Machine Learning and Artificial Intelligence Applications 302
Machine Learning and Artificial Intelligence Applications 302 discusses strategies for applying machine learning (ML) and artificial intelligence (AI) capabilities to various manufacturing processes. ML algorithms can help improve manufacturing processes throughout a product’s lifecycle, including monitoring raw material use, optimizing production processes and supply chain logistics, and improving the product delivery and service for end users. Manufacturers can use machine learning libraries to gain valuable insights from data. Developing ML models requires the use of a programming language, such as Python, and an understanding of data analysis. Leveraging ML and AI can also vastly improve the quality of manufactured products.
After taking this course, users will understand how computing devices can utilize machine learning models and how machine learning capabilities can be integrated into AI systems to automate a variety of manufacturing tasks and operations.
Number of Lessons 12
- Machine Learning and Artificial Intelligence for Manufacturing
- Machine Learning Methods
- Building Machine Learning Models
- Optimizing Raw Material Quality
- Enabling Generative Design
- ML/AI Review
- Enhancing Automated Production Processes
- Optimizing Predictive Maintenance
- Automating Quality Control
- Optimizing Supply Chain and Production Logistics
- Improving Safety
- ML/AI Final Review
- Describe machine learning and artificial intelligence.
- Distinguish between supervised, unsupervised, and reinforcement machine learning.
- Describe how manufacturers can implement machine learning models.
- Describe how machine learning can improve raw material quality.
- Describe how machine learning and AI enhance product design.
- Describe how machine learning and AI enhance process automation.
- Describe how machine learning and AI can improve predictive maintenance.
- Describe how machine learning and AI can improve quality control processes.
- Explain how machine learning and AI can improve supply chain and logistics management.
- Describe various ways machine learning and AI can improve safety for manufacturers.
AM. The process of joining or solidifying materials to make an object based on a three-dimensional computer model. Additive manufacturing methods typically build up layers of material to create an object.
The industry that covers machines or vehicles of flight. Aerospace manufacturers generally require highly specialized parts made with very high precision.
The entity in a reinforcement learning environment that performs actions. The agent in reinforcement machine learning is the artificial intelligence seeking to learn the correct actions to achieve a goal.
Artificial intelligence. A computer program with algorithms that function as behavioral rules, allowing a machine or computer to imitate intelligent human behavior. AI is able to discern and learn from its experiences in order to make optimized decisions during the operation or for subsequent processes.
A mathematical process designed to systematically solve a problem. Complex digital algorithms allow machine learning to predict and regulate operations.
An irregularity in a data set that does not fit with general trends or patterns in the data. Anomalies in a data set could indicate erroneous data or outliers that can be studied to learn new information.
A machine learning algorithm designed to expose irregular patterns in a data set. Anomaly detection algorithms are used to analyze unlabeled data sets.
AI. A computer program with algorithms that function as behavioral rules, allowing a machine or computer to imitate intelligent human behavior. Artificial intelligence is able to discern and learn from its experiences in order to make optimized decisions during the operation or for subsequent processes.
AR. A technology that superimposes a computer-generated image on a view of the real world. Augmented reality methods maybe be used with smart safety goggles to visually alert the wearer to potential hazards.
Self-governing. Autonomous systems can be configured to make decisions independent of human interaction.
A point of congestion during the production process. Bottlenecks limit the flow of production and can be easily identified with machine learning models.
The practice of using software and storing data on remote servers that can be accessed through the internet. Cloud computing is common among manufacturers and consumers.
The process of analyzing a data set to discover similar patterns, then dividing the data into groups based on these patterns. Clustering algorithms for unsupervised machine learning allow an artificial intelligence (AI) to analyze large, unlabeled data sets.
The processes of analyzing a data set to discover similar patterns, then dividing the data into groups based on these patterns. Clustering algorithms for unsupervised machine learning allow an artificial intelligence (AI) to analyze large, unlabeled data sets.
A programmable, automated device designed to assist humans with various tasks on the production floor. Cobots allow personnel to perform repetitive and often dangerous tasks more safely and efficiently.
Digital instructions written in a computer programming language that a computing device can translate into readable data. Computer coding uses different combinations of letters, numbers, and symbols, which a computer interprets and presents as a program, website, or other application.
A person who codes digital commands into a computing device. Computer programmers use various computer programming languages that tell a device what actions to perform.
computer programming language
A set of symbols and rules used to present information to a processor. Computer programming languages are used to develop machine learning models and determine actions for artificial intelligence (AI).
A deep learning capability that enables an artificial intelligence (AI) to detect and identify objects by recognizing visual patterns through a camera or on a screen. Computer vision can be useful for supervised or unsupervised machine learning but requires very large amounts of visual data.
CAD. A computer system used to design a model of a product. Computer-aided design is most often used to create part models for production.
