Machine Learning In Manufacturing Engineering

 

INTRODUCTION

A branch of artificial intelligence known as machine learning (ML) investigates and comprehends themes, relationships, and patterns in data to facilitate learning, processing, and decision-making without human involvement. Making computers and other devices "intelligent" enough to gain knowledge and be able to foresee the future and make judgments is the process of machine learning, to put it simply.

This process of making a machine capable of human-like learning is achieved by submitting massive quantities of data to the machine learning algorithms, which then analyze and make data-driven and data-centric suggestions based only on the supplied data. Due to several causes, the use of machine learning techniques has proliferated during the last two decades as well as the improved accessibility and capability of existing machine learning technologies. Today, machine learning is widely used in several aspects of production, such as management, optimization, monitoring, control, and diagnostics.

According to a survey, 92% of the top players of the business industry are already investing in machine learning, automation, and artificial intelligence. Process productivity and efficiency have improved by 55% in the past few years by implementing machine learning. 

How does machine learning help in the manufacturing industry? 

Given the difficulties of a rapidly changing, unpredictably industrial environment, ML, as a component of AI, can learn and adapt to changes so that the owners, managers, designers, and engineers do not have to foresee every possible situation that might arise throughout the entire production process and provide solutions. As a result, ML makes a strong argument for its use in manufacturing, given the difficulties conventional first-principle modelling tools have in dealing with adaptation. The capability of machine learning to successfully learn from and adapt to various settings is a key feature.

Examples of Machine Learning In Manufacturing

Manufacturers are continuously finding new ways to apply artificial intelligence to various facets of their production operation. Some of the common use cases for AI in manufacturing today include:

Predictive maintenance: The tech draws from machine and equipment data to identify patterns that point to possible failure. This allows the relevant operators and maintained crews to fix machines learning can short through different supply-chain-related tasks like warehouse and inventory management, inbound and outbound shipments, and customer demand for products.

This prevents manufacturers from falling behind on order fulfillment and overall productivity.  

Energy utilization and prediction: Machine learning analyzes raw data from factory machines, drawing patterns regarding prevailing energy consumption. Additionally, manufacturers use the technology to predict future consumption, allowing proper planning.

Predictive quality and yield: Manufacturers apply machine learning to detects possible causes of yield losses and quality defects. The tech identifies probable wastage causes, allowing manufacturers to adjust their operation according.

Benefits of Machine Learning In a Manufacturing Environment 

With the different use causes, manufactures stand to gain significantly from adopting machine learning in their production operations. Some of the most notable benefits of machine learning include:

Better supply chain management: With the proper deployment of machine learning, manufactures ensure that the factory always has adequate materials. This improves customer satisfaction due to timely order completion and delivery.

Improved quality control: As earlier stated, manufacturers leverage machine learning for predictive quality as well as maintained. This allows the production line equipment to remain in perfect working order, reducing the changes of defective products coming off the line.

Lower production costs: Utilizing machine learning in the earlier use causes leads to lower production costs. As a result, the business spends less on fixing broken machinery, utility bills, and space utilization.

Better employee safety: Predictive maintained ensure that employees aren't affected or put in harm's way by operating faculty machinery. Additionally, the ideal space utilization and layout ensure that workers only ever need to operate in safe zones.

Improved product design: Machine learning allows manufactures to design in-demand products by considering customers' needs. Businesses can offer market-ready products based on the confidence that their products solve a particular pain point in the market. 

How AI is Improving Manufacturing?

when it comes to harnessing AI in the industry business, the possibilities are endless. While the technology has had a significant influence, there is still a lot of unused potential that may help manufactures improve every aspect of their operations. Artificial intelligence companies enables all types of manufacturers to perform more efficiently and effectively.

AI can help manufacturers make money in a variety of ways, from anticipating and forecasting demand to developing intelligent, connected devices that produce new and inventive business models.

AI has the potential to not only increase income but also substantially reduce costs. Manufacturers can use it to anticipate maintained needs, enhance manufacturing and supply chain efficiency, and evaluate service  trends to improve product design and reduce warranty costs.

Another advantages that should not be forgotten is risk minimization, which has a huge influence. Manufacturers may use AI to discover product flaws early and enhance quality control procedures to decrease liability, control production risks, solve customer service concerns before they spread, and even reduce safety risks by forecasting the possibility of harm from certain actions.

Does this mean AI will Eliminate Engineer's Jobs?

There are many benefits of AI and ML in manufacturing engineering, but some engineers fear that their jobs will be taken over by machines. Automation indeed has and will keep on taking human jobs positions but has also created new job vacancies only accomplished by humans. According to a study, 'One Hundred Year Study of Artificial Intelligence', conducted by Stanford University. "There's nothing imminent about the threat to jobs, and even when or if we get there, It will be balanced out by the positive impacts on society and the increased capabilities technology offers." It also states that science and engineering professions are such fields that are the least threatened and will concurrently witness huge benefits from AI tools.

Blog By:

Vishwakarma Institute of Technology, Pune.

Guided By

Prof. Pramod Kale

Class TY D

Batch B3

Group Members:

• Aman Sheikh 
• Bhushan Shelavale
• Pranav Shinde
• Omkar Sonone
• Abhishek Suryawanshi