methodologies: DMAIC and DMADV. The DMAIC and DMADV methodologies may seem similar but they have different use cases. The DMAIC methodology is used for existing processes or products that aren’t meeting customer’s needs or performing to standards. When a business needs to develop a process that doesn’t exist or when a product has been optimized but falls short of standards, DMADV is used. Lean manufacturing on the other


hand focuses on analyzing the workflow and eliminates waste. It tries to maximize value to the customer while using as few resources as possible. Ultimately, both quality assurance methods improve a company’s manufacturing efficiency while reducing waste and maximizing profit. But what if there is a way to take it one step further? What does the future of Quality Control look like? But, before we talk about that, a general understanding of Industry 4.0 is required.


Industry 4.0 Look at the graphic in Figure 4. How many of these concepts do you know or have heard of? I’ll go so far as to say all of them.


Industry 4.0 began with the rise of IoT and cybersecurity systems which were only made possible to the technological advancements in computing power. These 9 technologies are the enablers of industry 4.0 and allow for manufacturing processes to be completely automated with the help of autonomous robots, cloud storage systems and IoT. Previously unexplored territories


due to limitations in


technology can finally be revealed with things like simulations, big data and analytics, machine learning, augmented reality and additive manufacturing. Industry 4.0, when applied to factories will turn them into SMART factories and when applied to robots, make them work faster and better than ever before. One example is Audi’s Smart Factory.


If you search on Youtube: Audi Smart Factory – Future of Audi production, you’ll see something that might just make your jaw drop. Self-driving vehicles that move heavy cargo, modular robot arms that perform assembly, drones that carry


®


Figure 4: Industry 4.0


Figure 5: An advanced system with I4.0


steering wheels, and more… all in a factory.


Industry 4.0 allows existing quality control methods to evolve and improve.


What is the Cost Associated with a Lack in Quality Control? What happens when a company neglects quality control in favor of profitability? One example that instantly comes to mind is the Boeing 737 debacle. All that time used for training and development simply went to waste. All the cost associated with it used for equipment, maintenance, personnel and more. In the end, Boeing lost close to $50 billion in market cap in the span of 4 months. Was it worth it? At first, the cost of good quality control is expensive, as companies need to invest in cyber security systems, robots, and more. But the question comes down to, will it provide you more value than what you initially invested with? One can argue that the benefits of Industry 4.0 technology are limitless in the long run and sooner or later every company will have to jump on board due to increased competition.


Advanced System Designs Today Figure 5 shows what an advanced system looks like today with I4.0. How many of these steps can be


done through automation and Artificial Intelligence? That’s right. All of them. What’s more is that in some instances, software can enhance the image of the part it inspects and is able to characterize it using different metrology and inspection measurements. As shown below, software can take a Raw image, enhance it with an Automatic Defect Enhancement Filter and process it even further to reduce the noise if required (ADE-N). All of this can be done within a matter of seconds from taking the image. Along with image enhancement,


software can recognize defects in an image given certain criteria and mark whether that part will pass or fail the inspection.


By automating the inspection process and having a consistent way of measuring failed or passed parts, a company can drastically increase production and value. Another way


Continued on pg 24 July 2020 ❘ 23


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