What is an Intelligent Manufacturing System (IMS)?
Intelligent Manufacturing System (IMS) is a modern system of manufacturing that integrates the abilities of humans, machines, and processes to achieve the best possible manufacturing outcome. Manufacturing system refers to the entire process of gathering inputs, arranging, and transforming them into the desired output. IMS seeks to achieve optimal utilization of manufacturing resources, minimize wastage, and add value to the business.
The scope of manufacturing is significantly different now, compared to the initial days of the Industrial Revolution. Modern production focuses not only on quantity or quality but also on the preservation of resources and sustainability of processes. Through IMS, producers try to keep up with the continuously evolving industry and the ever-growing demand of consumers.
The use of intelligence in traditional manufacturing systems means bringing flexibility in production processes, analyzing existing processes and their shortfalls, collecting information about the same, and using the information to formulate better processes.
While traditional manufacturing works on the existing knowledge and experience of operators, IMS requires those involved in the process to learn from past production data, understand all complexities, forecasting production outcomes, and find better alternatives.
- Intelligent Manufacturing System (IMS) is a modern system of manufacturing that integrates the abilities of humans, machines, and processes to achieve the best possible manufacturing outcome.
- While traditional manufacturing works on the existing knowledge and experience of operators, IMS requires those involved in the process to learn from past production data, understand all complexities, forecast production outcomes, and find better alternatives.
- Depending upon the level of information technology and the characteristics of its integration with manufacturing systems, IMS can be generalized into three paradigms: Digital Manufacturing, Digital-Networked Manufacturing, and New-Generation Intelligent Manufacturing.
The Need for an Intelligent Manufacturing System
As the upper limits of manufacturing industries’ accomplishments kept on increasing with subsequent phases of the Industrial Revolution, existing human knowledge and competencies faced certain limitations as they failed to keep up with industrial developments. The traditional manufacturing system failed to handle uncertainties and complexities in processes due to the inability of the operator to keep up with the latest technological changes.
As more consumers demanded high-quality products, there was a need to increase production levels and quality of output within a shorter time period without raising the cost of production. The emphasis on resource optimization and sustainable methods of production put further pressure on manufacturers. Existing traditional systems were unable to fulfill the requirements effectively since manual operators could not adapt to the complexities or memorize large amounts of data necessary to forecast production outcomes.
For example, traditional manufacturing systems require the operator to set certain input parameters in machines and equipment before the start of actual production, based on a trial-and-error approach or the operators’ experience. Despite years of experience, machine operators might not know each possible uncertainty or complexity that might arise during production.
So, each time a complexity occurs, the entire process needs to be stopped and refigured in order to deal with the issue. It is not only time-consuming but also very costly. Such delays could have been avoided if the machine itself could record every uncertainty experienced in the past and accordingly suggest appropriate input parameters.
Thus, there was a need to integrate the use of machines and new technology to make quick forecasts and adopt a flexible approach to manufacturing based on self-learning. iT is how the idea of an intelligent manufacturing system developed.
The Three Paradigms of IMS
The concept of Intelligent Manufacturing underwent development over the years to incorporate the latest technological changes into the system of production. Depending upon the level of information technology and the characteristics of its integration with manufacturing systems, IMS can be generalized into the following three models or paradigms:
1. Digital Manufacturing
Also referred to as the first-generation intelligent manufacturing, the digital manufacturing paradigm is the foundation of IMS, on which subsequent paradigms are based. During the 1980s, production plans and output designs started to be made on computers instead of paper charts and graphs.
Digital simulations are made of the factory layout, product design, use of machinery, and labor in order to formulate the best value chain that reduces cost, enhances product quality, and achieves optimal use of resources. Since all of the things are done digitally, it reduces the time required for manufacturing products specific to consumer requirements.
2. Digital-Networked Manufacturing
The second-generation of intelligent manufacturing integrates the use of the internet in the computerized manufacturing system. The internet network connects ideas, processes, and data across various production units and allows collaborative manufacturing.
The networking technology allows collaborative R&D among different enterprises, connects enterprises in the same industry (horizontal integration), connects enterprises operating on different levels of the production process within the same industry (vertical integration), and connects users to enterprises. User interaction is the first step to transform the production process into a user-centric one.
3. New-Generation Intelligent Manufacturing
The integration of Artificial Intelligence (AI) with digital and networking technologies has led to strategic breakthroughs in IMS. The essence of AI in manufacturing processes can completely revolutionize production techniques and replace the need for human intelligence.
Artificial Intelligence gives new-generation intelligent manufacturing the power to engage in R&D and formulate new processes, designs, products, and business models without human intervention. Since machines are faster than humans, AI will not only reduce the time taken for production but also reduce the time taken to innovate and ideate.
Each of the above models is not mutually exclusive; rather, they build upon the characteristics of the previous one. The features of each of the models can be summarized as follows:
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