By strict definition, artificial intelligence (AI) references computer systems that are capable of performing tasks that are normally done by humans. AI supplants human intelligence — and the risk of human error — by aiding in activities ranging from speech recognition and language translation to visual perceptions and even decision-making.
In manufacturing some argue that AI has been readily embraced, citing the routine use of computer-aided design (CAD), machine vision, and predictive maintenance. While the assertion is accurate, other manufacturing experts argue that these are examples of “weak AI,” meaning despite the appearance of autonomous robotics, the “thinking” is actually the result of human-supervised, programmed outputs.1 The latter are typically proponents of “strong AI,” meaning a system has the intelligence to course-correct if it senses something is amiss en route to its defined goal/output.1
In fact, strong AI is likely the impetus behind the AI market size in manufacturing surpassing $1 billion USD in 2018, and the predicted CAGR in excess of 40% by 2025.2 The impact of AI in manufacturing is undeniable. For the tissue industry in particular, AI is changing the game — and Fabio Perini is changing the face of AI with Self Adjusting Machines (SAM).
SAM and Operational Efficiencies
Quality control is paramount for tissue manufacturers. It is a key contributor in both production optimization and overall equipment effectiveness, yet the historically manual inspection process either in an in-house lab or directly on the line introduces inefficiencies. Delays in analyzing and leveraging feedback means misinterpreting characteristics (e.g., firmness, etc.) or mis-assigning them to operator sensitivity — all of which contribute to waste on the line.
SAM, on the other hand, consistently measure key product characteristics (i.e., firmness, weight, etc.) and archive numerical data at regular intervals. There is no gap in quality testing, no human element in control measures, and no guesswork. Waste is reduced and line efficiency is improved. Further, because SAM self-adjust for parameters previously managed by the line operator — embossing pressure, the Ø set in RB, and the amount of paper that is used to form the log — constant production and constant quality are achieved without fixed human intervention.
SAM, Sustainability, and the Future of Industry 4.0
The AI algorithms SAM gather and depend upon are also insightful tools for manufacturers to determine and adapt to the changing market needs of an Industry 4.0 world.
The more strategic, data-led SAM approach allows for effective use of supply chains, staffing optimization, and practical management of raw materials. The latter is particularly important in terms of environmental benefits. Streamlining waste to manage the energy and raw materials used to obtain usable tissue products is less taxing on resources, in general, and also reduces the need for earmarking additional resources for recycling unnecessary waste.
AI and advanced applications like SAM promote open innovation around digital solutions that help tissue manufacturers succeed and remain socially responsible in a rapidly evolving marketplace. Learn more about the transformation and how Fabio Perini developed a comprehensive infrastructure of smart equipment and services for tissue converters in Digital Tissue™: Harnessing the Power of Industry 4.0.
1MachineDesign, What’s the Difference Between AI and Machine Learning?, June 17, 2017
2MachineDesign, How to Fit Artificial Intelligence into Manufacturing, September 19, 2019