Factory 2030 – The ‘Coming of Age’ of The Smart Factory
In December 1913, Henry Ford introduced the first moving assembly line enabling the mass production of an entire automobile. The innovation reduced the assembly time of a vehicle build from more than 12 hours to just 93 minutes.
When the Model T was introduced by Ford in 1908 he continued to work on building vehicles more efficiently. The famous Ford quote, to a gathered group of salesmen, “The customer can have any color he wants as long as its black” came about since black was the fastest drying paint color at the time. Model T’s only came in black for twelve out of its nineteen model years. Colors were added and included green, bright red, dark blue, brown, maroon and gray proving that the consumer even back then wanted choice and thus the age of flexible manufacturing was also born.
Much has changed in the world of manufacturing over the past 100 years; the changes about to be realized by the next generation of manufacturing concepts will bring about a 4th manufacturing revolution and dramatically change how the manufacturing supply chain integrates with the customer.
Factory 2030, is a Smart Factory, that is Industry 4.0 compliant whereby all machinery and equipment are connected allowing real-time monitoring to ultimately make decisions without human involvement. A combination of cyber-physical systems and the Industrial Internet of Things (IIoT) will make it all possible and make the Smart Factory a reality. One characteristic of the factory of the future is its ultimate flexibility whereby it will be able to mass produce products with a batch size of just one – allowing products to be fully customized to the customer needs – a far cry from the early days of ‘cookie cutter’ manufacturing operated Henry Ford.
In order for Factory 2030 to be realized it necessitates the coming together of numerous developing technologies. In this article we touch on many of these and their respective roles in bringing about the Smart Factory.
To connect all equipment it is necessary to use a high speed wireless network with minimum latency. A 5G private network is the foundation for flexible, efficient and responsive manufacturing delivering the digital replica, critical for next generation quality assurance, and one that can warn of predicted machinery faults ahead of time, offering full traceability of real-time quality while providing direct access to plant and equipment performance.
Edge computing is the practice of processing data near the edge of the network, and where the actionable data is being generated locally, instead of in a centralized data-processing warehouse. Edge computing is a distributed, open IT architecture that features decentralized processing power, enabling mobile computing and Internet of Things (IoT) technologies. In edge computing, data is processed by the collection device itself or by a local computation, rather than raw data being transmitted to a data center.
Edge devices reduce data loads and report meaningful, high-level results supplying real-time information. This approach allows the Smart Factory to analyze essential data faster by processing machine-based data closer to the source.
Moving process data directly from the factory floor to the digital cloud creates bandwidth and latency issues that can become a roadblock to real-time reporting. In response, smart sensors, with onboard data processing, efficiently perform computing at the “edge” of the factory network.
Smart sensors are the cornerstone of the Smart Factory. Smart Sensors allow the acquisition of data at high-speed and to readily interpret that data and share results with the factory infrastructure by networking with other machinery to drive downstream decision-making.
Adaptive manufacturing means that the manufacturing process is able to measure and adapt itself in real-time. This closed-loop manufacturing system, integral in almost all aspects of the Smart Factory, means that the manufacturing process is constantly adjusting itself to process variabilities under constant surveillance. Variability examples are tool wear, thermal changes in equipment or environment, material properties, etc.
Not all parts are manufactured to within microns. In cases, where larger manufacturing tolerances are mandated due to process or economic limitations, guided assembly ensures process optimization and improved quality of the assembly product. Measuring parts prior to, or integral within, assembly operations allows assembly to be optimized based upon real-time discrete part dimensional data.
Factory 2030 will move the traditional inspection processes, currently performed off-line, as sample-inspections to 100% inline inspection. The automated inline inspection process provides real-time data on each manufactured part/assembly ensuring any manufacturing issues are be immediately identified and dynamically rectified.
More part inspections will be performed nearline whereby critical manufactured part are measured and sequentially fed into the manufacturing process with their measured data providing the necessary intellect to allow the subsequent process(es) to be adapted accordingly.
A digital twin is a virtual model of the manufacturing process and the product as it completes its manufacturing journey. This pairing of the virtual and physical models allows for the analysis of data and monitoring of systems to head off problems before they occur, prevent downtime and ensuring only ‘perfect parts’ are manufactured.
Artificial Intelligence and Deep Learning
Artificial intelligence is essentially when machines perform tasks that typically would require human intelligence. Artificial intelligence encompasses machine learning, where machines learn by experience and acquire skills without human involvement. Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. All aspects of Smart Factory manufacturing will potentially be driven by machines with intellect allowing real-time decision-making without human engagement.
Quality Process Optimization Ensures Quality Improvements.
In the future manufacturing will less about producing volumes and more about the intelligent and dynamic creation of value. Advancements in digital technologies will lead to the preference for smart factories, where the machinery and equipment can improve processes through self-optimization and automation. The benefits of a smart factory will also extend beyond manufacturing into other functions such as supply chain logistics, planning, and product development.
Smart Factories will be capable of implementing corrective courses of action automatically and also predict and detect quality defect trends sooner together with the respective cause leading to lower defect rates and reduced lead times.
Metrology News will be reporting on new developments in the above emerging technologies throughout 2021 and beyond – all critical components in the delivery of Factory 2030.