EXPERIMENT OVERVIEW
EMDIP, manufacturing SME, is a service provider for the high-precision metal cutting sector, currently in need of a very efficient anomaly detection process. The company’s main challenge is to detect anomalies as soon as they appear, since an incorrect cut cannot (usually) be repaired and it produces very costly waste. Traditional visual (camerabased) inspection anomaly detection methods are not efficient enough since they are usually searching for anomalies in the cut, which is already too late because 1) there is already quality damage provided to the metal sheet and 2) it is very challenging due to fume produced in the cutting process. Therefore, a solution for this challenge would be to detect the problems before they escalate in the cutting process. As a part of the Industry4.0 journey, EMDIP already implemented an advanced anomaly detection system (D2Port) which uses deep learning to detect anomalies as soon as they appear and enables an efficient alarming system, offered by Nissatech, the experiment’s IT provider.
Although very accurate in detecting problems, D2Port does not provide enough details to solve identified challenges, a common problem for most anomaly detection systems. Indeed, to resolve an anomaly efficiently, it is important to know the context in which the problem emerges, as well as the corresponding experience/best practices, which are usually provided in various semistructured and unstructured data sources related to the maintenance process, like the reports from the maintenance activities or the maintenance manuals (to name but a few). Moreover, the exploration process should be organized as a structured conversation, enabling deep dive into the problem resolution process.
ConAI4Main will support:
- proper contextualization of the alarms, enabling a conversation for better understanding the alarm situation (incl. root causes), based on available information;
- supporting a dialogue for questioning the situations to discover what are possible impacts of the alarm, which is done through assisted querying in LLM, based on the data collected in the anomaly detection system;
- assisting in the reparation/resolution process itself, by providing hand-on instructions, as well as in documenting the process (feedback).
Big challenge which manufacturing is facing nowadays is silo-based understanding of the problems which happen in the production
Context of a problem is constrained on the selected set of information, although there might be other relevant sources available in the company
e.g. an anomaly detection system takes as inputs camara-based information, raw material, process parameters for detecting an anomaly, but for understanding the anomaly (its impact, urgency, costs and finding an optimal solution – how to recover from the anomaly) it could be important to get information/knowledge/experience about the worker, or client …
Objective 1:
New concept of the Conversation AI-based Digital Assistant for predictive maintenance
- Provide conceptualization of the system based on the requirements from real industry settings
Objective 2:
Implementation of the conAI4Main Digital Assistant for predictive maintenance
- Development of required modules and integration in a reliable and scalable solution (software as a service)
Objective 3:
Validation of the conAI4Main Digital Assistant in real industrial settings
- Provide PoC for predictive maintenance in metal cutting industry
The approach will be tested in a real manufacturing setting in the domain of metal-cutting, in the EMDIP factory (Nis, Serbia), which is well-equipped for the required experimentation.
Target users: maintenance engineers, quality control managers
EXPECTED IMPACT
EXPECTED IMPACT
Manufacturing SME will benefit from extending the existing anomaly detection system with a Digital Assistant which will improve the resolution of unusual alarm situations, esp. for novice operators. We expect that the improvement will be for at least 75% in the first year (measured by decreasing time and the success of the alarm resolution). In addition, the scrap rate should be improved for at least 25%. IT provider is recognized provider of various services for manufacturing companies. This proposal will support the services related to predictive maintenance, which will be extended with an innovative Digital Assistant support based on Conversation AI. This extension provides a significant competitive advantage and even enable an international success (see Exploitation plan). DIH partner will provide by extending own service offering in the new domain (DIA).
Moreover, general approach in embedding Conversation AI in the exploration process (Digital Assistant, DA) can be applied beyond the predictive maintenance process. For example, a similar Digital Assistant can be used in the Quality Monitoring and Control process, where the quality of the process and product is observed and the DA will support the process of resolving quality issues, detected in the process/product.
In general, there is a huge impact of our position in Industry 5.0. by enhancing Collaborative Intelligence
Technology impact
More general, proposed solution can be interpreted as an AI-based process behaviour-related contextualization of data analytics methods, which provides a higher level interpretation of the data analytics results and offers corresponding improvements. It can be an important driver for new generation of Conversation-AI-based process monitoring, analysis and control tools, which are focused on the dialog-driven analysis of process value streams and support lean-driven process improvement (and digitization)
In general, proposed approach can impact the digitisation process through reducing time and enabling better decision making by applying AI in the case of dynamic maintenance situations, like tool wear cases. Indeed, one of the main problems in digitisation is poor data/process quality/analysis and proposed approach resolves that bottleneck.
Economic impact
The market opportunity for this kind of systems are huge. Indeed, from the exploitation point of view the main outcome can be treated as an AI and DA-based Predictive Maintenance solution. Global Predictive Maintenance Market size was valued at USD 5.77 Billion in 2022 and is poised to grow from USD 7.57 Billion in 2023 to USD 66.46 Billion by 2031, at a CAGR of 31.2% over the forecast period (2024–2031).
Based on the current PoCs we will start with Equipment/Process monitoring and control, widely recognized as important techniques for improving quality and (proactively) detecting abnormal behaviour, leading to more efficient approaches for process improvement (e.g. detecting some problems ahead of time). As being early on this market, we expect to have a share of 10% of manufacturing SMEs, focusing on central Europe domain in first year
Other impact
Proposed solution offers a new better way for resolving possible issues (anomalies), making the workspace less stressful and more engaging for workers. This will lead to a more employee satisfaction, having a huge societal impact. Second, this solution will offer new services which will be managed by new types of workers, creating new job opportunities.
By reducing anomalies, we pave the way for sustainable manufacturing that minimizes negative environmental impacts and consumption of energy, natural resources, while being socially responsible and economically viable.