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Since the term Industry 4.0 was coined in 2011, its associated technologies have developed to allow a data-rich, interconnected, and highly automated form of production called smart manufacturing. Smart manufacturing leverages Industry 4.0, which is characterized by interconnected cyber-physical systems such as intelligent robots and machines that can self-diagnose and warn of possible failures. The proliferating IoT brings more powerful devices and machines with smart sensors that upload continuous streams of usage data to the cloud for analysis.
Businesses should adopt smart manufacturing to help streamline processes, increase productivity, stay competitive, and prepare for the future—including for unprecedented events, such as a pandemic.
AI/machine learning goes hand-in-hand with smart-manufacturing data analysis, as it can process data and recognize patterns in the data much faster than people. Some level of AI is often embedded into smart factories’ cobots and other robotics systems. As the price of AI drops, it is also being used in the microprocessors of edge computing IoT devices and smart-factory machines. AI-based computer vision can also derive insights from video of the factory floor. For example, Drishti’s AI-powered analysis of manual assembly lines can provide worker training, reduce product defects, optimize processes, and more.
Smart manufacturing’s use of robotics is also becoming more diverse and collaborative, as cobots become increasingly popular due to social-distancing mandates. Robots and automated machines vary in levels of AI, autonomous decision-making, sensing ability, communicative ability, and mobility. But generally in smart manufacturing, robotics systems gather a lot of data and are well-connected to the cloud and the smart factory at large.
Additive manufacturing, also known as 3D printing, has revolutionized rapid prototyping and now supplements traditional manufacturing with finished products—or even infrastructure like small-scale buildings and bridges. It’s expected to eventually be used in mass production, as well. Meanwhile, hybrid manufacturing combines metal additive manufacturing with subtractive manufacturing on a single machine to further reduce material waste and produce parts quickly.
Advanced computer numerical control (CNC) machines perform precise multi-axis milling, lathing, cutting, drilling, and other operations from the designs and models of computer-aided manufacturing (CAM) software. Often in smart manufacturing, CNC machines have wireless sensors as part of the IoT.
Smart-manufacturing devices, machines, robots, and so on are typically part of the IoT, meaning they include wireless network-connected sensors that upload data for analysis. With the plummeting cost of sensors, low-cost processors are also increasingly part of IoT devices, which means performing computing tasks locally before uploading to the cloud. That’s known as edge computing. The term IIoT (Industrial Internet of Things) refers to IoT machines on a production line, which can usually perform predictive decision-making based on input data that lower costs and waste.
With cloud computing, IoT sensor data is stored and analyzed with AI/machine-learning algorithms on off-site servers. An example of what the cloud can do for smart manufacturing is the Volkswagen Industrial Cloud, which combines all data from all 122 Volkswagen Group facilities and processes it in real time to make improvements. Long-term, Volkswagen’s goal is to connect more than 30,000 locations from 1,500 suppliers worldwide to the Industrial Cloud and possibly create a market for smart-manufacturing software
Smart manufacturing uses simulation software to create “digital twins” of physical parts and products, which can be tested, validated, and optimized digitally before manufacture. Simulation becomes more valuable the closer the digital twin gets to a precise physical representation.
In modern society, manufacturing is connected to all human activities, and it is a source of products and services that are necessary for human health, safety and well-being. The necessity for decreasing the negative impact of the manufacturing industry has recently
increased. This is getting recognized as a global challenge due to the rapid increase in life quality standards, demand, and the decrease in available resources.
The “sustainable manufacturing” concept refers to all industrial activities from the factory to the customer including all in-between steps (i.e., resources and services that are connected to the manufacturing chain). Since the manufacturing stage is a part of the product’s supply chain, which consumes more energy and resources, implementation of the “design of manufacturing” approach is an important key to achieve sustainability goals. Furthermore, when taking the full perspective of sustainability in consideration, sustainable manufacturing can be an essential strategy to promote better financial performance and satisfy social and environmental objectives and regulations.
Sustainable manufacturing is based on the 6R (i.e., reduce, redesign, reuse, recover, remanufactured, and recycle) instead of the 3R approach (i.e., reduce, reuse, and recycle). In the 6R methodology, “reduce” refers to reducing the efforts of using the resources and energy consumption during manufacturing, resulting in lower waste during the usage stage. The “reuse” perspective is connected to the reuse of the products or the previously manufactured parts after its first lifecycle, which contributes to reducing resource consumption.
“Recycle” is the process of reusing the used materials that are typically considered as waste into new materials or products. Regarding the “Recover”, it occurs when components are collected at the end of the first lifecycle and then disassembled, cleaned, and prepared for the next lifecycle. The act of “Redesign” consists of using methods such as Design for Environment (DfE), to redesign the merchandise to make it further maintainable. In terms of the “remanufacture”, it includes the reusing of a previously used product, restoring it to initial state through the recycling of as many parts as likely without loss of operation. Many efforts have been made to develop models for implementing sustainability in the manufacturing industry.
Some smart manufacturing technologies inherently promote efficient resource use. Simulation software, for instance, can drastically reduce the amount of physical waste by moving many physical tests (such as vehicular crash tests) into simulated environments. Simulation can also predict the durability and lifespan of different materials and test alternate materials for end products with the lowest impact on the planet, Jonnalagadda says. DFM can also introduce sustainability benefits into a product. For example, generative design software uses AI to generate an abundance of design options that can reduce product weight and the amount of material required while maintaining strength and cost.
Cybersecurity and data protection are also top priorities; 58% of manufacturers from the Accelerating Smart Manufacturing study expressed concerns about data and intellectual-property theft from participating in a smart-manufacturing ecosystem.
UK researchers aim to develop new technologies for the production of superior components for the DOD
The US Army Combat Capabilities Development Command’s Army Research Laboratory (DEVCOM ARL) and partners have announced a $50m project to advance manufacturing capabilities in the country. Under the five-year collaboration between DEVCOM ARL, University of Kentucky, the University of Tennessee, Knoxville (UT), UK’s project, ‘Next Generation Materials and Processing Technologies’ (NextGen MatProTech), will receive nearly $23.8m from the US Department of Defense (DoD).
Researchers in the UK aim to develop new materials processing and technologies for the manufacturing of superior products and components for DoD and civilian use. The project will generate new discoveries and identify high-potential technological innovations. It will also strive to meet the strategic research needs of the country in materials and processes as identified by the National Academy of Sciences, the National Academy of Engineering and the National Academy of Medicine.
The researchers will collaborate with army engineers and scientists to pursue new nanostructured metal alloys and advanced composite materials. For this, they will use methods such as smart, sustainable and hybrid manufacturing processes.
Four primary research areas have been identified by the team: engineered high-temperature materials; advanced additive manufacturing; novel manufacturing processes and predictive modelling; and performance assessment. Additionally, 13 UK researchers will take part in initiatives that are identified as relevant to these research areas in seven project topics.
