The automation of industrial manufacturing

| October 14, 2020

Conveyor belts are the backbone of the factory floor. Everything is transported on them from the raw material to the completed product. With today’s multi-product manufacturing process, they are not able to and are not designed to handle thousands of different products moving to constantly change locations.

Manufacturing Multiple Products with Minimal Disruption to the Production Floor

Many companies are solving these problems with advanced robotic technology. To avoid congested areas on factory floors, manufacturers employ Automated guided vehicles (AGV’s) and Autonomous mobile robots (AMR’s) that employ an application development platform and advanced software solution.

Although the term automated and autonomous is used interchangeable by many, they are distinctly different operations procedures in the manufacturing industry:

  • Automated – machinery or equipment performing a set of human functions.
  • Autonomous – machines or equipment that operates independently with no set instruction from humans.

Adopting robotic technology to and optimize your manufacturing process will take you on a four-stage journey of:

  • Entrants – fixed automation robotics (operations are manually programmed)
  • Veteran – Use of the digital twin ( building an algorithm for the entire production process)
  • Pioneer stage – More production processes are automated (task-based programming may be implemented for robots)
  • Visionary – This is where AGVs and AMR’s and AI replace entire conveyor belts and manufacturing systems.

Advances in Modern Robotics

With the introduction of AI, autonomous robots are moving to a new level. AI manufacturing systems make use of algorithms to analyze data it collects from machines and processes. The manufacturing industry is a sector that has large volumes of data that can be processed through AI algorithms.

This process is also known as the Industrial Internet of things (IIoT ). When numerous industrial devices (anything from machinery to planes to engines and computers) are filled with sensors and connect to collect their data. This data is collected through wireless networks and shared.

This process can improve:

Predictive analytic and production output – no need to go through the elimination process when a machine is faulty. The AI algorithm can pinpoint anomalies more quickly and suggest solutions and tools to correct the fault. Production schedules can be synced to enhance production output.

Generative design process – Product details are entered as well as details such as budget and constraints and processes to meet all possible product options. Products can solutions can then be tested to work out suitable manufacturing conditions. Human bias is eliminated.

Improve the process quality – Better product quality is ensured through innovative processes. AI can ensure that the manufacturer meets the required standards and regulations. Technologies like ML and big data are especially suited for this procedure.

Market adaptability – AI has become pivotal in the distribution and supply chain of manufacturers. Through the use of IIoT customer behavior and changes in the markets can be predicted. Strategic planning can be implemented to meet market demand.

Supply chain optimization – Utilizing AI in this process transparency can be achieved. Customer service can be enhanced through faster lead time. Cost and time incurred on warehousing are minimized by the data analysis algorithms.

Implementation of AI in Manufacturing

IIoT budgets may vary from company to company. The key factor for manufacturers are systems that support production. AI can be implemented in small projects, it may be advisable to collaborate with the technician on and implementation plan. This will lead to workers adopting the technology easier.

When several small projects have been implemented successfully, larger projects on the machines can be implemented. When all machines are interconnected the future design of the new factory can begin. Most AI implementations on smaller machines can pay for themselves within 45 days.

IT Experts and Data

AI implementation can be overwhelming, concerns on how to use billions of data points from interconnected machines are common. An assessment with an AI vendor will set you on the right path. First existing data will be run through models to see what they can learn. Older equipment may not have sensors and will have to be added to collect the data.

Most AI vendors do not require that you employ extra expert staff. Beyond an alert that notifies them of an issue, the machine operator may not notice any difference. However, it is advisable to have someone on the business side to analyze and articulate the output.

Manufacturers that adopt this technology early will have a future advantage over their competitors, but this will soon be standard technology with all manufacturers.

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