Machine learning (ML) is widely known as a great and powerful tool for lots of applications within intelligent manufacturing systems and smart manufacturing. Hence, its significance will enhance more and more in the nearest future.
Machine learning has not only an interdisciplinary nature that presents a massive chance of advancement but also an essential risk at the same time as cooperation between various disciplines. Examples of such fields include Industrial Engineering, Computer Science, Mathematics, and Electrical Engineering, and they are essential to driving progress.
One of the most interesting developments is in the sector of machine learning, including data mining (DM), artificial intelligence (AI), knowledge discovery (KD) from databases, etc. Nevertheless, the machine learning sector is distinct, and lots of various algorithms, theories, and approaches are accessible and useable. For lots of manufacturing practitioners, this signifies an obstacle regarding the adoption of these great tools and hence may prevent the usage of large amounts of data increasingly being accessible and useful.
Challenges of Manufacturing Domain
The manufacturing sector is no doubt a well-established sector, notwithstanding the significance of it, cannot be overemphasized and highly rated enough. Over the previous decades, various well-developed economies experienced a decrease in the manufacturing contribution towards their GDP. Although, in the past few years, multiple initiatives to revise and improve the manufacturing sector were already in gear.
The challenges manufacturing sectors are experiencing currently are different from the challenges in the past. Written below are the challenges the manufacturing industry is experiencing:
- Active and flexible enterprise abilities and supply chains.
- The increasing importance of manufacturing of high value-added products.
- Renewable manufacturing processes and products.
- Adoption of high-quality improved manufacturing technologies.
- Make use of enhanced knowledge, information management, and AI systems.
- Introduction of Innovation in processes, products, and services.
Close cooperation between sector and research, discover and adopt innovations and technologies.
How Can Machine Learning Resolve The Challenges
In other to prevail over some of the current main challenges of complicating manufacturing systems, acceptable candidates are machine learning techniques. These data-driven methods can discover highly complex and non-linear patterns in data of various types and sources and also change raw data to great functions, spaces, commonly called models, which are then later utilized for prediction, detection, classification, regression, or forecasting.
The current highest level of development of machine learning, again with attention to manufacturing applications, is being presented. Surrounding these circumstances, a structuring of various machine learning techniques and algorithms is effectively developed and presented.
Based on the statement made by Michal Bambušek, the Sales Manager of NeuronSW Ltd’s, the project paid more attention to sales and marketing plans. He also said, “We trained sales staff and identified key markets and go-to-market strategies for the neurons technology and conducted case studies to develop and adapt it to various sectors. We made new business contact that helped us in discovering some new areas and uses for our technology, which helped in enhancing and progression.”
With innovations from machine learning, it’s no doubt that both machines and people will benefit from this high technology created through the initiative. Čermák stated that “There is no doubt that asset maintenance is one of the core areas of exploration in many industries worldwide.
Most people believe strongly that in the nearest future predictive sound maintenance will become a standard function of most machines with moving parts, assisting manufacturers and operators similarly. As for future researches, scientists and technologists are doing everything they can to learn from case studies and upgrade the technology and research.