ABSTRACT:
Artificial Intelligence (AI) and Machine Learning (ML) are now among the buzzwords in manufacturing. Articles are written about various aspects of AI and ML in manufacturing. From some of the literature, one might conclude that AI and ML will solve all of our problems. These technologies are very sophisticated and are not going to easily accommodate the “noise” of the factory floor. We can see some of these techniques beginning to make inroads into manufacturing, but the road to full-scale implementation will be a rocky one.
TABLE OF CONTENTS: -
WHAT IS MANUFACTURING?
TYPES OF MANUFACTURING PROCESS
MANUFACTURING INDUSTRIES
TYPES OF MANUFACTURING INDUSTRIES
WHAT IS AI?
INDUSTRIAL REVOLUTION
ROLE OF AI IN MANUFACTURING
USES OF AI IN MANUFACTURING
ADVANTAGE OF AI IN MANUFACTURING
DISADVANTAGE OF AI IN MANUFACTURING
WHAT IS ML?
INDUSTRIAL REVOLUTION
ROLE OF ML IN MANUFACTURING
USES OF ML IN MANUFACTURING
ADVANTAGES OF ML IN MANUFACTURING
DISADVANTAGES OF ML IN MANUFACTURING
WHAT IS MANUFACTURING ?
Manufacturing is the processing of raw materials or parts into finished goods through the use of tools, human labor, machinery, and chemical processing. Large-scale manufacturing allows for the mass production of goods using assembly line processes and advanced technologies as core assets. Efficient manufacturing techniques enable manufacturers to take advantage of economies of scale, producing more units at a lower cost.
Manufacturing is a value-adding process allowing businesses to sell finished products at a higher cost over the value of the raw materials used. It is often reported on by the conference board, and well examined by economists.
TYPES OF MANUFACTURING PROCESS: -
The different types of the manufacturing process are as follows: -
Casting: - It is a manufacturing process in which a liquid material is usually poured into a mold, which contains a hollow cavity of the desired shape, and then allowed to solidify. The solidified part is also known as a casting, which is ejected or broken out of the mold to complete the process.
Casting materials are usually metals or various time-setting materials that cure after mixing two or more components. Casting is most often used for making complex shapes that would be otherwise difficult or uneconomical to make by other methods. Heavy equipment like machine tool beds, ships' propellers, etc. can be cast easily in the required size, rather than fabricating by joining several small pieces.
2. Molding: - Molding or molding is the process of manufacturing by shaping liquid or liable raw material using a rigid frame called a mold or matrix. This itself may have been made using a pattern or model of the final object.
A mold or mold is a hollowed-out block that is filled with a liquid or pliable material such as plastic, glass, metal, or ceramic raw material. The liquid hardens or sets inside the mold, adopting its shape. A mold is a counterpart to a cast. The very common bi-valve molding process uses two molds, one for each half of the object.
Articulated molds have multiple pieces that come together to form the complete mold, and then disassemble to release the finished casting; they are expensive, but necessary when the casting shape has complex overhangs.
3. Forming: - Forming, metal forming, is the metalworking process of fashioning metal parts and objects through mechanical deformation.
The workpiece is reshaped without adding or removing material, and its mass remains unchanged. Forming operates on the materials science principle of plastic deformation, where the physical shape of a material is permanently deformed.
4. Machining: - It is a process in which a material (often metal) is cut into a desired final shape and size by a controlled material removal process.
The processes that have this common theme, controlled material removal, are today collectively known as subtractive manufacturing, in distinction from processes of controlled material addition, which are known as additive manufacturing. Exactly what the "controlled" part of the definition implies can vary, but it almost always implies the use of machine tools.
5. Joining: - Joining is one of the manufacturing processes by which two or more materials can be permanently or temporarily joined or assembled with or without the application of external elements to form a single unit.
MANUFACTURING INDUSTRIES
They are those industries whose manufacture and trade are based on the fabrication, processing, or preparation of products from raw materials and commodities. This includes all foods, chemicals, textiles, machines, and equipment. This includes all refined metals and minerals derived from extracted ores. This includes all lumber, wood, and pulp products.
TYPES OF MANUFACTURING INDUSTRIES: -
Clothing and Textiles: - Companies that process raw wool, cotton, and flax to make cloth are categorized under the clothing and textiles sector. This also applies to using wool and cloth to make clothes, outerwear, upholstery fabrics, and bedding. The output of seamstresses and tailors belongs to the clothing and textile sector. Synthetics such as polyester fall under chemical manufacturing. The material, not the product, is at the center of defining this sector.
Petroleum, Chemicals, and Plastics: - The process of turning chemicals, coal, and crude oil into usable products, along with the making of soaps, resins, paints and pesticides, and medicines belong to this sector of manufacturing. But rubber manufacturing is considered a part of plastic work. This sector of the industry also includes the use of crude oil to make certain plastics, as well as gasoline and other chemicals.
