Smart manufacturing is a broad concept and is not something that can be implemented in a production process directly. It is a combination of various technologies and solutions which collectively, if implemented in a manufacturing ecosystem, is termed as smart manufacturing. All these technology and solution can be categorized under three broad categories as connectivity, intelligence, and flexible automation, and is broadly called as enabling technology.
Data is at the core of growth of smart manufacturing where data will decide what to do and when to do. Analysis of these data will help in making the production process efficient.
Some of the prominent enabling technology in today’s manufacturing ecosystem are mentioned below. Other IT related technology includes Human Machine Interface, Enterprise Manufacturing Intelligence, Plant Asset Management, Manufacturing Execution System, Industrial Communication, and Warehouse Management Systems.
These technologies are yet to evolve and adopted at full scale by the manufacturing ecosystem. However, these technologies may bring big changes in the way manufacturing is being carried out in current scenario. Be sure to brush up on your terms as we enter the age of smart manufacturing.
Digital Twin
In past, Engineers use to create some scientific model to understand the real-world problem. During initial days of industrialization, it was comparatively easy but as machine become sophisticated so does its modelling. Digital twin was in existence roughly from last 50 years but it is now that it is being used in widespread way. This is mainly because of innovation and advancement in sensor and network technology, which helped in digitalizing any existing or new model. Other technologies which compliments the digital twin includes Industrial Internet of Things (IoT), Artificial Intelligence (AI), and Augmented Reality & Virtual Reality (AR/VR).
Blockchain In Manufacturing
Blockchain is still at nascent stage of its development, but it has huge scope in manufacturing ecosystems. In a very simple way where ever there is a transaction, that transaction is validated, recorder (can’t be changed at later stage) and secured, this entire concept is called as blockchain. Supply Chain and smart contracts are some of the important application of blockchain in manufacturing. The blockchain market in manufacturing has yet to evolve fully, and thus we are expecting the market to start generating significant revenue from 2020 onwards.
Automated Guided Vehicle
Automated guided vehicles (AGVs) are used for automated handling of materials. AGVs safely handle and transport all kinds of products, eliminating the need for human intervention in production, logistic, and in warehouse environments. AGVs can increase the efficiency and productivity of material handling operations. Now, many of AGV is also equipped with robotics related accessories which helps AGV to be implemented and perform wider level of application.
Industrial Cyber Security
Smart manufacturing is all about data, so all the equipment, component and systems implemented under the ecosystem of smart manufacturing is driven by data. To protect these data organization should implement robust industrial cybersecurity, as it is important for smooth operation of the manufacturing shop floor. Advanced cybersecurity products provide comprehensive security to critical infrastructure and ensure confidentiality, integrity of the system.
Industrial Machine Vision
Industrial machine vision is a combination of mechanical, optical, electronic, and software systems used to detect defects in objects, surfaces, materials, and manufacturing processes. In recent time machine vision has got huge popularity specially in the ecosystem of smart manufacturing, which helps in improving quality, increasing flexibility, and operational efficiency. Machine vision coupled with robotics systems and Artificial Intelligence will further help in strengthening the manufacturing systems under the concept of smart manufacturing.
Machine Condition Monitoring
Machine condition monitoring is the process of determining operational state and condition of a machine for detecting potential breakdowns with the help of automation. Machine condition monitoring optimizes equipment readiness and reduce maintenance and staffing requirements. It is used to prevent unscheduled outages, reduce downtime, and optimize machine performance. This technique is primarily classified into preventive machine monitoring and predictive machine monitoring. Predictive monitoring allows companies to detect potential trouble, diagnose problems, and choose remedial actions before downtime occurs.
Artificial Intelligence in Manufacturing Market
Manufacturing industry is already experiencing benefit of implementation of artificial intelligent. Collaborative robots, predictive maintenance are few applications where artificial intelligence for manufacturing is implemented. Among these, predictive maintenance uses AI to predict the failure of any machine or its component in advance. Collaborative robots is one such application where human and machine has to work in sink, which is enabled by AI.
Collaborative Robots
Collaborative robots, or co-bots, are robots that are designed to work alongside humans in a workspace to provide enhanced efficiency. These robots are different from industrial robots in a number of ways, such as the absence of “safety fence” while working alongside humans, simplified programming and reduced setup time. Implementation of AI in Co-Bots will further strengthen its capability to work in accordance with Human.
3D Printing
Industrial 3D printing is used in various applications such as tooling, robotics, and special machinery. Robotics forms an important part of industries such as automotive, aerospace & defense, and others. Industrial 3D printing simplifies the expensive and time-consuming process of manufacturing tools, eliminating assembly lines and thereby, reducing labor costs as well. Industrial 3D printing is also used for developing special machinery such as heavy equipment and machinery components.
