assess object details too small to be seen by the human eye, and inspect them with greater reliability and lesser error. On a production line, machine vision systems can inspect hundreds or thousands of parts per minute reliably and repeatedly, far exceeding the inspection capabilities of humans. It also uses Artificial Intelligence to mimick human level intelligence to distinguish anomalies, parts, and characters, while tolerating natural variations in complex patterns. It merges the adaptability of human visual inspection with the speed and reliability of a computerized system.