Flexible mobile machines are guided by software agents and artificial intelligence (AI) to create the optimum production system for each product. This makes it possible to react quickly to changes on the store floor. Machine manufacturers and plant operators must continuously adapt to global competition. In addition to the existing challenges posed by disrupted supply chains, market conditions and energy costs, there are two key requirements:

• Customer requirements are increasing in complexity, both in terms of the machines themselves and in terms of IT integration.
• The demand for smaller batch sizes requires machines to be retooled quickly for different product variants.

These challenges are increasingly being faced by manufacturing companies and require a high level of innovation on the part of machine manufacturers. At the same time, these market changes are opening up exciting opportunities to improve their own market position, including

• Potential to reduce costs
• Increasing production efficiency
• Flexibilization and reconfigurability of systems

New production concepts

Some of the new requirements break with the previous focus on high cycle rates. They require machine builders and plant operators to re-evaluate existing products and develop new production concepts:

• Resilient value creation networks and supply chains
• Digital twins for materials, processes, production networks and the product as a whole
• Automated product configuration through AI systems
• Self-organizing process route planning

Existing processes are analyzed with the help of process mining methods. These methods help to identify the optimization potential in the production lines. The results reveal deviations and bottlenecks by comparing the planned process with the actual workflow on the store floor.

It becomes clear that although rigid line networks are efficient for fixed product groups, they quickly reach their limits with variable product requirements. Ideally, the line should be individually adapted for each variable product group, but this is currently usually too costly.

The use of autonomous mobile machine tools and intelligent software agents enables the automated adaptation of the production line to the specific requirements of the product. The software agents provide the machines with all the information they need to automatically group themselves into a new production line.

Software agents and agent systems

A software agent fulfills basic core functions:

• It is aware of its environment and can sense it
• It can interact with the environment
• The interaction enables the agent to perform its task optimally

An agent is more than a digital twin, as it acts with foresight, consciously navigates through its environment and achieves goals independently. In this concept, an agent always represents a component of the real system, be it a machine, a workpiece or an order.

In order to support an entire production hall, many agents are required that communicate and cooperate with each other. This creates a decentralized multi-agent system (MAS) that supports the entire production concept.

Flexibility and speed

The implementation of the tasks of the multi-agent system (MAS) on real components requires:

• A connection to the environment by means of sensors and actuators
• Interconnected communication via any communication protocols
• Algorithms for precise target determination

Recently, approaches have been developed to automatically adapt the entire line structure to the product to be produced using a MAS. In these systems, the agents control the line layout using Automated Guided Vehicle (AGV)-based machines, which can be coupled as required and carry out various production and process steps. Control is no longer centralized, but decentralized and implemented by intelligent agents on the store floor.

This development dissolves the traditional hierarchy of the production line and leads to a heterarchy of various equivalent software agents and hardware components. The resulting flexible and independent hardware modules can be used in a variety of ways. The autonomous decentralized system also complies with RAMI 4.0 standards.

AI for finding the agent's goal

AI has become indispensable in many everyday devices: From voice assistants such as Alexa and Siri to autonomous driving. It enables constant adaptation to the environment, including delivery to the customer. In mechanical engineering, AI is generally used in the context of machine learning, either to predict machine states in advance or as part of the manufacturing process to recognize patterns. This forms the basis for technologies such as predictive maintenance and automatic quality control through image analysis.

In the environment outlined above, AI has a new task: to optimize the product-controlled structure of the production line by:

• Determining the optimal use of space on the store floor in order to operate as many production lines as possible simultaneously.
• Carrying out job planning taking space utilization into account in order to meet delivery deadlines and other production specifications.

AI can be trained for both requirements. It always receives the current status of the environment and the planned targets via the MAS, whereupon the AI can react with production forecasts. The AI uses this knowledge to suggest to the agent in the production line the best current route to achieve the target. Depending on the agent's task, this can relate to the line structure, processing in the process, scheduling or any other activity.

The advantage of the overall system is that many steps can be carried out automatically without the need for manual intervention in production planning. The transparency of production is maintained, as is the possibility of manual intervention.

This article was created based on documents from infoteam Software.