Machine Learning in Manufacturing: What is it exactly?
Machine learning includes training algorithms using enormous datasets so that they can progress on their own, much like humans can. Artificial intelligence (AI) and Machine learning, which are frequently used interchangeably, serve different purposes. While AI broadly refers to the idea of computer-based human thinking emulation, machine learning is a subset of AI that enables computers to learn without explicit programming. The machine learning in manufacturing market, valued at $921.3 million in 2022, is projected to reach $8,776.7 million by 2030, with a strong estimated compound annual growth rate (CAGR) of 33.35%, as per Kings Research’s forecast.
Manufacturers mainly utilise two machine learning models: supervised and unsupervised learning. To anticipate machine lifespan and equipment breakdown, supervised learning finds patterns in huge datasets with predetermined outcomes. Unsupervised learning identifies patterns in datasets with unknown results, assisting in the discovery of abnormalities and flawed manufacturing process components.
5 Astonishing Ways Machine Learning is Transforming Manufacturing!
Let’s take a closer look at some of the key applications of machine learning in manufacturing in order to understand the intricate connection between ML and manufacturing.
1. Supply Chain Optimisation
The cognitive supply chain in manufacturing, a vital part of the sector, is being revolutionised by machine learning. This change will improve a number of crucial areas:
- Demand forecasting: This method examines customer preferences and behaviours using time series analysis, feature engineering, and natural language processing (NLP).
- Warehouse Control: This provides prompt product restocking and effective stock control by utilising deep learning-based computer vision technologies.
- Logistics and transportation optimisation: Machine learning algorithms assess and choose the most effective shipping and transportation routes.
2. Inspection and Monitoring
The use of computer vision for part inspection and process monitoring is a highly effective application of machine learning in manufacturing. High-throughput part inspections can be carried out effectively using cost-effective sensors and ML-based algorithms. Additionally, computer vision techniques enable high-quality continuous process monitoring.
A recent study presents a data-driven approach for independently analysing problems with integrated circuit (IC) wire bonding. The method’s efficacy is demonstrated using X-ray images from a semiconductor factory.
3. Predictive Maintenance
A rise in market value of 38% is anticipated for predictive maintenance between 2020 and 2025, according to a PwC analysis, making it one of the manufacturing machine learning assets with the quickest growth.
Contrary to fixed schedule or SCADA-based systems, predictive maintenance uses algorithms to foresee components, equipment, or system failures. It’s crucial to remember that the calibre of the training data is what determines the success of predictive quality analytics. Manufacturers must therefore have a well-thought-out data-collecting strategy in place to gather all essential process data.
4. Quality Control
Process-based losses are getting harder to sustain. The ability to automatically identify the fundamental causes of production losses brought on by processes is provided by machine learning. This is achieved by continuous multivariate analysis using specialised ML algorithms and Root Cause Analysis (RCA) enabled by machine learning.
Following this, automatic recommendations and warnings are created to notify production teams and process engineers of an approaching issue, easing the flow of important information on preventing losses before they emerge.
5. Utilisation of Digital Twins
Digital twins powered by ML can be used by manufacturers for quick diagnosis, process review, and performance forecasting. By enabling total customisation in design, production, and operation, these digital twins also have the potential to revolutionise engineering practices. Essentially, manufacturing companies can develop virtual representations of their products and procedures, allowing for testing and improvement before physical production.
The Proof is in the Product: What Are the Key Applications of Machine Learning in Manufacturing?
Can machines really learn to make manufacturing smarter? Examples say yes! Manufacturers worldwide are harnessing the power of machine learning aiming to enhance their standing in the sector. Let’s understand this through the work of industry leaders.
Siemens
The Anomaly Assistant app from Siemens makes use of machine learning to train AI to recognise significant anomalies in corporate operations. It identifies problems influencing a facility’s economic efficiency using process data.
Plant operators can fine-tune AI’s focus and perform in-depth analysis using the app’s dashboard. Through an iterative process, abnormalities are efficiently identified and assessed by the AI using process data.
The software can be installed on existing infrastructure or in the cloud, and it can be accessed via Amazon Web Services (AWS), a SIMATIC Box PC, or virtual machines (ESX, Hyper V) locally. Siemens and its clients may more easily collaborate during the review phase for seamless communication thanks to the cloud-based approach, which is very helpful.
General Motors
The transition to electric vehicles and ridesharing has placed the automobile sector in the face of unprecedented challenges. General Motors uses machine learning in manufacturing to address these issues. It employed generative design in a project with Autodesk to create a seat bracket, which resulted in a 40% lighter, 20% stronger, and 3D-printed component by consolidating eight parts into one. This demonstrated the effectiveness of generative design in product development.
Rolls-Royce
The 2020-launched aircraft design collaboration between Altair and Rolls-Royce Germany now incorporates cutting-edge machine learning into manufacturing processes. This inclusion accelerates product development, minimises sensor needs, and simplifies testing, resulting in a quicker market delivery.
The IntelligentEngine vision from Rolls-Royce advances machine learning integration by allowing communication between jet engines, the support system, and client airlines. Additionally, this technique enables engines to improve performance optimisation by learning from their mistakes.
Schneider Electric
To remotely monitor oil pump settings in its plants, Schneider Electric, a leading power and industrial automation expert, uses a predictive IoT analytics solution with Microsoft Azure Machine Learning and Azure IoT Edge. This technology detects irregularities in temperature and pressure, averting problems and breakdowns. It is used by oil & gas companies for efficient maintenance, increased productivity, and safety, all of which benefit the environment and the workers.
Vistra
With 39 gigawatts of capacity, Vistra is the largest competitive power producer in the US. The company has embraced digital analytics, including machine learning in manufacturing, to improve its operations. It simplified complicated machine speeds, temperature, pressure, and oxygen level monitoring chores. With a heat-rate optimizer powered by artificial intelligence from Vistra, efficiency is increased by 1%, saving the company a lot of money and decreasing greenhouse gas emissions.
Looking Ahead
A new era of effectiveness, accuracy, and innovation has begun as a result of machine learning integration in the manufacturing sector. The uses of machine learning in manufacturing are numerous and significant, spanning from demand forecasting and process optimisation to predictive maintenance and quality control. The transformational potential of this technology is powerfully illustrated by real-world examples like General Motors’ generative design project and Siemens’ Anomaly Assistant. As manufacturers continue to leverage machine learning, we can anticipate even more breakthroughs, solidifying its pivotal role in shaping the future of manufacturing.
Author Details –
Name: Alisha Patil
Bio: A budding writer and a bibliophile by nature, Alisha has been honing her skills in market research and the B2B domain for a while now. She writes on topics that deal with innovation, technology, or even the latest insights into the market. She is passionate about what she pens down and strives for perfection. An MBA holder in marketing, she has the tenacity to deal with any given topic with much enthusiasm and zeal. When switching off from her work mode, she loves to read or sketch.
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