Introduction of Smart Manufacturing

 

Over the past several years, the topic of Smart Manufacturing has been a conversation among manufacturing experts, strategists and thought leaders. However, despite its recent coverage in the press and in journal articles, many in the front lines of manufacturing aren’t quite sure what Smart Manufacturing entails, its importance or how it’s even relevant to their organization. If you’re sitting there wondering what Smart Manufacturing is, you’re not alone.

To state it simply, it is the use of real-time data and technology when, where and in the forms that are needed by people and machines.

But if you are looking for more comprehensive definitions, there are two from leading organizations. According to the National Institute of Standards and Technology (NIST) Smart Manufacturing are systems that are “fully-integrated, collaborative manufacturing systems that respond in real time to meet changing demands and conditions in the factory, in the supply network, and in customer needs.”

The SMLC definition states, “Smart Manufacturing is the ability to solve existing and future problems via an open infrastructure that allows solutions to be implemented at the speed of business while creating advantaged value.”

Smart Manufacturing is being predicted as the next Industrial Revolution. And, as with many other advances throughout recent years, it all has to do with technology connectivity and the unprecedented access to and contextualization of data. Think of your smartphone, only on a grander scale.

There are “microprocessors” that make it possible for Smart Phones to operate like mini-computers. There’s the “cloud” where almost an unlimited amount of data can be stored and retrieved.

There are “apps” that can be downloaded to help us keep track of what we spend, track the location of people and devices, track how many steps we’ve walked … and the list goes on and on.  SM utilizes all of the same components, addressing the complexities of security, interoperability and intellectual property for manufacturing.

Furthermore, SM integrates data and information from multiple open and vendor applications and products that can be composed to form new solutions.  It can be applied to a single machine line, an entire factory or across a network of suppliers and customers. In fact, linking and integrating among and across all of these in synchronized time is possible.

These improvements make it realistic to manage manufacturing operations with more precision and better collaboration among employees, suppliers and partners. SM will create an open atmosphere where fact based decisions can be made and decision makers will have the trusted data when it’s needed, where it’s needed and in the most useful form. Solving problems will be based on a total picture.

 

Advanced robotics

Advanced industrial robots, also known as smart machines operate autonomously and can communicate directly with manufacturing systems. In some advanced manufacturing contexts, they can work with humans for co-assembly tasks. By evaluating sensory input and distinguishing between different product configurations, these machines are able to solve problems and make decisions independent of people. These robots are able to complete work beyond what they were initially programmed to do and have artificial intelligence that allows them to learn from experience. These machines have the flexibility to be reconfigured and re-purposed. This gives them the ability to respond rapidly to design changes and innovation, which is a competitive advantage over more traditional manufacturing processes. An area of concern surrounding advanced robotics is the safety and well-being of the human workers who interact with robotic systems. Traditionally, measures have been taken to segregate robots from the human workforce, but advances in robotic cognitive ability have opened up opportunities, such as coots, for robots to work collaboratively with people. Advanced robotics systems are ready to transform industrial operations. Compared with conventional robots, advanced robots have superior perception, inerrability, adaptability, and mobility. These improvements permit faster setup, commissioning, and reconfiguration, as well as more efficient and stable operations. The cost of this sophisticated equipment will decline as prices for sensors and computing power decrease, and as software increasingly replaces hardware as the primary driver of functionality. Taken together, these improvements mean that advanced robots will be able to perform many tasks more economically than the previous generation of automated systems.

Producers are now deploying advanced robotics as an essential element of advanced automation that enables the self-controlled factory of the future. Enhancing plant structures and processes with digital technologies can increase productivity and flexibility in both the factory and the supply chain, enabling producers to rapidly adjust to changing customer needs.