A cutting fluid used for lubrication and for decreasing the temperature of the tool and workpiece to prolong tool life. Coolant levels can be monitored digitally in a cyber-physical system.
A process by which a material gradually degrades or wears away. Corrosion typically occurs when a material is exposed to atmosphere, moisture, or other substances.
A material's ability to resist deterioration and chemical breakdown due to surface exposure in a particular environment. Corrosion resistant materials can extend the lifecycle of components that must be used in harsh operating environments.
The amount of a product or service that a customer requires at a specified time. Customer demand patterns typically guide production goals for manufacturers.
CPS. A hardware device that links physical objects and processes with virtual objects and processes in an interconnected network. A cyber-physical system increases the capabilities of a device.
A field of study that uses statistics and data analysis with computing technology. Data science allows manufacturers to gain valuable insight from historical data to improve operations.
An individual who uses computing systems to perform data analysis in order to interpret and present insights gained from data. Data scientists can help manufacturers operate more efficiently by adjusting processes based on data analysis.
An advanced form of machine learning that uses neural networks with multiple hidden layers. Deep learning algorithms can enable machines to exhibit advanced, human-like behaviors but require significant amounts of data.
A virtual representation of a physical asset or part. A digital twin evolves with the part throughout its product lifecycle.
A period of time when production stops, often due to mechanical failure or maintenance needs. Downtime can be minimized by using digital twins to predict maintenance requirements.
Able to be drawn, stretched, or formed without breaking. Ductile materials generally have low strength.
The final customer for whom a product or service is intended. End users can benefit from machine learning models aimed at improving service and predictive maintenance.
generative design software
An advanced type of computer-aided design (CAD) software that uses cloud computing and artificial intelligence to design products. Generative design software can allow engineers to innovate designs and produce alternative designs much faster than traditional CAD software.
A controlled heating and cooling process used to change the structure of a material and alter its physical and mechanical properties. Heat treatment is often used to adjust a material's hardness.
A process that uses both traditional and additive manufacturing (AM) to create a finished part. Hybrid manufacturing can involve either using a traditional manufacturing process on a mostly additively manufactured part or vice versa.
Using computing technology to analyze data from digital images in order to alter the image or perform other processes based on the digital data. Image processing capabilities help enable computer vision and object recognition software.
Manufacturing that uses connected devices and digital technologies to combine the physical world with the digital. Industry 4.0 is also known as the 4th Industrial Revolution.
Information given to a computing device in order to be processed or analyzed. Input data is used by machine learning algorithms in order to produce an output.
A set of digital information that is defined prior to being analyzed by a computer. Labeled data is required in supervised machine learning algorithms.
Using a concentrated beam of light to generate data about the geometric shape of an object. Laser scanning is used to create a 3D model of a physical object.
The amount of time it takes from the beginning of a project to the completion of a finished product. Lead time can also be the length of time from the customer's order to shipment.
An approach to manufacturing that seeks to reduce the cycle time of processes, increase flexibility, and improve quality. Lean manufacturing systems help to eliminate waste in all its forms.
The entire timeline of an asset, from its design, production, and use to its disposal and replacement. Each stage of a product's lifecycle can be improved by machine learning and artificial intelligence.
Fully automated manufacturing that can operate independently in a facility without any employees on site. Lights-out manufacturing allows facilities to operate for extended hours while reducing labor costs.
The area of business that focuses on the purchase, production, transport, and distribution of materials within a company. Logistics planning and management efficiency can be improved with machine learning algorithms.
A substance used to prevent friction between two surfaces in relative motion. Lubricants such as oil and grease reduce resistance, wear, and heat.
ML. The process that enables a digital system to analyze data in order to build predictive models. Machine learning systematically solves problems using highly complex algorithms.
machine learning library
A digital processing application database designed for machine learning applications. Machine learning libraries can be a feasible starting point for manufacturers who want to leverage machine learning technology.
machine learning model
A computing function that uses one or more algorithms to perform specific calculations on data to produce an output. Machine learning models may use supervised, unsupervised, or reinforcement learning methods.
A manufacturing process that involves removing material to form an object. Machining can occur using traditional methods, like turning, drilling, milling, and grinding, or with less traditional methods that use electricity, heat, or chemical reaction.
The shape and alignment of microscopic components in a material. Microstructure analysis helps determine the properties of a material.
Machine learning. The process that enables a digital system to analyze data in order to build predictive models. ML systematically solves problems using highly complex algorithms.
The ability of an artificial intelligence system to identify specific objects using machine learning algorithms. Object recognition is considered a computer vision capability.
An input value in a data set that falls outside of the normal value range that the rest of the data falls within. Outliers in data sets can indicate inaccurate data or an anomaly that requires further analysis.
A calculated result or action produced by a computing device after processing data inputs. Outputs produced by computing devices may be in the form of data or may include a response to data.
