If you have a smartphone or you have used Google to search for your favourite dress, if you watch movies online or you have used maps to navigate, you have been using Machine Learning to suit your needs. If you have not been introduced to Machine Learning, the words Machine Learning or Artificial Intelligence often bring in a picture of a Robot, mostly a humanoid version of a Robot that probably can listen to our commands and do what we like. But Whether you have been introduced to the world of Machine Learning or not, Machine Learning has already entered your World.
Machine Learning has been used in different ways in different industries and the Future of the Manufacturing Industry looks more optimized, efficient, and manageable with the influence of Machine Learning. There is a revolutionary change already in progress and it promises a better future.
GE alone has saved more than $1.5BN for their customers by implementing Machine Learning in their manufacturing industries.
As per a survey from McKinsey, by 2025 smart factories will generate $37 trillion.
In the last century, we have been celebrating the invention of computers which can produce an output when we give input and logic, they could do it when a condition occurs, for several times or as long as we ask them to do. They were tireless, almost flawless, and never demanded anything more than probable electricity to run.
Welcome to the new generation where machines can decide What to do, how to do and when to do it if you just tell them what the output is you need for every input. They decide the logic by “Learning “the input and output so that they can predict an output when you give a new input.
Is it sounding complex? Let’s say you take a bus to your school every day. You know that if you catch the bus at 8.30 am, you will reach the school by 8.45 and if you catch the 8.40 bus, you will reach the school at 9 am. You did not arrive at this by calculating the speed of driving, distance, or traffic. It is a culmination of a lot of factors and though there can be situations when the prediction can go wrong, it is almost always right. These criteria cannot be defined by specific If – Then- Else conditions, so the historical data is used to train the machines to help build a “Model” which is then used for predicting the output for the new inputs.
With the trained and tested models, the world has achieved great accuracy that helps every industry in very different ways.
Manufacturing is one industry where Machines are extensively used, which means it is easy to deploy IoT sensors that can transmit the data required to train the machines and eventually use the same in different ways.
Machine Learning trends indicate that Manufacturing Industries with equipment that run on Machine Learning are projected to be 10% cheaper in annual maintenance costs while reducing downtime by 20% and reducing inspection costs by 25%.
Let us now jump to see 10 ways Machine Learning in Revolutionizing Manufacturing Industry
Though the term Machine Learning rarely refers to Robots, Machine Learning for Machines is not such a bad idea after all. When Robots can learn to Pick Objects better, see objects better or Perform their operations such as Cutting, Molding, injecting better, a new world of Machine learned Machines opens. When Deep Reinforcement learning is used, it helps Robots train themselves to enhance their performance. The next stage of advancement would be to enable Robots to learn together, FANUC and NVIDIA are researching and working on enabling Multiple Robots to Learn Together.
One of the biggest challenges in large-scale manufacturing industries is the ability to monitor their machines in real-time and then there are industries such as Electrical Power Grid where real-time monitoring is a nightmare. An innovative way to monitor such large-scale manufacturing plants would be to create a Virtual Twin for each of the equipment and then simulate each of the machines with a virtual machine with the data from the sensors. The sensors provide smart monitoring helping in understanding the health of the equipment, when this data is combined with real-time data such as weather, ambient temperature, etc., the system can provide smart monitoring for otherwise impossible large-scale systems. GE Digital provides Digital twins for Assets, Networks, and Processes and has had great success building digital Twin for Power Grids.
It is every manufacturer’s dream to identify the optimal process referred to as the Golden Batch, which defines the best process to create the highest quality output with the best efficiency. Though there are quite a few challenges in developing models that can identify the optimal process because of differences in the manufacturing processes wrt equipment, ambiance, scale, and poor data quality, there is a bright scope for machines to identify the shortcomings and point to the optimal process using Machine Learning. CSense from GE has provided significant improvements in the Power industry by helping them predict “Day ahead capacity” prediDay-aheadtter.
A Normal factory uses software to manage the assets while Smart factories do more than just monitoring, their performance is optimized, and the entire factory process becomes customizable. Almost all the smart factories explore Machine Learning by beginning with Machine Learning for Asset Management. IoT devices set at various assets help the maintenance cycle to be planned better and result in reduced shut downtimes. When the IoT data is combined with ambient data, the Machine Learning model becomes a vital tool to manage smart factories., e.g.: How much a fuel valve should be open for optimal usage. Beyond this, customizing design and process would become easier. A new design can be implemented easily as the Machine Learning model can create the process about each asset and can implement the same. Industries such as steel manufacturing have been using Neural Networks for monitoring data from IoT sensors in their factories to improve their performance and ensure optimal usage.
