To fully exploit the potential of connected digital technologies, manufacturers need to consider the whole lifecycle of a product: from initial design and prototyping through to full-scale production and beyond to servicing. Prof Darek Ceglarek introduces the concepts.

The digital transformation of today’s industries to meet the demands of the Fourth Industrial Revolution requires a delicate balancing act: leveraging the differences between the physical, digital, and biological spheres in action.

Implementing high performance production technologies is not enough. A true transformation must take this further, to provide tools that will help industries better understand, and rapidly respond to, environmental, societal and economic changes and challenges.

Emerging technologies such as the Internet of Things (IoT); cyber-physical systems; big data; cloud computing; robotics, and artificial intelligence (AI) are all crucial enablers for digital transformation.

But in my view, what is still lacking is the digital lifecycle framework that can link the design of products and/or manufacturing processes with production; and subsequently, with service systems.

Digital lifecycle management (DLM) provides this transformative framework, integrating individual data from interconnected systems (including those mentioned above), with the multidisciplinary simulation of products and manufacturing processes.

This enables the creation of predictive and prescriptive operations to achieve resilient performance in production systems.

Essentially, this means two things for industry. First, we must develop and implement in-process monitoring. Second, we must develop analytics-driven defect identification, root-cause analysis, and put in place corrective and preventive actions.

For example, in the face of growing competition on a global scale, manufacturers need to shorten the time-to-market. This is closely dependent on their ‘lead time to ramp-up’ and ‘lead time to full production’. To achieve this goal cost efficiently, manufacturers must run product and process design, and ramp up to full production with ‘zero-defect’ capabilities.

Engineering changes after a prototype has been released from the design stage must be minimised, and rapid identification and root-cause analysis of production faults during ramp-up is vital.

The ability to do this successfully allows for a longer period of full production and customer adoption of the finished product, thereby generating more economic value to the manufacturer and overall benefits to society at large.

I believe we need to place a key emphasis on the development of lifecycle analytics that allow integration of engineering models and data from different phases of the product lifecycle through data exploration, visualisation and machine learning.

This would enable the application of CAE and ICT tools for a variety of engineering tasks in the product lifecycle, together with the capabilities to self-recover from product failures and changes.

The principles which guide digital lifecycle management solutions in production systems and factories are underpinned by the fact that manufacturing processes, products and interlinked services are challenged by rapidly evolving external drivers, including new materials; new regulations; new technologies; sustainability, and new services and communications systems in an intensely cost-pressured environment.

Meeting these challenges rapidly and effectively demands the development of tools able to model and simulate both the products, and the processes and services, through the whole product lifecycle, to get things right first time.

As part of my research and development work heading the digital lifecycle management group at WMG (formally the Warwick Manufacturing Group), University of Warwick, I was part of a multi-partner consortium to develop a process to improve ‘right first time’ implementation of remote laser welding (RLW) in an automotive assembly system.

The goal was to develop an engineering platform for an emerging joining technology in the automotive industry that would enable the exploitation of this technology and ultimately support other joining processes in the sector.

The RLW Navigator programme integrated a universal simulation engine and experimental models to precisely model, configure, optimise and control process variation, production throughput, and costs in multi-stage assembly processes.

By bringing together manufacturing system design information (CAD/CAM) with statistical analysis/variation simulation, it could support the early introduction of new processes, and fundamentally change these introductions from current trial-and-error to systematic mathematics-based system and station configuration, optimisation and control.

The new RLW Navigator process required 60% less space on the shop floor, and compared to the traditional resistance spot welding (RSW) process, used five robots instead of 14 and was able to build two car doors in the same cell in less time than the current process for one RSW door.

In addition, the RLW Navigator provided opportunities for enhanced product design. Some of the product features, which are required for RSW, could be eliminated (e.g. notches), reduced (flange width) or modified (the upper frame cross section of a door can be smaller, with the same stiffness requirements).

The advantages of DLM approaches and solutions provide unique opportunities for rapid deployment of new emerging technologies, as well as in-process quality improvement. For projects like the RLW Navigator, this can yield significant improvements in the automotive manufacturing sector, with the potential to ensure its competitive edge in the world markets.

Data warehouses and cloud solutions can be employed across the whole digital lifecycle management product lifecycle. Exploiting the huge variety and amount of data available to manufacturers through lifecycle analytics and data-mining from cloud computing can eliminate product and process failures to ensure zero-defect products and services.

Finally, digital lifecycle management involves innovations that can link cyber-physical systems with industrial informatics and intelligent data analytics. Results from these technologies can significantly impact a range of areas, including automotive; aerospace; consumer goods, and healthcare industries.