Artificial intelligence techniques for industrial automation and smart systems

Abstract

Artificial intelligence (AI) has navigated away from public skepticism, back into the limelight in an impactful way. From an application perspective, it is largely accepted that the industrial implications of AI will be significant, even if the broader societal implications are still under question. AI has the power to drive competitiveness in the industrial sphere in a manner that has not been seen in the past. According to a Goldman Sachs report about the foreseeable impact of this formidable technology, businesses which do not learn to leverage AI technologies are at the risk of being left behind in the competitive market of enterprises. A key role that AI techniques will play in industrial environments would undoubtedly be that of automation. Streamlining industrial processes by reducing the redundancy of human intervention is a strategy of importance for businesses to both increase revenue and spend more time on product innovation. The world is entering a new phase of industrialization, commonly termed as Industry 4.0. The application of cutting edge technologies like AI is paramount in building smart systems that allow industries to gain a competitive edge. The industrial transformation is aided in part by smart manufacturing and data exchange which contribute to high-level industrial automation. The Industrial Internet of Things (IIoT) forms an internetwork of a vast number of machinery, tools, and other devices which amalgamate into a smart system that ultimately allow for greater efficiency and productivity in high-stakes situations in industries. Intelligent devices that form a smart system have the ability to use embedded automation software to perform repetitive tasks and solve complex problems autonomously. For this reason, it is generally agreed upon that industrial applications of smart systems using AI would significantly improve reliability, production, and customer satisfaction by improving accuracy and reducing errors at rates beyond human capacity. A Globe Newswire report from 2019 has found that ‘‘AI in industrial machines will reach $415 million globally by 2024 with collaborative robot growth at a compound annual growth rate of 42.5%.’’ Inevitably, the integration of AI algorithms and techniques enhances the ability of enterprises to leverage the power of IIoT and big data analytics to provide value to their market segments. However, some functional challenges hinder the process of integrating industrial activities into the smart machine ecosystem. A particularly persistent problem is that of securely storing, efficiently processing, and profitably analyzing the enormous volume of data that is generated from sensors in the smart systems. Businesses often find it difficult to integrate new technologies into seemingly sturdy existing systems. AI algorithms must be functionally supported by data analytics and smart systems must employ robust security frameworks in order for automation systems to truly help businesses meet their future manufacturing challenges in a cost-effective manner. AI provides a compelling opportunity for businesses to expand their operational efficiency by paving way for more automated industrial processes. This special edition focuses on the AI techniques that can be used to achieve this and the relationship between AI and smart systems to facilitate greater industrial automation. Some topics that are relevant to this theme include, but are not limited to: