Digital twin is often an integral component of a smart factory system
Published on : Wednesday 04-01-2023
Dick Slansky, Senior Analyst, PLM & Engineering Design Tools, ARC Advisory Group, Boston.
Do you feel that India has finally caught up in deployment of automatic machines and processes at all levels of Industry? Which verticals are ahead and which are lagging?
I believe India is now embracing new technologies. According to the World Intellectual Property Organisation's (WIPO) Global Innovation Index (GII) Report of 2022, India has made dramatic progress in climbing up the ladder of innovation, with its rank improving from 81st in 2015 to 40 in 2022. It ranks first among the lower-middle income countries as well as in central and southern Asia. Innovation initiatives in India cover a wide range like space technology, smart cities, healthcare and telecommunications. India's recent progress in vaccine development and COWIN application for Covid-19 vaccination are due to concerted efforts by all stakeholders.
No specific industry verticals are lagging behind as all have realised the benefits of implementing automation: flexibility, scalability, improved quality and productivity. However, the extent of adoption varies. The use of cobots (collaborative robots) across industries should be more widespread.
Manufacturing has operated for a long time in silos of the verticals. Smart Manufacturing actually tries to change this idea towards networking and collaboration. What are the big stumbling blocks in M2M communication and also sharing of data between departments?
A smart factory works by integrating production systems, machines, workers, and real-time data into a single, digitally connected ecosystem. A smart factory not only accesses and analyses data, but it can also learn from experience using today's AI/ML technology to optimise processes with predictive and prescriptive analytics. It interprets and gains insights from data sets to forecast trends and events and to recommend and implement smart manufacturing workflows and automated processes. These cognitive manufacturing ecosystems rely on the access of real-time operational information that drives predictive and prescriptive analytics. Today, the implementation of a digital twin is often an integral component of a smart factory system. Establishing communications across the digital thread and the product life cycle of design/build/operate/maintain is essential.
In Smart Manufacturing, two topics find frequent mention. The first is OEE – which can be measured by through-put or capacity utilisation. The second topic is Quality. Is the emphasis same for different types of manufacturing?
In the context of today’s manufacturing environment where the Industrial Internet of Things (IIoT) and Manufacturing 4.0 have mandated that factories must be intelligent and connected, the definition of a Smart Factory would also include established manufacturing methods, such as OEE improvement, quality cost reduction, inventory reduction, speed-to-market time reduction, operating cost reduction, and energy efficiency. Each of these areas represent candidates for process improvement that the technology and methodology of a smart factory can directly address and more. Both OEE and Quality Management systems are equally essential to successful manufacturing operations. The key to good implementation of these systems for a smart connected factory is based on comprehensive and integrated and dynamic data architecture.
Many manufacturing industries have a machine shop at the core for generating structures and products. There are new technologies for metal cutting like lasers, water jets, etc. How does this affect the traditional machine tools industry? Secondly, would additive manufacturing make a mainstream impact or stay restricted to rapid prototyping?
Using 3D printing (Additive Manufacturing) to fabricate production parts is now an established part of fabrication and production across multiple industries and has moved well beyond prototyping parts. For example, the automotive industry is second only to aerospace and defence in the use of AM for production. Most vehicles produced today have a wide assortment of AM fabricated parts incorporated into the overall assembly.
In the next 5-10 years, AM will become the standard manufacturing technology. Innovative designs enabled by generative design methods based on AI algorithms and the use of new materials will become common when removed from the constraints of traditional manufacturing processes. Additionally, these designs will be part of a continuously improving process of production efficiency and optimisation. AM and its complementary technologies will allow for more consolidation of individual parts, and a more streamlined manufacturing process overall, with these designs requiring less assembly time and reduced maintenance in the field.
One area where AM is evolving significantly is in direct manufacturing. This is where, due to the advancement of the next generation of 3D printing machines, AM is beginning to be adopted for more volume production capacities. As more companies produce printed parts in larger volumes, and at scale, the price points for additive technology and materials continues to drop. Moreover, as printing techniques and part resolution continuously improve, and newly developed “digitally” materials consisting of tunable micro-structures emerge, this will usher in a new dimension of applied material science and advanced production processes.
Industry 4.0 brings with it the concept of small batch size manufacturing to cater for a wide range of product spectrum. Traditional machine tools are geared for large volume high speed production and not so well suited for short production runs. What will change?
In the last few years, we have seen the introduction of much more flexible machine tools in respect to adapting to short run production. The next generation of smart machine tools will be enabled with AI to make them much more adaptable in changing the size and duration of a production run. Companies will be able to focus on more customisable products to meet specific customer demands. AI will also significantly improve the maintenance of machine tools with algorithms that can provide much more accurate predictive maintenance, and even do self-healing based preventative maintenance.
Are the concepts of Smart Manufacturing predominant in private industry? Do they equally apply for public sector manufacturers? Do large companies actually nudge their vendors to imbibe these concepts and technologies?
It is becoming increasingly apparent to business leaders that digital transformation is an urgent priority for supply chains and manufacturing operations that hope to be competitive and resilient well into the 2020s. The pandemic further exposed global supply chain weaknesses and industry vulnerabilities. Manufacturers are realising that traditional supply chains and current manufacturing systems are inadequate in today’s global commerce, and there is a real need to shift to more adaptable, agile, and intelligent production systems that are fully digitally enabled. Implementation of Smart Manufacturing technologies is moving from nice to have to a business imperative. Companies understand that in order to remain competitive globally and deal with new business and manufacturing challenges, they will need to adopt a range of Smart Manufacturing technologies.
Dick Slansky is Senior Analyst, PLM & Engineering Design Tools, ARC Advisory Group, Boston. Dick's responsibilities at ARC include directing the research and consulting in the areas of PLM (CAD/CAM/CAE), engineering design tools for both discrete and process industries, Industrial IoT, Advanced Analytics for Production Systems, Digital Twin, Virtual Simulation for Product and Production. Dick brings over 30 years of direct experience in the areas of manufacturing engineering, engineering design tools (CAD/CAM/CAE), N/C programming, controls systems integration, automated assembly systems, embedded systems, software development, and technical project management. Dick provides technical consulting services for discrete manufacturing end users in the aerospace, automotive and other industrial verticals. Additionally, he focuses on engineering design tools for process, energy, and infrastructure.
(The views expressed in interviews are personal, not necessarily of the organisations represented)