Humans are not Machines - Anthropocentric Human-Machine Symbiosis for Ultra-Flexible Smart Manufacturing

<p>Smart manufacturing is characterized by self-organizing manufacturing systems and processes that can respond to dynamic changes. We envision the rapid advancement of smart machines with empathy skills will enable anthropocentric human-machine teams that can maximize human flexibility and wellness at work while maintaining the required manufacturing productivity and stability. In this paper, we present a future-proofing human-machine symbiosis framework that features human centrality, social wellness, and adaptability. The essential technical challenges and methods are discussed in detail.</p>

Industry 4.0 latest Trends and it's Application

The manufacturing world had undergone four separate industrial revolts. Industry 4.0 is a most recent chapter in the Industrial revolt story that pivoted mainly on manufacturing automation, live data capturing, smart machines, and internet connectivity with key objectives of productivity rise, optimization of available working hours, and increased organization competitiveness. This research paper studies the industry 4.0 through the survey of available literature in a sequential manner that started with understanding the development of the manufacturing industry from industry 1.0 to industry 4.0, the nine key foundation blocks of the industry 4.0, the cyber-physical systems, the Smart factory concept, industry 4.0 application strategy guide step by step, Industry 4.0 benefits, risk, and challenges. We applied the Industry 4.0 concept step by step as a pilot project as per the Industry 4.0 application strategy guide at the bus bar trunking production factory with the target of delivering the demand of customers within committed or shorter lead time from the bus bar manufacturing shop and discussed the outcomes briefly. We concluded that Industry 4.0 helps to improve operator work-life, overall productivity, and to reduce the operating cost of production drastically in mass manufacturing processes. Industry 4.0 is also a boon in today’s changing work culture after the CORONA pandemic through its core concepts of digitization, paperless work, networking, and maximum internet usage. It also guides to adopt automation and smart machines that are communicating in real-time with its customer. We also observed that its application is costly in terms of investment to deploy for small or middle scale manufacturing plants, but they shall implement partly to get the benefit from it.

Man is not a machine – anthropocentric human-machine symbiosis for ultra-flexible smart manufacturing

<p>Smart manufacturing is characterized by self-organizing manufacturing systems and processes that can respond to dynamic changes. We envision the rapid advancement of smart machines with empathy skills will enable anthropocentric human-machine teams that can maximize human flexibility and wellness at work while maintaining the required manufacturing productivity and stability. In this paper, we present a future-proofing human-machine symbiosis framework that features human centrality, social wellness, and adaptability. The essential technical challenges and methods are discussed in detail.</p>

Man is not a machine – anthropocentric human-machine symbiosis for ultra-flexible smart manufacturing

<p>Smart manufacturing is characterized by self-organizing manufacturing systems and processes that can respond to dynamic changes. We envision the rapid advancement of smart machines with empathy skills will enable anthropocentric human-machine teams that can maximize human flexibility and wellness at work while maintaining the required manufacturing productivity and stability. In this paper, we present a future-proofing human-machine symbiosis framework that features human centrality, social wellness, and adaptability. The essential technical challenges and methods are discussed in detail.</p>

Man is not a machine – anthropocentric human-machine symbiosis for ultra-flexible smart manufacturing

<p>Smart manufacturing is characterized by self-organizing manufacturing systems and processes that can respond to dynamic changes. We envision the rapid advancement of smart machines with empathy skills will enable anthropocentric human-machine teams that can maximize human flexibility and wellness at work while maintaining the required manufacturing productivity and stability. In this paper, we present a future-proofing human-machine symbiosis framework that features human centrality, social wellness, and adaptability. The essential technical challenges and methods are discussed in detail.</p>

Man is not a machine – anthropocentric human-machine symbiosis for ultra-flexible smart manufacturing

<p>Smart manufacturing is characterized by self-organizing manufacturing systems and processes that can respond to dynamic changes. We envision the rapid advancement of smart machines with empathy skills will enable anthropocentric human-machine teams that can maximize human flexibility and wellness at work while maintaining the required manufacturing productivity and stability. In this paper, we present a future-proofing human-machine symbiosis framework that features human centrality, social wellness, and adaptability. The essential technical challenges and methods are discussed in detail.</p>

Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0.

The Scope and Applications of Artificial Intelligence in the Medical Sector

The terminology Artificial Intelligence (AI) describes the application computing systems and technology to effectively simulate smart actions and smart thinking compared to the human mind. The concept of AI was introduced as the engineering and science of making smart machines that can operate without the engagement of humans using Machine Learning (ML). This research provides a wider scope of the concept of AI in the medical field, handling the various concepts and terms associated with the concept, including the present and future implementation of the concept. The major research materials applied are Google and PubMed searches, which were conducted using the “Artificial Intelligence” as the basic keyword. More references were retrieved by cross-referencing major publications. The advancements in AI technology in recent times and the present application of medicine have been analyzed critically. This paper ends with an assumption that AI focuses on implementing changes in the medical practices in previously unidentified ways. However, many of the application are still in the initial stages and require exploration and development. In addition, clinical experts have to comprehend and adapt with development for effective delivery of medical services.