Almost everything that is used on a daily basis, including vehicles, computers, coffee makers, and children's toys, is manufactured in a factory. Thus, it makes sense that manufacturing is sometimes referred to as the fundamental pillar of the global economy.
Over the years, manufacturing techniques have evolved with technology. Moving from human-centered approaches to assembly lines that rely heavily on machines to the highly automated facilities that are being adopted today, a lot of trends and technologies have come together to alter the process of production. This transformation is known as digital manufacturing or Industry 4.0.
Especially after the COVID-19 pandemic started, digital manufacturing technologies altered the production processes in a way that was never seen before.
Organizations and leaders responded to supply-chain disruptions that impacted the sourcing and distribution logistics as they worked to guarantee the health and safety of their workforce.
In the post-pandemic world, production facilities will need to react quickly to adapt to new supply sources and changing consumer needs. Digital technologies are crucial because they provide manufacturers with the adaptability and resilience required to mobilize and operate in a new territory under these types of conditions.
What is digital manufacturing and design?
Applying digital technologies to the manufacturing process is known as digital manufacturing. Various sectors use digital manufacturing technologies. For instance, the complete production process (tooling, machining, assembly sequencing, and factory architecture) can be digitally designed by an automotive original equipment manufacturer (OEM) concurrently.
This makes it possible for manufacturing experts to inform designers right away if there are limitations in any step of the manufacturing process. A holistic picture of product and process design is produced by this partnership between manufacturing engineers and designers.
However, the efficiency of the process depends on having the appropriate knowledge at the proper time and location. All departments and functions across the value chain are involved in the effort to connect diverse systems and bridge manufacturing processes.
As a result, the entire product lifetime is influenced, including the stages of product design, production, and servicing. Each stakeholder has quicker access to more precise data due to digital manufacturing technologies. As a result, processes run more smoothly, and corporate decision-making is more efficient.
The whole process of digital manufacturing has been made possible due to the advancements in various digital technologies. In the future, these technologies are expected to evolve even further, which will take the manufacturing industry to the next level. The upcoming technological trends impacting the digital manufacturing industry are mentioned as follows:
1. Fifth generation (5G) network in manufacturing: With the help of 5G networks, businesses can create smart factories and fully utilize innovations such as automation, artificial intelligence, augmented reality, and the Internet of Things (IoT) for problem-solving.
Operators can develop new sources of income using 5G. Manufacturing is one of the most important industries for new revenue potential for operators addressing industry digitalization with 5G technology, along with energy and utilities.
The network qualities required for manufacturing are provided by 5G technology. Critical applications need to enable low latency and high reliability. Widespread connectivity is ensured through high bandwidth and connection density.
Manufacturers presently rely on fixed-line networks to meet these requirements. With the use of mobile 5G technology, factory floor production reconfiguration, layout modifications, and alterations will be possible with greater flexibility and shorter lead times at a cheaper cost.
2. Industrial internet of things (IIoT) in manufacturing: Solutions for the Industrial Internet of Things (IIoT) enable manufacturers to operate more intelligently by utilizing linked assets, real-time data analytics, and monitoring to stay adaptable, informed, and in charge.
One of the best examples of these connected IIoT devices is sensors. Manufacturers may gain insights into the ways in which their machines are operating, improve maintenance procedures, cut down on machine downtime, and even predict when problems might arise using data collected from sensors on manufacturing equipment.
3. Artificial intelligence for accurate predictions in manufacturing: Predictive maintenance, as used in the manufacturing industry, is the process of identifying machine and component failure patterns using sensor data and artificial intelligence (AI).
The theory is that manufacturers may take preventative action and maintain their equipment more efficiently by knowing when a machine or component is likely to break.
By evaluating the data from these sensors, experts can evaluate a machine's status, detect abnormalities, and repair equipment before they break.
Such sensors were previously employed on older motors and transmissions. This demonstrates how predictive maintenance techniques can be used on older equipment as well.
4. Digital twins in manufacturing: A virtual version of an actual component used in the manufacturing process is referred to as a digital twin or digital replica. This digital representation incorporates inputs from a real-world component as an improved computer model. The physical component's state, capabilities, and/or interactions with other devices are reflected in the digital twin.
Digital twins are being used more frequently in manufacturing across all sectors. Technology for visualizing digital twins works well with the sensors that firms employ to collect crucial data on industrial operations.
The data gathered by smart manufacturing systems may be included in interactive and visual models.
5. Robotics in manufacturing: Robots are employed in manufacturing to perform repetitive activities and streamline the workflow of the entire assembly process. Robots and humans work together to manufacture different types of products. Numerous tasks are hazardous or involve working with large amounts of materials, both of which can be risky for human employees.
Due to the repetitive nature of their tasks, employees may grow tired or distracted, even if it is temporary, which can lead to mistakes on their part. Robots, on the other hand, can avoid making such errors due to their dexterity and advanced machine learning capabilities.
Conclusion
The adoption of digital manufacturing technologies is accelerating across industries to improve the efficiency of decision-making in the manufacturing process. It has given various benefits to the manufacturers and business owners, such as cost savings and reduced time to market, and establish a joined-up manufacturing process that integrates digital tools with the actual physical execution through modeling and simulating processes.
Manufacturers can build a digital system through the production process to analyze data from all stages of the product lifecycle and develop processes that can be put into practice by adopting a computer-centered manufacturing process.