Advanced robotics can yield various benefits:

• Productivity: Automation of manual tasks drives higher productivity as advanced robots take over such previously manual tasks as assembly of flexible parts. The ability of advanced robots to self-adjust to changing process parameters improves resilience by eliminating “micro stops” that often occur in con­ventional robotics processes. In addition, advanced robots are significantly easier than conventional robots to set up and reconfigure, if suitable simulation software is available, and they are quicker at learning how to perform tasks. The rapid ramp-up of processes is especially valuable for production systems that require frequent adjustments, such as in response to product changeovers or customization requests.
• Quality: Advanced robots can outperform human workers on some tasks, such as assembly, delivering greater reliability and precision, and thus improving quality.
• Safety: Compared with conventional robots, advanced robots can perform more tasks that are dangerous or physically demanding for human workers—such as tasks performed in hazardous environments or operations that could lead to repetitive-stress injuries. The use of machine-vision technologies that improve robots’ perception can enhance safety, even in fenceless environments.
• Agility: Producers can use advanced robotics in configuring new production systems that meet the rising demand for more product variations, customized products, and product redesigns.

Survey respondents expect multispeed usage, mobile applications, and robotic kitting to become significantly more important. At the same time, however, the importance of collaborative robots in supporting manual tasks, as they are used in many pilot applications today, will decrease relative to other applications. In many cases, to achieve positive economics for investment, robots must replace human workers, rather than merely support them.

 Advanced robotics will have a major effect on the workforce. Jobs that primarily involve routine manual activities (such as loading and unloading machines) are the likeliest to be fully automated. For jobs that involve both routine and no routine tasks, the share of no routine tasks (such as maintenance and shop-floor management) will increase. As manual work shifts toward no routine tasks, workers must acquire more-advanced skills. New job categories will arise as technological adoption introduces new needs. Human roles will shift toward tasks that require technical capabilities and soft skills (such as the ability to take initiative). For example, technicians will deal primarily with errors that automated systems cannot handle.

Most survey participants expect the use of advanced robots to reduce the total number of employees at their company, although regional differences are evident in the results Among participants from Asian companies, 56% expect the number of employees to decline by at least 5% within the next five years. This expectation was strongest among participants from Chinese companies: 67% of them expect the number of employees to decline by at least 5%, and 21% expect the reduction to exceed 20%. Fewer participants from North America (50%) and Europe (44%) expect a decline of at least 5%. Participants from most countries expect demand for white-collar workers to increase.

 

3D printing

The 3D printing process builds a three-dimensional object from a computer-aided design (CAD) model, usually by successively adding material layer by layer, which is why it is also called additive manufacturing. The term “3D printing” covers a variety of processes in which material is joined or solidified under computer control to create a three-dimensional object,with material being added together (such as liquid molecules or powder grains being fused together), typically layer by layer. In the 1990s, 3D-printing techniques were considered suitable only for the production of functional or aesthetic prototypes and a more appropriate term for it was rapid prototyping. As of 2019, the precision, repeatability, and material range have increased to the point that some 3D-printing processes are considered viable as an industrial-production technology, whereby the term additive manufacturing can be used synonymously with “3D printing”. One of the key advantages of 3D printing is the ability to produce very complex shapes or geometries, and a prerequisite for producing any 3D printed part is a digital 3D model or a CAD file.

The most-commonly used 3D-printing process (46% as of 2018) is a material extrusion technique called fused deposition modeling (FDM). While FDM technology was invented after the other two most popular technologies, stereo lithography (SLA) and selective laser sintering (SLS), FDM is typically the most inexpensive of the three by a large margin, which lends to the popularity of the process.

Finishing

Though the printer-produced resolution is sufficient for many applications, greater accuracy can be achieved by printing a slightly oversized version of the desired object in standard resolution and then removing material using a higher-resolution subtractive process.

The layered structure of all Additive Manufacturing processes leads inevitably to a stair-stepping effect on part surfaces which are curved or tilted in respect to the building platform. The effects strongly depend on the orientation of a part surface inside the building process.

Some printable polymers such as ABS, allow the surface finish to be smoothed and improved using chemical vapor processes based on acetone or similar solvents.

