| |March 20209b. Quality 4.0Manufacturers are finding it harder than ever to maintain consistently high levels of quality which is the need of the hour. This is due to rising complex-ity in products development and very short time to market. This new pursuit for quality has been named as Quality 4.0 and AI is at its forefront. Qual-ity issues cost companies a lot of money, but with the use of AI algorithms developed through machine learning, manufacturers can be alerted of initially minor issues causing quality drops.c. Human-robot collaborationAs the adoption of robotics in manufacturing increases, AI will play a major part in ensuring the safety of human personnel as well as giving robots more focus to make decisions that will increase the process excellence based on real-time data collected from production environment. In simple, AI shall facilitate robots to assist human to supplement his capability and relieves from hard work.d. Generative designManufacturers can also make use of artificial intelligence in the design phase. With a clearly de-fined design brief as input, designers and engineers can make use of an AI algorithm, generally referred to as generative design software, to explore all the possible configurations of a solution. e. Market adaptation / Supply chainArtificial intelligence algorithms are used to optimize the supply chain of manufacturing operations and to help them better respond to, and an-ticipate, changes in the market.How AI can be implemented in Industry 4.0The key elements to be considered for Implementation AI in Industry 4.0are· Analytics · Big data · Cloud or Cyber · Domain knowledge· Evidence Analytics is the essen-tial part of AI, which will bring value if other relat-ed elements are present. Big data technology and Cloud are both essen-tial elements, which pro-vide the source of the information and a platform for Industrial AI. Domain knowledge is to understanding the problem, usage of industrial AI solution, collection of right data with the right quality, parameters associated with the physical characteristics of a system / process. Evidence is an essential element for testing AI models and trains them with cumulative learning ability. By gathering data patterns and the evidence associated with those patterns can optimize the AI model to become more accurate, comprehensive and robust.ChallengesThe expectations from Manufacturing AI are ver-satile and enormous and even a partial fulfillment of these expectations would represent unique and real challenges of applying AI to industries. Among the existing challenges and com-plexities, the following ones are of higher im-portance and priority:· Machine-to-machine interactions· Data quality· Cybersecurity.ConclusionTo leverage AI for significant optimization in manufac-turing performance, it's critical that we account for the entire produc-tion process. It goes without say-ing Cyber-physical systems and machine learning are corner-stones of the Industry 4.0 philosophy. AS THE ADOPTION OF ROBOTICS IN MANUFACTURING INCREASES, AI WILL PLAY A MAJOR PART IN ENSURING THE SAFETY OF HUMAN PERSONNELDr. R. Chandran
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