Smart Factory Insights: Hands-off Manufacturing

Assuming perfect manufacturing engineering and reliable process, then product reliability issues are always caused by variation, whether as the result of a simple operational mistake by a person, the wear of a mechanical component on a machine, deviations in material characteristics, or simply a change in the environment. The use of automation has not eliminated causes of unreliability, nor defects, which ironically continues to drive the need for humans to be hands-on, even as part of SMT operations. There is clearly something missing, so cue our digital twin.

With successful manufacturing automation, people are there for their brains, not for their hands. If we were to put a red dot on everything that we touched in manufacturing, whether through necessity, we would quickly have a factory that looked like it had contracted measles. Success can be measured by reducing the number of human touches that need to be made to the product, materials, tools, or machines themselves. This rule applies to assembly itself as well as dealing with exceptions that happen. How then to keep our brains focused while keeping fingers on keyboards and away from danger and still creating world-class process and product reliability?

With each innovation of hardware within manufacturing, we lose first-hand sight of both routine and non-routine events that occur, which include abnormalities and trends that negatively impact reliability and quality. Automation only includes the sensors it needs, assuming other factors are taken care of elsewhere, and unless pushed, typically only shares the minimum of information. When humans were all over the manufacturing line, we were aware of surroundings, seeing issues developing out of the corner of our eyes, that we would remember and address. Without the ability to use our eyes, another form of visibility is needed. We need to gather data. The history of gathering and trusting data from manufacturing has been fraught with challenges—thankfully now being resolved through the increased use of the plug-and-play IPC-CFX standard, which drives IIoT data exchange across the whole shop floor. Of course, data is not visibility; it must be contextualized through the IIoT-based MES layer, which builds event information based on disparate data trails, together with known configuration information, material conditions, work-order perspectives, etc., to create the live digital twin of the manufacturing operation. This visibility is our digital peripheral vision without us interacting directly.

It’s not incorrect to say that the method to improve reliability is to be data-driven, but is very much a simplification. Raw data, proprietary data, and even IPC-CFX are not solutions in themselves, including measurements and inspections. Any data that is gathered must be processed in some way in order to create value. Machine learning famously processes raw machine data measurements to refine and improve the operation. This is incorrect; value comes from the interpretation of results, by another party, such that the determination of whether an escape is really a defect or not, for example the result of an inspection by AOI. Qualification and contextualization bring the value. SMT line closed-loop solutions are another example, where software provides qualification of variation in inspection data coming from one or more machines, then applying corrections and compensation to other machines to keep variation under control. However, it is incorrect to believe that the value of such solutions comes solely from the raw data. Every data point is contextualized by analytic software, based on such things as the dimensions of the PCB, the use of different nozzles for different materials, etc. The type of correction is based on the knowledge of the product, work-order, and materials which do, of course, change. A difference in the material vendor, representing variation potentially in the size, shape, or features of that material, could lead to the closed-loop software making a false call. Holistically, further information needs ultimately to come from MES to understand the complete context of the operation.

These two simple examples illustrate the use of contextualized data to make decisions that impact reliability; in other words, preventing defects from occurring not only during manufacturing, but out into the market. Using trend analysis and refinement in the understanding of the threat that variation represents, a whole slew of hands-on actions is prevented that, in a defect-ridden process, compound quality risk geometrically. There are many other areas where hands-off, data-driven manufacturing ensures that no “out of control” condition is reached to the extent that human action is required. Six Sigma is the leading example of a statistical tool that detects in real-time whether a complex series of data points will remain within control limits, or whether there is a chance that the limits could be exceeded in the near future. Six Sigma is therefore a good engine to be used for “AI-based” monitoring. The problem again is that, as with the case of raw data, the use of Six Sigma is only a means to an end, a tool that needs to be used, with algorithms needed that enable it to be effective for use.

The Active Rules Engine is a great way to think about an AI application that resides within the MES solution, and that is responsible for automatically monitoring a great many contextualized trends, making decisions, and raising alarms, much in the same way as our peripheral vision and thoughts did in the past. Some decisions, such as in the case of SMT line closed-loops, lead to decisions that are executed without the need for any human involvement, becoming more common as we learn how to use our digital-twin visibility more effectively. Some potential decisions, however, need to be referred up to humans, who are now able to manage manufacturing operations much more effectively from their screens, seeing the holistic visibility of the problem, contextualized within the entire operation, rather than having to be physically out on the lines. Such challenges include bottlenecks in the flow of the process, the removal of a suspect material, absence of material or tools, or a potential premature failure of a machine part. Being able to recognize the issue and create a solution avoids the initial quality impact of a defect, as well as avoiding most of the consequences of any failure, all from the comfort of your armchair. Reducing the number of defects found in manufacturing indicates the reduction of market reliability issues.

Now that the contextualized data has taken us so far, there is one more step that we can reliably take to promote hands-off manufacturing. There are likely to be various digital solutions in operation on the shopfloor, which, just like machines, will each do fundamentally different things, and most likely, will come from different suppliers with different core-focused areas of expertise. Take for example, the use of AMR (autonomous mobile robots), a technology that is very useful in executing decisions made by our Active Rules Engine, as well as those escalated to humans. AMR fleet management is dependent on knowing the live status of manufacturing completions and requirements, both routine and exceptional. This can easily be orchestrated through MES in which the Active Rules Engine is based, with the AMR fleet manager driving the detailed specific AMR flow and task management, as they cover material transportation, setup and tear-down of materials and tools, product transfers, and tool management, as well as bringing feeders, nozzles, heads, and stencils, etc., in and out of production for routine maintenance. IIoT-based interoperability between shop-floor solutions is essential in allowing automation to work effectively, with the minimum of manual intervention, no matter what challenges appear, leading potentially to the elusive SMT cleanroom, lights-out operation.

Hands-off manufacturing, by addressing causes and effects of variation in the digital domain, with necessary mechanical activities moved outside of the production area where possible, means that the manufacturing physical and digital twin succeed in levels of reliability and quality that no human nor automated solution could ever achieve unaided.

This column originally appeared in the July 2021 issue of SMT007 Magazine.

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2021

Smart Factory Insights: Hands-off Manufacturing

07-12-2021

The use of automation has not eliminated causes of unreliability, nor defects, which ironically continues to drive the need for humans to be hands-on, even as part of SMT operations. There is clearly something missing, so cue our digital twin.

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Smart Factory Insights: Me and My Digital Twin

04-12-2021

A fully functional digital twin involves more than it may initially seem. At first we tend to think about access to information. There is a great deal of trust to be taken into account when creating a digital twin, as there is scope for its use both for good and evil.

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2020

Smart Factory Insights: Changing Roles in the Digital Factory

12-01-2020

Experts once required to have a knowledge of specialized materials and processes are giving way to those experienced in the application of automated and computerized solutions. Michael Ford describes how it is time to reinvent the expectations and qualifications that we seek in managers, engineers, and production operators to attract and support a different kind of manufacturing innovation.

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Smart Factory Insights: Smart Factories—Indirectly the Death of Test and Inspection

11-04-2020

In the smart factory, test and inspection are reinvented, contributing direct added value, playing a new and critically important role where defects are avoided through the use of data, and creating a completely different value proposition. Michael Ford explains how the digitalized Deming Theory can be explained to those managing budgets and investments to ensure that we move our operations forward digitally in the best way possible.

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Smart Factory Insights: Trust in Time

08-05-2020

We’ve all heard of “just in time” as applied to the supply chain, but with ongoing disruption due to COVID-19, increasing risk motivates us to return to the bad habit of hoarding excess inventory. Michael Ford introduces the concept of "trust in time"—a concept that any operation, regardless of size or location, can utilize today.

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Smart Factory Insights: It’s Not What You Have—It’s How You Use It

06-03-2020

According to the reports, all the machines in the factory are performing well, but the factory itself appears to be in a coma, unable to fulfill critical delivery requirements. Is this a nightmare scenario, or is it happening every day? Trying to help, some managers are requesting further investment in automation, while others are demanding better machine data that explains where it all went wrong. Digital technology to the rescue, or is it making the problem worse?

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Smart Factory Insights: Seeing Around Corners

04-20-2020

Each of us has limitations, strengths, and weaknesses. Our associations with social groups—including our friends, family, teams, schools, companies, towns, counties, countries, etc.—enable us to combine our strengths into a collective, such that we all contribute to an overall measure of excellence. There is strength in numbers. Michael Ford explains how this most human of principles needs to apply to IIoT, smart manufacturing, and AI if we are to reach the next step of smart manufacturing achievement.

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Smart Factory Insights: Size Matters—The Digital Twin

02-01-2020

In the electronics manufacturing space, at least, less is more. Michael Ford considers what the true digital twin is really all about—including the components, uses, and benefits—and emphasizes that it is not just an excuse to show some cool 3D graphics.

