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Smart manufacturing is advancing utilisation

Author : Bill Davis is the Solution Director of Industrial Machinery and Heavy Equipment Industry for Siemens

13 August 2020

According to analysts, manufacturing technology innovations, productivity and business growth are being adopted to address the rise of mass customisation and converging technology advancements for the next generation of manufacturing infrastructures. This is what is called smart manufacturing.

Smart manufacturing – optimising digitally

Smart manufacturing, at its core, is a digital approach that supports companies in optimising all steps of the manufacturing machine process, from machine creation to executing it, manufacturing, and extending into the service life.  Similarly, it supports a growth path for addressing a dynamic marketplace. It provides several benefits by improving manufacturing throughput, uptime, and performance while minimising costs, including overhead, operations, and capital. 

All this empowers smart manufacturing further, enabling machine manufacturers and designers to create additional value by closing the loop between manufacturing operations and engineering. Hence the growing popularity of smart manufacturing.

Smart manufacturing – machining capabilities

Machine builders are incorporating the following abilities to enable smart manufacturing:

Connect – Customers expect machines to communicate with the other machinery in their plant via a machine builder (OEM).

Adapt – With all the information generated by sensors and capabilities, smart machines can recognise changes in upstream products and processes and adjust to these dynamic operating conditions. 

Predict – An increasing emphasis is on the simulation and predictability of a machine’s performance in the field requiring a high-fidelity digital twin of the machine. 

Extende – It is now possible to extend the life of a machine in the customer facility with predictive maintenance and adaptive performance. The objective is to create more value for a manufacturing customer and to optimise the cash flow.

Manufacturing digitalisation leverages the digital twin

Smart manufacturing brings intelligence into every aspect of the manufacturing process, encompassing the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Industry 4.0. It is the integration of intelligence in the actual machines, parts, materials, products, buildings, and supply chain. It also applies that intelligence within a connected, open end-to-end process and infrastructure. With smart manufacturing, data is the master, not the system.

The digital twin is fundamental to implementing smart manufacturing. A digital twin that encompasses the mechanical, electrical and programmable logic control (PLC) enables a comprehensive approach to simulate the machine. For example, when making the machine tool component, an inspection process is added to the manufacturing process for describing what data is to be measured and collected for creating traceability and a close-the-loop process based on a high-fidelity digital twin. 

The next vital step is linking the digital twin of the product with the digital twin of the machine. It includes building the part and executing it, along with managing delivery, manufacturing, operations and quality. Managing manufacturing operations drives greater efficiencies by coordinating all these activities to deliver the correct parts at the right time.

Advancements in part manufacturing, from additive to higher performance multi-axis and combination mill-turn machining centers, require CAM software that can take full advantage in maximising production capacity. Also, companies are incorporating Model-Based Definition (MBD) into 3D models to leverage that information into robotic inspection programs.

Additionally, robotics is becoming a staple in today’s manufacturing environment, with robotic machining and human-assist robots (cobots). Advanced robotics integration is a part of the smart manufacturing solution to simulate robot performance and integration on the factory floor. 

Advanced plant level simulation capabilities track and trace materials through the factory from raw material to project rack to machine and optimise the layout to decrease both high traffic areas and dead zones.

Smart manufacturing also addresses the massive complexity of the machine bill-of-materials (BOM). Each function needs its own view of the BOM that fits the purpose, tracing back to the single source of truth, requiring advanced analytics and capabilities to schedule, manage operations, and execute with quality. It is vital to have this traceability from the engineering bill-of-material to the manufacturing BOM. For example, the recipe for every part must include the CAM code and the quality inspection plan (and results) so that the customer has 100 percent traceability of quality from end to end.

All these capabilities are helping companies take advantage by adopting innovative processes for improving the overall performance of the machine, thus refining products, processes, resolving failures, and improving operations of machinery. 

Industry trends impact on machinery suppliers

Technological advancements are driving industrial machinery companies to fully realise Industry 4.0, with amazing implications. The following trends are re-shaping the engineering, manufacturing and service operations for most machinery suppliers:

Machines automate processes to help companies’ lower costs and expedite delivery of their goods to the end user. Consequently, trends in the broader consumer market in the end define what machinery customers need. A typical consumer product’s development cycle is compressing considerably – lot sizes are smaller, and product life spans are shorter. So, machinery customers need machines that are more flexible and adaptable to an ever-changing product mix, often with customised features or functions that require machine builders to innovate more quickly.

Machinery component suppliers have fully embraced IoT enabled devices. Thus, machinery manufacturers are on a challenging learning curve to take advantage of available information. The number of I/O (input/output device-driven) channels and different communication protocols (wired networks and wireless 5G) provides an order of magnitude increase in information flow compared to recent years. That means automation code developers are forced to choose which channels to use while building more intelligent machines.

Discrete programming is enabling machine users to gain insights from all the IoT information. The hyper-automation trend requires vast amounts of data and cloud-based analytics to accelerate learning about machine behaviour and performance to automate machine functions. Hyper-automation is also enabled by the emergence of low-code tools that help machine users mine data analytics for many business processes – manufacturing optimisation, engineering reliability, and cost reductions. 

Global, highly innovative competition has always existed. Now the challenge comes from more flexible, agile startup companies that begin from the basis of machine learning and are not encumbered by existing business processes or legacy customer engagements. Some offer production-as-a-service and other innovative software-enabled service monitoring tools and machine optimisations – even on competitor’s machines.

Expertise for smart manufacturing 

A comprehensive approach to smart manufacturing enables machine manufacturers, designers and engineers to create additional value to their machines and manufacturing process through multiple high-tech means. Having an executable digital twin is crucial, along with possessing the software for realising and implementing all steps in the manufacturing process, including creation, execution, and service life. The Xcelerator portfolio, from Siemens Digital Industries Software Solutions, offers a fully integrated portfolio of software and services for customisable, industry-specific digital enterprise needs including key capabilities for smart manufacturing.


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