From Electrical Energy to Water usage, Machine Learning can rule the energy consumption in Manufacturing Industries. With the data from IoT sensors, Machine Learning models can correlate and find the factors contributing to high energy consumption and suggest ways to reduce the same. The Machine Learning Models can help to intelligently cool the devices to reduce the carbon footprint when used for Network devices, Suggest the air to water ratio used for cleaning bottles in the beverage industry and even suggest the optimal number of stirring required while manufacturing Liquids such as Processed Fruit Juices. Ensuring Energy conservation through Machine Learning would help us build a sustainable future not just for the Manufacturing industry, but for human civilization.
Quality control for products in almost all the manufacturing industries requires lab testing and another process that consumes time. To ensure reduced delay in the process, the samples are only tested at regular intervals rather than tests being done for every sample. Besides, the cost of testing every sample may make this quality control for individual samples almost an impossible task. By using Machine Learning Models, we can predict the quality of a sample without actually testing it. One of the Paper Manufacturing Industry, Skjern Paper utilized Machine Learning models to predict the quality of papers produced through the Mullen Burst Test which is an industry-standard to measure the paper’s physical strength and fibre bond. The model provides instant feedback on the differences in the chemical level, which was otherwise a time-consuming lab process. With this instant feedback, chemical levels were adjusted to produce high-quality papers consistently
Can you detect downtime even before they occur? Humans wish but Machines do. Through intelligent IoT devices that track the status and health of devices, anomalies can be detected and can be used to predict downtimes. combined with documentation, notification and fast action, anomaly detection can ensure on-time repair, reduce outages and provide a probability of downtime occurrence even before they occur. Coca-cola Austria utilized the service of Mind sphere from Siemens to prevent 4 major shutdowns which considerably reduced the downtime all in a span of just 6 months.
In Metallurgical Industries such as Gold, Silver, Iron, the ability to decide the quality of output even before it is produced would be as good as finding a lot of Gold. If the Machine Learning Models can suggest the optimum value of input factors, it would be a booty of Golden treasure. By gathering the values of the input factors such as Temperature, Oxygen level, Chemical Components used for extraction and the quality of the metal produced, it is possible to find the optimum values of the contributing factors to use so that the quality of the output can be enhanced. In an ore extraction company, With the data from the various IoT devices, the Machine Learning Model recommended increasing the level of oxygen used during the extraction which enhanced the grade of the ore and increased the yield by 3.7 %
With the data from Real-Time Usage and Performance, the prototype quality can be largely enhanced. This is especially used in the automobile industry where the wear and tear, usage, and other data from existing automobiles help by large in building the new prototype models. Companies such as BMW used this in sample prototype testing for almost 7 years now. This also reduces the vulnerability of the products, reduces the recalls for new designs thereby improving the brand trust and value.
An optimized schedule to clean the equipment ensures lower downtimes and maximum utilization. Manufacturing industries increasingly use Machine Learning Models to decide the best time to clean the equipment with minimal impact on production. Caterpillar marine provided a sensor-based analysis for the Ships running with and without clean hulls. Based on the data and the correlation analysis, they recommended Hull cleaning once in 6 months rather than every 2 years. This helped in more efficient utilization of the ships with reduced repairs resulting in Greater Client Satisfaction.
Though not directly seen, the sale and inventory data drive any manufacturing industry. Any manufacturing industry needs to predict the sales for optimized manufacturing and Machine Learning models have been trained for years to help predict the sale. The majority of the Automobile Industry uses Machine Learning Models to predict sales and decide inventory based on the same. As per a Machine Learning trends report from McKinsey forecasting errors in the supply chain can be reduced by 50% and lost sales can be reduced up to 65% by implementing Machine Learning across industries in the next 5 years
While you are reading this blog on your smart device, Machine Learning models are already making a note to recommend more of such articles to you, on a serious note the manufacturing Industry is moving towards a Smart Factory where the machines would provide recommendations for optimal usage of devices, Assist in Preventive Maintenance and ensure minimal or no disruptions. Beyond all, they would help us conserve more energy, ease up providing custom designs and make changes much easier.
Machine Learning trends from PWC predict that in the next 5 years manufacturing industries would increase their predictive maintenance by 38%, visualization and automation by 34 % and Big Data will contribute to their growth by 31%.
The day when machines would predict our desired model of car, place an order to the automobile company, where it would be designed and manufactured with minimal disruptions and be delivered at our doorstep even before we start searching is not far away!