Electronics, Computers, and Transportation: - Though these fields are closely related, they are usually treated as different sectors of manufacturing. Most of the products in this manufacturing sector use electric power, and all require a power source. Within this sector, you'll find all appliances and microprocessors, semiconductors, chips, and all audio-visual equipment. The transportation sector is self-defining, as it contains all automobiles, trains, and planes that do not fall under other sectors, such as metalwork or chemical manufacturing.
Food Production: - The inclusion of agriculture into manufacturing in modern society shows how agriculture has changed over the years, imitating more of a food production factory than an organic-style farm of just a century ago. As the simplest of all manufacturing industries, it includes all forms of food production – from the farm to the dinner table – including such work as canning and purifying.
Metal Manufacturing: - Along with oil and chemical manufacturing, metals belong to heavy industry, while the remaining sectors are generally considered as light industry or consumer-oriented industry. The production of metals includes all forms of iron, aluminum, and steel manufacturing, as well as forging, engraving, coating, and stamping.
Wood, Leather, and Paper: - Wood production includes all forms of manufacturing floors or housing, as well as sawing and laminating. Under leather industries, you'll find all tanning and curing, but the creation of leather clothes falls belongs to clothing and textiles. The paper production process is typified by the cleansing of raw wood pulp into paper products of various kinds.
WHAT IS ARTIFICIAL INTELLIGENCE (AI)?
Artificial Intelligence (AI) is the stimulation of human intelligence processes by machines, especially by computer systems. Specific applications of AI include Expert System, Natural Language Processing (NLP), Machine Vision, and Speech Recognition.
AI programming focuses on three cognitive skills:
Learning processes: - This aspect of AI programming focuses on acquiring data and creating rules for how to turn the data into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task.
Reasoning processes.: -This aspect of AI programming focuses on choosing the right algorithm to reach the desired outcome.
Self-correction processes: - This aspect of AI programming is designed to continually fine-tune algorithms and ensure they provide the most accurate results possible.
INDUSTRIAL REVOLUTION: -
1. Industry 4.0 and smart maintenance
In manufacturing, ongoing maintenance of production line machinery and equipment represents a major expense, having a crucial impact on the bottom line of any asset-reliant production operation.
For this reason, predictive maintenance has become a must-have solution for manufacturers who have much to gain from being able to predict the next failure of a part, machine, or system.
Predictive maintenance uses advanced AI algorithms in the form of machine learning and artificial neural networks to formulate predictions regarding asset malfunction.
This allows for drastic reductions in costly unplanned downtime, as well as for extending the Remaining Useful Life (RUL) of production machines and equipment.
2. The rise of quality 4.0
Because of today’s very short time-to-market deadlines and a rise in the complexity of products, manufacturing companies are finding it increasingly harder to maintain high levels of quality and to comply with quality regulations and standards.
On the other hand, customers have come to expect faultless products, pushing manufacturers to up their quality game while understanding the damage that high defect rates and product recalls can do to a company and its brand.
Quality 4.0 involves the use of AI algorithms to notify manufacturing teams of emerging production faults that are likely to cause product quality issues. Faults can include deviations from recipes, subtle abnormalities in machine behavior, and changes in raw materials.
3.Human-robot collaboration
The International Federation of Robotics predicts that by the end of 2018 there will be more than 1.3 million industrial robots at work in factories all over the world. In theory, as more and more jobs are taken over by robots, workers will be trained for more advanced positions in design, maintenance, and programming.
In this interim phase, the human-robot collaboration will have to be efficient and safe as more industrial robots enter the production floor alongside human workers.
Advances in AI will be central to this development, enabling robots to handle more cognitive tasks and make autonomous decisions based on real-time environmental data, further optimizing processes.
B. ROLE OF AI IN MANUFACTURING
Manufacturing automation involves a complicated and detailed-oriented approach to produce a material, hence the following are the use cases for the industry.
AI and Automation are hot topics for manufacturing companies of the future. With AI and automation-powered technologies, the manufacturer can improve efficiency, fasten processes, and even optimize operations. It can reduce the production cost by 20% of which 70 % comes from improved resource productivity.
Transportation and logistics companies are in forefront of AI and Automation adoption. Companies in emerging nations are more enthusiastic about these benefits, while industrialized nations have a different view.
AI and automation will transform the value chain from end to end. The operations of the organization will be most heavily affected by this change. These factors augment existing levers that help in improving productivity.
C. USES OF AI IN MANUFACTURING: -
1.Digital twins
It is a virtual representation of a real-world product or asset. Thanks to digital twins, manufacturers can improve their understanding of the product and allow businesses to experiment in future actions that may enhance asset performance.
2.Product development
Manufacturers can use digital twins before their physical counterpart is manufactured. This application enables businesses to collect data from the virtual twin and improve the original product based on data.
3.Design customization
Due to the shift toward personalization in consumer demand, manufacturers can leverage digital twins to design various permutations of the product. This allows customers to purchase the product based on performance metrics rather than its design.
4.Shop floor performance improvement
A digital twin can be used to monitor and analyze the production process to identify where quality issues may occur or where the performance of the product is lower than intended.
5.Logistics optimization
Digital twins allow manufacturers to gain a clear view of the materials used and provide the opportunity to automate the replenishment process.
