Enabling technologies in advanced manufacturing

This section outlines two enabling technologies in advanced manufacturing: the internet of everything and 3D printing.

The internet of everything in advanced manufacturing

A number of digital technologies have been identified as key enablers — with potentially disruptive capabilities — for advanced manufacturing, including:

• the internet of things and machine to machine connections

• cloud computing

• big data

• analytical software.

Collectively these are increasingly becoming known as the internet of everything — the networked connection of people, process, data and things (Cisco 2016). Common examples of products enabled by these technologies are generally consumer based products including

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mobile phones, wearable fitness devices and smart televisions and fridges. Even though the development of these enabling technologies is driven by other markets, when adopted by manufacturers, these digital solutions are predicted to offer solutions necessary to respond to global economic drivers (CEDA 2014).

What is the internet of things?

The internet of things (IoT) is a group of objects or ‘things’ with network connectivity and computing capability — enabled by embedded information and communication technology such as sensors and actuators — facilitating these objects to collect, exchange and analyse data (Internet Society 2015). This exchange can occur over the internet and/or via other means such as radio frequency identifiers (RFID), near field communication (NFC), or Bluetooth. The key concept is that network connectivity and computing capabilities extend to a range of ‘things’ that are not ordinarily considered to be computers and that connectivity and collection of data is likely to occur with minimal human intervention (OECD 2015a).

In a manufacturing setting, sensors can be spread across the factory floor monitoring and recording a variety of attributes such as temperature or location. This data is then fed back through a network to computers that analyse it, and signals are sent to adjust and improve processes. Potentially every part of a product can have a sensor allowing the possibility to track some aspect of performance. For example, a General Electric sodium nickel battery factory in the United States has over 10 000 sensors. General Electric found that some battery parts failed quality tests after spending too much time on the manufacturing line.

Subsequently, the amount of time particular parts spend in factory ovens and elsewhere on the production line are tracked and ‘alarms flash’ when parts approach a certain time limit (Fitzgerald 2013). Monitoring is not restricted to the factory floor, with the potential to track products throughout the supply chain and post-sale.

Advances in digital technology have increased the functionality of machines from basic monitoring and recording (ascertaining the nature of an environment in the factory such as temperature or humidity) to making decisions that change or control that environment (for instance adjusting settings, such as opening and closing vents, within a factory based on the weather forecast to maintain ideal temperature or humidity in the factory). These tasks or actions can be undertaken remotely or with limited human intervention (OECD 2015a).

While the connectivity between ‘things’ is projected to increase there is much debate about the pace and scale of this change, with estimates of the likely number of connected devices varying widely (chapter 1). Despite this, there is a growing consensus that IoT will be characterised by a rapid increase in the number of connected devices and an evolution in the range of associated applications and services on offer as a consequence.

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Big data, cloud computing and analytical software

Physical objects embedded with sensors facilitate the collection of data — extremely large quantities of data. Big data is an evolving term that describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be analysed for information. Big data can be characterized by the ‘3Vs’: extreme volume of data; wide variety of types of data; velocity at which the data needs to be processed (figure C.2).

Figure C.2 Characteristics of big data – 3Vs

Source: Adapted from Soubra (2012).

The need for cloud computing has been, in part, driven by the growth in big data. Because data sets have become too big to store and analyse using traditional methods, new approaches have emerged — cloud computing. Cloud computing is a general term for the delivery of hosted services over the internet. It enables companies to consume computer resources as a utility — just like electricity — rather than having to build and maintain computing infrastructure in-house. Cloud computing is attractive to businesses as:

• it allows firms to incrementally change their computer storage needs as their demand changes. Maintaining in-house computer infrastructure requires lumpy investment in computer capacity as well as the physical space to store infrastructure

• it provides access to information, via the internet, outside the physical business offices or factory allowing monitoring of manufacturing processes from remote locations.