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Smart Factory Insights: What You No Longer Need to Learn

01-14-2020

Naturally evolving layers of technological applications allow us to build and make progress, layer by layer, rather than staying relatively stagnant with only incremental improvement. To gain ground in manufacturing, Michael Ford explains how we need to embrace next-layer hardware and software technologies now so that we can focus on applying these solutions as part of a digital factory.

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2019

Smart Factory Insights: Dromology—Time-space Compression in Manufacturing

11-25-2019

Dromology is a new word for many, including Microsoft Word. Dromology resonates as an interesting way to describe changes in the manufacturing process due to technical and business innovation over the last few years, leading us towards Industry 4.0. Michael Ford explores dromology in the assembly factory today.

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Smart Factory Insights: Trends and Opportunities at SMTAI 2019

10-14-2019

SMTAI is more than just a simple trade show. For me, it is an opportunity to meet face to face with colleagues and friends in the industry to talk about and discuss exciting new industry trends, needs, technologies, and ideas.

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Smart Factory Insights: Recognizing the Need for Change

09-24-2019

We are reminded many times in manufacturing, that "you cannot fix what you cannot see" and "you cannot improve what you cannot measure." These annoying aphorisms are all very well as a motivational quip for gaining better visibility of the operation. However, the reality is that there is a lot going on that no-one is seeing.

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Accelerating Tech: Standards-driven, Digital Design Flow for Industry 4.0

04-24-2019

The term “fragmented manufacturing” is a good way to describe current assembly manufacturing challenges in an Industry 4.0 environment. Even in Germany, productivity reportedly continues to decline. To reach the upside of Industry 4.0, data flows relating to design play a major role—one that brings significant opportunity to the overall assembly business.

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The Truth Behind AI

02-28-2019

The term "artificial intelligence" or "AI" has become a source of confusion for many—heralded as part of Industry 4.0, yet associated with the threat of automation replacing human workers. AI is software rather than hardware, and it's time to put these elements of AI into context, enabling us as an industry to embrace the opportunities that so-called AI represents without being drawn in, or pushed away, by the hype.

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2018

Resolving the Productivity Paradox

12-22-2018

The productivity paradox continues to thrive. To a growing number of people and companies, this does not come as a surprise because investment in automation alone is still just an extension of Industry 3.0. There has been a failure to understand and execute what Industry 4.0 really is, which represents fundamental changes to factory operation before any of the clever automation and AI tools can begin to work effectively.

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The Truth About CFX

10-23-2018

A great milestone in digital assembly manufacturing has been reached by having the IPC Connected Factory Exchange (CFX) industrial internet of things (IIoT) standard in place with an established, compelling commitment of adoption. What's the next step?

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Advanced Digitalization Makes Best Practice, Part 2: Adaptive Planning

08-27-2018

For Industry 4.0 operations, Adaptive Planning has the capability of replacing both legacy APS tools, simulations, and even Excel solutions. As time goes on, with increases in the scope, quality and reliability of live data coming from the shop-floor, using for example the CFX, it is expected that Adaptive Planning solutions will become progressively smarter, offering greater guidance while managing constraints as well as optimization.

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Advanced Digitalization Makes Best Practice Part 1: Digital Remastering

07-02-2018

As digitalization and the use of IoT in the manufacturing environment continues to pick up speed, critical changes are enabled, which are needed to achieve the levels of performance and flexibility expected with Industry 4.0. This first part of a series on new digital best practices looks at examples of the traditional barriers to flexibility and value creation, and suggests new digital best practices to see how these barriers can be avoided, or even eliminated.

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Configure to Order: Different by Design

01-15-2018

Perhaps in the future, sentient robots looking back at humans today will consider that we were a somewhat random bunch of people as no two of us are the same. Human actions and choices cannot be predicted reliably, worse even than the weather. As with any team however, our ability to rationalize in many different ways in parallel is, in fact, our strength, creating a kind of biological “fuzzy logic.”

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2017

Counterfeit: A Quality Conundrum

10-01-2017

There is an imminent, critical challenge facing every manufacturer in the industry. The rise in the ingress of counterfeit materials into the supply chain has made them prolific, though yet, the extent is understated. What needs to be faced now is the need for incoming inspection, but at what cost to industry, and does anyone remember how to do it?

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