Some additive manufacturing techniques are capable of using multiple materials in the course of constructing parts. These techniques are able to print in multiple colors and color combinations simultaneously, and would not necessarily require painting.

Some printing techniques require internal supports to be built for overhanging features during construction. These supports must be mechanically removed or dissolved upon completion of the print.

All of the commercialized metal 3D printers involve cutting the metal component off the metal substrate after deposition. A new process for the GMAW 3D printing allows for substrate surface modifications to remove aluminum or steel.

 

Materials

Traditionally, 3D Printing focused on polymers for printing, due to the ease of manufacturing and handling polymeric materials. However, the method has rapidly evolved to not only print various polymers but also metals and ceramics, making 3D printing a versatile option for manufacturing.

 

Multi-material 3D printing

A drawback of many existing 3D printing technologies is that they only allow one material to be printed at a time, limiting many potential applications which require the integration of different materials in the same object. Multi-material 3D printing solves this problem by allowing objects of complex and heterogeneous arrangements of materials to be manufactured using a single printer. Here, a material must be specified for each voxel (or 3D printing pixel element) inside the final object volume.

The process can be fraught with complications, however, due to the isolated and monolithic algorithms. Some commercial devices have sought to solve these issues, such as building a Spec2Fab translator, but the progress is still very limited. Nonetheless, in the medical industry, a concept of 3D printed pills and vaccines has been presented. With this new concept, multiple medications can be combined, which will decrease many risks. With more and more applications of multi-material 3D printing, the costs of daily life and high technology development will become inevitably lower.

Metallographic materials of 3D printing are also being researched. By classifying each material, CIMP-3D can systematically perform 3D printing with multiple materials.

 

Smart Manufacturing Paradigms

In this section, the leading visions for the future of smart manufacturing are discussed and for each one, different examples from leading industry and research organizations are presented.

Cyber Physical Systems in Smart Manufacturing

As one of the main elements of Industry 4.0, cyber-physical systems play a significant role in the future of smart manufacturing systems.

• Connection Layer

Systems, comments, machines, and humans are essential parts of manufacturing systems and their contributions highly depend on their connection with the rest of the manufacturing elements. For instance, an operator may require advanced intelligence from the production system in order to make the most efficient decisions for scheduling maintenance or production orders. Advanced communication technologies such as 5G technology would significantly enhance connectivity across manufacturing systems.

• Cyber Layer

This layer is a central hub for data storage where big data analytic tools are utilized for a better and more efficient decision making. A digital twin can be realized in this layer by integrating cyberspace to the physical components through tactile internet. Moreover, similarity-based methods can be used to perform peer to peer comparison across machines and help in better fault diagnosis and enhancing their efficiency.

• Cognition layer

In this layer, info graphic tools are utilized to present the results of analytic studies to the users. Simple radar charts and degradation trends can be used for a simple representation of the component’s health condition. Then operators can easily make a decision based on the presented data.

• Configuration layer

In this layer, the decisions made in the Cognition layer are applied to the physical system to make the systems self-adapt, self-configure, and self-resilient.

 

Digital Twin Shop-Floor

One of the specific challenges to achieve smart manufacturing is to converge the manufacturing physical space and the virtual space, for realizing smart operations in the manufacturing process. In this context, the shop-floor, as a basic unit of manufacturing, should achieve the interaction and fusion between physical and virtual spaces, which is not only the imperative demand of smart manufacturing but also an evolving trend. Accordingly, the novel concept of digital twin shop-floor (DTS) based on digital twin (DT) is introduced. In this chapter, the evolution path of shop-floor is analyzed first, then the concept of DTS and its four key components are discussed, including physical shop-floor (PS), virtual shop-floor (VS), shop-floor service system (SSS), and shop-floor digital twin data (SDTD). The operation process and interaction mechanism for DTS are studied and the characteristics, key technologies, as well as challenges ahead are investigat