D. ADVANTAGES OF AI IN MANUFACTURING: -
Direct Automation
24/7 Production
Safety
Lower Operational Costs
Greater Efficiency
Quality Control
Quick Decision Making
E. DISADVANTAGES OF AI IN MANUFACTURING: -
Not budget-friendly
No improvement in experience
Zero creativity
WHAT IS MACHINE LEARNING (ML)?
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly.
A. INDUSTRIAL REVOLUTION: -
1. Machine learning in Industry 4.0
A decade ago, the term Industry 4.0 was coined to refer to the process of digitalization in the industrial sector and since then we have seen an increase in the number of companies in this field that are committed to the implementation of advanced technologies such as IoT, Blockchain and all the branches of Artificial Intelligence (AI): machine learning, deep learning, cognitive intelligence, etc.
In this direction, the European Union is moving ahead with a firm step. In February 2020, the European Commission presented the “White Paper on Artificial Intelligence”. A joint strategy between all EU countries, as explained by its president, Ursula von der Leyen, aims to attract more than 20 billion euros per year over the next ten years to invest in Artificial Intelligence (AI). A figure that is expected to be reached with the contribution of the private sector and the co-financing of the states. Companies in the ceramics, automotive, energy management, and food and beverage markets are already benefiting from the advantages of implementing AI through machine learning algorithms.
B. ROLE OF ML IN MANUFACTURING: -
ML in manufacturing is used in two ways i.e., supervised and unsupervised learning.
1. SUPERVISED MACHINE LEARNING
In manufacturing use cases, supervised machine learning is the most commonly used technique since it leads to a predefined target: we have the input data; we have the output data, and we’re looking to map the function that connects the two variables.
It demands a high level of involvement – data input, data training, defining and choosing algorithms, data visualizations, and so on. The goal is to construct a mapping function with a level of accuracy that allows us to predict outputs when new input data is entered into the system.
Initially, the algorithm is fed from a training dataset, and by working through iterations, continues to improve its performance as it aims to reach the defined output. The learning process is completed when the algorithm reaches an acceptable level of accuracy.
2. UNSUPERVISED MACHINE LEARNING
Unsupervised machine learning algorithms infer patterns from a dataset without reference to known or labeled, outcomes. Unlike supervised machine learning, unsupervised machine learning methods cannot be directly applied to regression or a classification problem because you have no idea what the values for the output data might be, making it impossible for you to train the algorithm the way you normally would. Unsupervised learning can instead be used to discover the underlying structure of the data. Additionally, since you do not know what the outcomes should be, there is no way to determine how accurate they are, making supervised machine learning more applicable to real-world problems.
USES OF ML IN MANUFACTURING
1.Image Recognition
Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons, places, digital images, etc. The popular use case of image recognition and face detection is, Automatic friend tagging suggestion:
Facebook provides us a feature of auto friend tagging suggestion. Whenever we upload a photo with our Facebook friends, then we automatically get a tagging suggestion with a name, and the technology behind this is machine learning's face detection and recognition algorithm.
It is based on the Facebook project named "Deep Face," which is responsible for face recognition and person identification in the picture.
2. Speech Recognition
While using Google, we get an option of "Search by voice," which comes under speech recognition, and it's a popular application of machine learning.
"Speech recognition" is a process of converting voice instructions into text, and it is also known as "Speech to text", or "Computer speech recognition." At present, machine learning algorithms are widely used by various applications of speech recognition. Google Assistant, Siri, Cortana, and Alexa are using speech recognition technology to follow the voice instructions
3. Product recommendations
Machine learning is widely used by various e-commerce and entertainment companies such as Amazon, Netflix, etc., for product recommendations to the user. Whenever we search for some product on Amazon, then we started getting an advertisement for the same product while internet surfing on the same browser, and this is because of machine learning.
Google understands the user interest using various machine learning algorithms and suggests the product as per customer interest.
As similar, when we use Netflix, we find some recommendations for entertainment series, movies, etc., and this is also done with the help of machine learning.
4. Self-driving cars
One of the most exciting applications of machine learning is self-driving cars. Machine learning plays a significant role in self-driving cars. Tesla, the most popular car manufacturing company is working on a self-driving car. It is using an unsupervised learning method to train the car models to detect people and objects while driving.
5. Medical Diagnosis
In medical science, machine learning is used for disease diagnoses. With this, medical technology is growing very fast and able to build 3D models that can predict the exact position of lesions in the brain.
It helps in finding brain tumors and other brain-related diseases easily.
ADVANTAGES OF ML IN MANUFACTURING: -
Predictive Maintenance
Logistics and Inventory Management
Cybersecurity
Robotics
Automated Guided Vehicles (AGVs)
Generative Design
DISADVANTAGES OF ML IN MANUFACTURING: -
Data Acquisition
Time and Resources
Interpretation of Results
High error-susceptibility
After considering all the information we came to know that the use of AI and ML in the manufacturing sector has created a new era of revolution in the industrial sector and has led to drastic changes. Although there are some disadvantages of using AI and ML in the manufacturing sector count of advantages is more so they are more effective.
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