Artificial intelligence in mechanical engineering
The demand for faster, more flexible and more precise production is constantly growing. A survey of managers in the mechanical engineering industry shows that over 65% of respondents report increasing pressure. They need to speed up their production processes and reduce operating costs at the same time. In addition, the growing shortage of qualified specialists is causing problems in many countries. Companies are increasingly reliant on automation and digital technologies to meet these challenges.
Artificial intelligence plays a key role in the transformation of traditional practices in this sector. As a result, more than 70% of companies specializing in mechanical engineering are planning to increase their investments. They want to invest more in AI-based technologies in the next five years (Industry Report, 2023). These investments are aimed at overcoming specific challenges, as illustrated in the following use cases.
Specification and documentation made easy!
A manufacturer of loading equipment, specializing in the development and production of ship and land loading arms, is facing challenges. These challenges can be tackled effectively through the use of AI. These specific challenges include adapting to different types of cargo, complying with international safety regulations and managing production resources efficiently. The following use cases illustrate how AI-supported solutions can transform this company’s internal processes. They also show how customer interactions are being changed by AI-supported solutions. These use cases illuminate routes to individual AI solutions.
Use case A: Artificial intelligence in mechanical engineering: Automated specifications for loading equipment
The specifications for loading equipment are often very complex, as they must meet strict safety standards and be adapted to specific environmental conditions. For example, loading arms for ships must be able to withstand extreme weather conditions and the associated dynamic loads. An AI system can be used to automatically analyze customer specifications and translate them into technical requirements. This not only reduces the error rate during data transfer, but also speeds up the entire design process considerably.
Implementation and benefits
The introduction of an AI-based system for processing customer specifications enables automatic data extraction. This data is fed directly into the design software, allowing technical drawings and construction plans to be created more quickly and accurately. This leads to an optimized production pipeline, shorter development times and increased customer satisfaction thanks to fast and error-free deliveries.
The wow effect of AI automated customer specifications
What has changed for employees? Employees can now concentrate on more complex tasks, while the AI takes over repetitive and error-prone processes. We are talking about significant time and resource savings , which significantly increases the overall efficiency of the company. This step is essential in view of the current challenges in mechanical engineering. It takes into account the exponential nature of technological developments. This increases the capacity for innovation and rapid adaptation to market requirements.
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Use case B: Artificial intelligence in mechanical engineering: compliance and support through AI technologies
Compliance with international norms and safety standards is particularly critical in the area of loading facilities. Typical examples of this are the international safety standards for the loading of hazardous materials and compliance with environmental protection regulations. An AI-driven assistant can serve as a fast and reliable source of information on the latest standards and technical regulations. Employees can get immediate access to the information they need by simply asking the assistant, which is particularly beneficial in the fast-paced production environment.
Implementation and benefits
The AI assistant uses advanced NLP techniques to understand employee requests and provide correct information. In practice, NLP techniques include methods such as tokenization, parsing, entity recognition and sentiment analysis. Tokenization is the breaking down of text into smaller units (tokens), which is the first step in the processing of text data. Parsing refers to the syntactic analysis of sentences in which the structural dependency between words is determined. Named Entity Recognition (NER) describes the process of identifying and classifying important information units in a text, such as names, places and organizations. Sentiment analysis is the process of determining the emotional tone of a text in order to recognize moods such as positivity or negativity.
These techniques enable the AI assistant to analyze human speech. This allows it to understand the context of a request. It can also generate relevant, precise answers. Constant updating of the database with the latest standards and regulations is guaranteed. This ensures that the company is always up to date with the latest compliance requirements. This reduces the risk of non-compliance and promotes consistently high product quality and safety.
The wow effect Internal AI assistant for compliance and technical support
What specifically has changed here for employees? Access to critical information is now almost real-time, allowing employees to make faster and more informed decisions. There is talk of considerable time and resource savings, which helps companies to operate in an agile and compliant manner in a constantly changing global market. This step is essential in view of the current challenges in mechanical engineering and the speed of technological developments.
Is there such a thing as safe AI?
The discussion of these use cases raises a frequently asked question: How can organizations ensure that their AI solutions remain scalable, secure and compliant? This is where enterprise AI architecture comes into play. It is a comprehensive solution that not only integrates individual applications. It comprises an entire ecosystem of AI tools and databases within a company.
Enterprise AI as a solution
Enterprise AI is a comprehensive AI architecture that is specifically designed to seamlessly integrate different AI applications within an organization. This architecture makes it possible to use both internally developed and commercial AI applications effectively and to access a standardized database.
Benefits of enterprise AI
- Scalability: The central management platform allows the performance of the AI systems to be scaled as required, which is critical for the company’s growth.
- Security and data protection: Enterprise AI uses state-of-the-art security mechanisms and data encryption to ensure that all company and customer data is protected at all times.
- Increased efficiency: The integration of various AI tools leads to further automation of work processes, saving time and costs.
Using the example
For the manufacturer of loading equipment, Enterprise AI offers optimization of existing processes. It also offers the opportunity to develop new services. This includes predictive maintenance and optimized customer interactions. Precise predictions of maintenance requirements for loading arms can minimize downtimes and improve customer service.
All well and good. What does the process look like in practice?
The path from the idea to the finished product supported by AI follows a clearly structured process. At the loading equipment manufacturer, the process began with a workshop. There, key employees came together to identify and define relevant AI use cases based on the company’s actual requirements and challenges.
Workshop and use case definition
In a dynamic environment, three main use cases for AI were identified through brainstorming that had the potential to realize significant efficiency gains and quality improvements. This workshop laid the foundation for further development by motivating the team and preparing them for the innovations to come.
Assessment and timetable
The initial workshop phase was followed by a comprehensive on-site assessment to carry out an in-depth analysis of the selected AI use cases. The project team worked closely with various departments to ensure that all technical and operational details were recorded correctly. This analysis resulted in a clear roadmap for the development and implementation of the AI solutions.
Design sprint and prototyping
In the subsequent design sprint, the developed ideas were translated into concrete work packages . The project team carried out work shadowing in the relevant departments in order to develop a deeper understanding of the specific work processes. In several development sprints, a functioning prototype was created, which was intensively tested and optimized to ensure the highest possible effectiveness and reliability.
Quality assurance and implementation
After the prototype was completed, extensive tests were carried out. The tests checked the quality targets set. They ensured that the AI solutions met the company’s strict requirements. The successful review led to the final implementation of the solutions in the production environment. There, they immediately had a significant impact on the efficiency and quality of the production processes.
To find out more about customized AI solutions, the process flow or your specific use case, or to take the first step towards more effective production, please contact Katja Tibbe, Head of Sales, at or by phone on +49 221 999 697-30.
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