A failing, deficiency, or imperfection in the specified and expected quality of a manufactured part. Part defects can be more easily detected using machine learning models.
A maintenance approach that involves collecting data related to machine operation in order to service a machine before maintenance issues arise. Predictive maintenance is performed on a scheduled basis and helps prevent unscheduled downtime.
A prediction of how many product units will be produced within a specified amount of time. Production forecasting practices that incorporate machine learning can provide more accurate estimates of expected production under different conditions.
A specific sequence of computer code that directs a computing system to carry out an action. Programming commands in machine learning libraries apply pre-coded algorithms to data.
A device that detects a change in a physical condition and turns it into an electrical signal. Proximity sensors can sense the presence, or absence, of an object without making physical contact.
A type of computer programming language used for a range of general applications. Python is the most common language used for machine learning applications.
A system of managing quality by inspecting finished products to make sure they meet specifications. Quality control relies on error detection and correction.
A type of computer programming language developed by for statistical applications. R is commonly used for machine learning applications.
An unprocessed substance used to make the finished part. Raw materials in manufacturing include metal, polymer, and ceramic.
A way of describing a nearly instantaneous interval of time. Real-time analysis allows AI systems to adjust processes to improve efficiency automatically.
A set of steps and calculations used to analyze or predict how input or independent variables impact an output or dependent variable. Regression algorithms can be used in supervised machine learning models.
reinforcement machine learning
A process in which an artificial intelligence (AI) learns the best actions to achieve a goal through trial and error. Reinforcement machine learning enables an AI to learn details about inputs and produce correct outputs autonomously.
A field of technology that is focused on programmable mechanical devices. Robotics enable the work of a person to be done by a robot with a higher degree of accuracy.
root cause analysis
A study undertaken to find the first or underlying cause of a problem. Root cause analysis involves the collection and study of data to determine the true cause of a problem.
The practices and policies that a company puts in place in order to preserve the health and well-being of employees, equipment, and facilities. Safety standards can be improved by using machine learning models to analyze risks.
Unusable material removed from a workpiece to create a finished part. Scrap is a waste product of manufacturing that is significantly reduced through using machine learning models.
The pressing and heating of raw materials to create a solid shape. Sintering creates materials with very uniform contents.
A management philosophy and process improvement method that uses data to identify problems and point to improvements. Six Sigma's goal is to reduce the number of defects to less than 3.4 defects per million opportunities, which is near perfection.
Digital cameras that use computing software with computer vision capabilities. Smart cameras used in manufacturing are typically IIoT devices that operate wirelessly.
Technologically integrated manufacturing that creates and uses data in real time to address the needs of the factory, supplier, and customer. Smart manufacturing is an advancement of traditional manufacturing automation.
smart personal protective equipment
Smart PPE. Any clothing or device that uses computing technology to alert the wearer to potential hazards. Smart personal protective equipment may use visual, audible, or other sensory alerts to warn the wearer of potential dangers.
A device equipped with software that can detect physical changes in the environment and process them as digital signals. Smart sensors are more advanced than normal digital sensors since they use their own computing software to process data rather than sending the data to an external system to be processed.
A computing technology that can send and receive data without human intervention. Smart technology generally requires internet connectivity to enable data processing.
A material's ability to resist forces that attempt to break or deform it. Strength is an important mechanical property for many manufactured parts.
The relationship between a material's strength and its weight. Materials that are light but also very strong have a high strength-to-weight ratio.
supervised machine learning
A process in which a human operator labels data inputs for an artificial intelligence (AI) and defines the desired outputs the AI should produce. Supervised machine learning typically requires extensive human labor in order to prepare and label data sets.
A complex network of companies and suppliers that produce and distribute a product. A supply chain consists of a company, its suppliers, its distributors, and its customers.
A group of data for which the output variable is not known. A test data set is used to test the accuracy of a regression algorithm.
A physical property that indicates how well heat energy transfers through a material. Materials with low thermal conductivity make good heat insulators.
Previously recorded data used to train and improve machine learning algorithms. The training data for supervised learning includes historical data for both inputs and outputs.
A set of digital information that is not defined prior to being analyzed by a computer. Unlabeled data can be analyzed using unsupervised machine learning algorithms.
unsupervised machine learning
A process in which an artificial intelligence (AI) analyzes unlabeled data and produces outputs without human interaction. Unsupervised machine learning models help discover patterns in data that humans may not recognize.
A smart device that monitors vibration levels of machine components. Vibration sensors can alert AI systems to hazardous machine conditions.
A computer software program that models or demonstrates a process or operation. Virtual simulation of manufacturing processes can test the processes under varying conditions to improve efficiency.
wireless local area network
WLAN. A network that enables wireless communication between devices at a single geographical location. Wireless local area networks are typically established by connecting a wireless access point to a wired internet device.