Citation
Sanders, D. (2009), "Ambient-intelligence, rapid-prototyping and where real people might fit into factories of the future", Assembly Automation, Vol. 29 No. 3. https://doi.org/10.1108/aa.2009.03329caa.002
Publisher
:Emerald Group Publishing Limited
Copyright © 2009, Emerald Group Publishing Limited
Ambient-intelligence, rapid-prototyping and where real people might fit into factories of the future
Article Type: Viewpoint From: Assembly Automation, Volume 29, Issue 3
Keywords Rapid prototypes, Electric machines, Artificial intelligence, Factories
Artificial intelligence systems have been improving for a decade (Sanders, 1999) and new advances in machine intelligence may not be far away. Ambient-intelligence has been developing to create seamless interactions between people and digital systems (Riva et al., 2005). These electro-mechanical systems will be personalized and responsive to people. In industrial research, a less human and more system-centred definition of ambient-intelligence is emerging but industrial use is limited (Stokic et al., 2007). Although the introduction of ambient-intelligence into assembly and manufacturing is slow (Sanders et al., 2008), it promises to bring improvements in flexibility, reconfigurability and reliability.
This brings us to a point in history when our human biology appears too frail, slow and over-complicated in many industrial situations (Sanders, 2008c). To overcome this, we are beginning to mix sensor systems (Sanders, 2008b) and some powerful new technologies to overcome those weaknesses, and the longer we use that technology, the more we are getting out of it. We use less energy, space, and time, but get more and more output for less cost. The time may be coming when a human being will be able to think of an object and then watch it appear before their eyes (Brooks, 2000). Rapid-prototyping is already automatically constructing physical objects using solid freeform fabrication and proving to have advantages over high-speed machining for manufacturing prototypes.
Rapid-prototyping takes virtual designs from CAD or modelling software and transforms them into thin, virtual, horizontal cross-sections. Then, it creates each cross-section in physical space, one after the other, until a model is finished; a WYSIWYG process where the virtual model and the physical model are almost identical. In the Regional Centre for Manufacturing Industry at Portsmouth University, machines read in data from drawings and lay-down successive layers to build up a model from a series of cross-sections. This additive fabrication is able to create almost any shape by manufacturing solid objects through the sequential delivery of energy and material to specified points in space to produce a part (Hopkinson et al., 2005; Wohlers, 2007). As rapid-prototyping becomes rapid-manufacturing (Assembly Automation, 2007), a number of competing technologies are becoming available. Some melting or softening material to produce layers (Forbes, 2004) and others are laying liquid materials that are cured (Pham and Dimov, 2001), or in the case of lamination systems, thin layers are cut to shape and joined together.
Rapid-prototyping techniques are being used for manufacture, albeit in small numbers. The new machines doing this are exceeding human performance in increasing numbers of tasks. As they merge with us more intimately and we combine our brain power with computer capacity to deliberate, analyse, deduce, communicate and invent, then we may be on the threshold of a new manufacturing age […] an intelligent age, a collaborative design age, a rapid-manufacturing age.
Rapid-manufacturing is being used in many places and 12 percent of additive fabrication users are now involved in manufacturing (Wohlers, 2007). In 2003, that figure was just 3.7 per cent. Morris Technologies has the world’s highest concentration of Direct Metal Laser Sintering machines and is using them for both prototypes and manufacturing. Boeing is using rapid-prototyping machines to produce parts, tooling and manufacturing aids for the F18 and other military aircraft and millions of hearing aid shells have been produced on stereo-lithography machines at 3D systems. These machines produce complex geometries without considering conventional manufacturing limitations. Additive fabrication methods based on powder metal beds can create parts with interior cavities and features that could not be machined or cast economically. The more complex the geometry, the better that rapid-manufacture looks (Ogando, 2007). Plastic-based processes can likewise create geometries that would be impossible to mould while ignoring design-for-moulding rules. With intelligent design and machinery then designers only consider functional requirements and not manufacturability or design-for-assembly (Sanders, 2009b).