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Artificial intelligence and robots

Big data and cloud computing have provided a demand for analytical software technologies such as artificial intelligence. With artificial intelligence, computing software examines numerous previous examples of different scenarios and ‘learns’ patterns to enable it to recognise likely future scenarios. For example, a heart rate monitor can be provided with examples of normal heart rate signals allowing it to recognise irregular signals in the future.

Artificial intelligence enables computers to perform tasks that only humans could previously do. Placing artificial intelligence technology in machines — combining it with sensors, IoT and cloud computing — creates robots that can copy a wide range of human capabilities, including visual as well as analytical tasks. It is this technology that allows production processes to be automated, but it can also feature in the pre-and post-production phases of the product life.

3D printing in the advanced manufacturing

3D printing, or additive manufacturing, is a process whereby three dimensional objects are printed from digital information using specialised software applications. The design information, which is captured in ‘slices’, is sent to a 3D printer — much like a laser printer. That information is printed by adding layers on top of layers or slice by slice (figure C.3).

Although the full capabilities of 3D printers are yet to be realised, a diverse range of material can be used in printing including plastic, metal, food products and human tissue (Deloitte 2013; King et al. 2014). There are many applications for 3D printing including automotive, aviation, footwear, jewellery and medical — although some applications are at different stages of advancement.

While there have been large technological developments in 3D printing in recent years, 3D printers largely remain the domain of hobbyists and advanced industry and academia.

Recent consumer demand for 3D printing, usually printing in plastic, has been driven by substantial falls in price. In contrast, 3D printers used in industrial settings with high technical specification are considerably more expensive and consequently their use is not cost-effective for some applications. For example, 3D printing with metal is much more complex than using other materials such as plastic. The equipment also has a high capital cost in the range of $1 million per unit. Due to these challenges, Australian industry has been slow to adopt metal 3D printing (CSIRO 2016a).

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Figure C.3 How 3D printing works

1 A laser source sends a laser beam to solidify the material.

2 The elevator raises and lowers the platform to help lay the layers.

3 The vat contains the material used to create the 3D object.

4 The 3D object is created as parts are layered on top of each other.

Source: Based on Deloitte (2013).

3D printing offers a range of benefits to manufacturers

By its nature, 3D printing uses the material needed and reduces the amount of waste material. Unused material can be recycled for another project (3Designs Products 2016).

Developing prototype designs using 3D printing is faster and reduces costs, especially for low volume products. Traditional prototype manufacturing requires tooling and machining by people — a process that can take weeks for one iteration. Changes to designs, that flow through to patterns, moulds or dies, are often required. These changes can be costly. Then there is the additional time taken to manufacture another prototype. In contrast, 3D printing uses designs developed in a specialised computer program and often take only hours to print. Prototype iterations are faster using 3D printing as there is no time taken in retooling

— instead changes are made to the computer design (3Designs Products 2016). For example, Timberland shoes, using 3D design and printing processes, can design a new shoe sole in approximately 90 minutes at a cost of $35 — a process that previously took one week and cost $1200 (King et al. 2014).

3D printing offers the possibility of product customisation at a market competitive price as 3D digital design can be created or altered to customer specifications without the costs associated with traditional manufacturing moulding and tooling. Customised products include jewellery, dental and medical tools and aids. For example, using a 3D scanner to

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map a patient’s mouth, a mouthpiece can be designed to reduce pauses in breath during sleep (sleep apnoea). The mouthpiece, printed from lightweight titanium and coated with medical grade plastic, is customised for each patient. The mouthpiece retails for $1700 with rebates available from private health insurers (CSIRO 2016b).

3D printing also opens the possibility of designing parts with complex geometric shapes — shapes that are simply not possible using standard construction methods:

The limitations of standard machining have constrained product design for years. With the improvements in additive manufacturing, now the possibilities are endless. Geometry that has been historically difficult or impossible to build; like holes that change direction, unrealistic overhangs, or square interior cavities, is now possible and actually simple to construct.

(Yakos 2014, p. 1)

Software can be used to design parts through consolidation of a number of parts, reducing the weight through lattice and mesh structures and creating unconventional shapes. It can also determine where to put material to optimise the strength to weight ratio.