Artificial intelligence in industry

Possible applications of industrial AI and machine learning in the production domain can be divided into seven application areas:[4]

• Market & Trend Analysis
• Machinery & Equipment
• Intralogistics
• Production Process
• Supply Chain
• Building
• Product

Taxonomy of application areas and application scenarios for machine learning and artificial intelligence in production.

Each application area can be further divided into specific application scenarios that describe concrete AI/ML scenarios in production. While some application areas have a direct connection to production processes, others cover production adjacent fields like logistics or the factory building.[4]

An example from the application scenario Process Design & Innovation are collaborative robots. Collaborative robotic arms are able to learn the motion and path demonstrated by human operators and perform the same task.[5] Predictive and preventive maintenance through data-driven machine learning are examplary application scenarios from the Machinery & Equipment application area.[4]

In contrast to entirely virtual systems, in which ML applications are already widespread today, real-world production processes are characterized by the interaction between the virtual and the physical world. Data is recorded using sensors and processed on computational entities and, if desired, actions and decisions are translated back into the physical world via actuators or by human operators.[6] This poses major challenges for the application of ML in production engineering systems. These challenges are attributable to the encounter of process, data and model characteristics: The production domain’s high reliability requirements, high risk and loss potential, the multitude of heterogeneous data sources and the non-transparency of ML model functionality impede a faster adoption of ML in real-world production processes.

The challenges for ML applications in production engineering result from the encounter of process, data and ML model characteristics.

In particular, production data comprises a variety of different modalities, semantics and quality.[7] Furthermore, production systems are dynamic, uncertain and complex,[7] and engineering and manufacturing problems are data-rich but information-sparse.[8] Besides that, due the variety of use cases and data characteristics, problem-specific data sets are required, which are difficult to acquire, hindering both practitioners and academic researchers in this domain.[9]

The development of ML applications – starting with the identification and selection of the use case and ending with the deployment and maintenance of the application – follows dedicated phases that can be organized in standard process models. The process models assist in structuring the development process and defining requirements that must be met in each phase to enter the next phase. The standard processes can be classified into generic and domain-specific ones. Generic standard processes (e.g., CRISP-DM , ASUM-DM, KDD, SEMMA, or Team Data Science Process) describe a generally valid methodology and are thus independent of individual domains.[10] Domain-specific processes on the other hand consider specific peculiarities and challenges of special application areas.

The Machine Learning Pipeline in Production is a domain-specific data science methodology that is inspired by the CRISP-DM model and was specifically designed to be applied in fields of engineering and production technology.[11] To address the core challenges of ML in engineering – process, data, and model characteristics – the methodology especially focuses on use-case assessment, achieving a common data and process understanding data integration, data preprocessing of real-world production data and the deployment and certification of real-world ML applications.

Machine Learning Pipeline in Production

The foundation of most artificial intelligence and machine learning applications in industrial settings are comprehensive datasets from the respective fields. Those datasets act as the basis for training the employed models.[7] In other domains, like computer vision, speech recognition or language models, extensive reference datasets (e.g. ImageNet, Librispeech,[12] The People's Speech) and data scraped from the open internet[13] are frequently used for this purpose. Such datasets rarely exist in the industrial context because of high confidentiality requirements [9] and high specificity of the data. Industrial applications of artificial intelligence are therefore often faced with the problem of data availability.[9]

For these reasons, existing open datasets applicable to industrial applications, often originate from public institutions like governmental agencies or universities and data analysis competitions hosted by companies. In addition to this, data sharing platforms exist. However, most of these platforms have no industrial focus and offer limited filtering abilities regarding industrial data sources.

• Operational artificial intelligence
• Artificial intelligence in heavy industry