Machine intelligence combines a wide variety of advanced technologies to give machines an ability to learn, adapt, make decisions and display new behaviours (Sanders, 2008c). This is achieved using technologies such as neural networks (Sanders et al., 1996, 2001), expert systems (Hudson et al., 1997; Tewkesbury and Sanders, 1999b), self-organizing maps (Burn and Home, 2008), fuzzy logic (Zoumponos and Aspragathos, 2008) and genetic algorithms (Manikas et al., 2007) and that machine intelligence technology has been developed through its application to many areas, for example:
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assembly (Schraft and Ledermann, 2003; Guru et al., 2004);
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building modelling (Gegov, 2004; Wong et al., 2008);
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computer vision (Bertozzi et al., 2008; Bouganis and Shanahan, 2007; Chester et al., 2007; Sanders, 2009d);
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environmental engineering (Sanders and Hudson, 2000; Patra et al., 2008);
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human-computer interaction (Sanders and Baldwin, 2001; Sanders et al., 2005; Sanders, 2009a, c; Zhao et al., 2008);
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internet use (Bergasa-Suso et al., 2005; Chang et al., 1999; Kress, 2008);
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powered wheelchair assistance (Sanders and Stott, 1999; Sanders and Langner, 2009; Stott and Sanders, 2000; Pei et al., 2007);
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maintenance and inspection (Sensor Review, 2007; Nadakatti et al., 2008);
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medical systems (Assembly Automation, 2008; Cardoso and Cardoso, 2007; Ohbayashi, 2008);
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robotic manipulation (Bullinaria and Li, 2007; Sreekumar et al., 2007; Sanders, 2008a);
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robotic programming (Bogue, 2008; Tewkesbury and Sanders, 1999a; Urwin-Wright et al., 2003); and
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sensing (Sanders, 2007, 2009a; Trivedi and Cheng, 2007).
These developments in machine intelligence are being introduced into rapid-prototyping and rapid manufacture. At the click of a mouse or the flick of a switch or the thought of a brain […] you might have almost anything made to order. We may be close to creating magic boxes that can bring the stuff of imagination into being.
Why do we need people though? If the machine intelligence and ambient systems are so clever then what are people for?
Well, products result from team-efforts rather than individual. Creating a successful product involves a multi-disciplinary group of designers, manufacturers, suppliers and customers (Chang et al., 1999). Designers need to evaluate designs and ability to manufacture and assemble (Sanders, 2009d). Effective collaboration reduces costs and development time, and www-based collaborative systems are already integrating design and manufacturing and providing virtual collaborative environments to view and manipulate designs (Lee and Kim, 2007).
Although an individual may be able to think of interesting things, it is unlikely that those things would be useful products. Designers still need to need to communicate with customers and domain experts and their machines. They may be dispersed widely but distributed product development architectures will allow engineering collaborations across ubiquitous virtual enterprises (Lee and Kim, 2001). New problems here will include security, sharing of product information, synchronization across virtual enterprises and using semantic webs for more human-oriented collaborations.
Globalization and out-sourcing are changing the structure of manufacturing and design processes as it becomes distributed, both organizationally and geographically. Competition is increasing and companies are faced with high rates of technological change, shrinking product life cycles, and intense competition in global, dynamic and fragmented markets. Rapid-prototyping is becoming important in reducing costs and time. Designs can be evaluated using a prototype made in hours or days instead of weeks. Design flaws can be detected and corrected more quickly and new products can be tested and retested much faster.
Rapid-prototyping can increase effective communication, reduce mistakes, minimize engineering changes and extend product lifetime by adding necessary features and eliminating redundant features early. Development time therefore reduces. By allowing engineering, manufacturing, marketing and purchasing to examine a product early in the design process, mistakes can be corrected and changes made while they are easy and inexpensive.
A problem at the moment is that designers and engineers do not know the shortcomings of prototyping materials. Additive fabrication systems may produce parts that are good enough even if they do not exactly match the properties from conventional manufacturing methods but engineers need more data to know whether their prototype parts are safe. At the same time, using prototyping machines to produce finished goods every day brings problems of repeatability, quality, throughput and reliability. Additive fabrication systems are still really prototyping machines that can be coaxed into manufacturing. Even the best direct digital manufacturing systems cannot yet meet the tightest tolerance and surface finish requirements without secondary machining, bench work and polishing (Ogando, 2007).
It may take another decade for engineers to recognize the benefits given the current lack of familiarity and the technical barriers associated with rapid prototyping. It will take a long time for direct digital manufacturing to be considered commonplace […] but it is expanding. Commercial applications are limited at the moment as they can only output single material objects relatively slowly. Most devices are also expensive. However, when it comes to prototyping new components or producing masters for injection moulding or casting, such limitations disappear in the face of relative costs, time savings and limitations of traditional machine-tools.
For the immediate future, advantages will come from research into: CAD and modelling, properties of prototyping materials, creating more capable prototyping machines, quantifying different prototyping techniques, selecting prototyping techniques using new web-based selection tools, and creating new design and collaboration tools.
In the much longer term, people in the factories of the future may use ambient-intelligence systems to create virtual designs. Direct brain-computer interfaces may communicate their ideas to machines and to virtual teams of human stake-holders around the world before rapid-prototyping machines quickly manufacture the agreed designs. Research to get us there will include human-machine interfaces, sensors, artificial intelligence and ambient intelligence.
David Sandersbased at Faculty of Technology, University of Portsmouth, Portsmouth, UK
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