Advanced Manufacturing and Materials Engineering:
Manufacturing is the process of converting raw materials, components, or parts into finished goods that meet a customer’s expectations or specifications. Manufacturing research includes the development of new manufacturing processes that lead to better products or processes using fewer resources.
Materials engineering deals with the understanding of how materials work, creating new materials for new applications as well as developing existing materials to improve performance. The structure of a material is engineered from an atomic level, so that its properties can be tailored to suit a particular application.
Computer-aided design (CAD) is the use of computer systems to aid in the creation, modification, analysis, or optimization of a design. CAD software is used to increase the productivity of the designer, improve the quality of design, improve communications through documentation, and to create a database for manufacturing. Computer-aided manufacturing (CAM)is the use of a computer to assist in all operations of a manufacturing plant, including planning, management, transportation and storage. Its primary purpose is to create a faster production process and components and tooling with more precise dimensions and material consistency with reduced energy consumption. Computer-aided process planning (CAPP) is a linkage between the CAD and CAM modules. It provides for the planning of the process to be used in producing a designed part. Process planning is concerned with determining the sequence of individual manufacturing operations needed to produce a given part or product.
Operations management is an area of management concerned with designing and controlling the process of production and redesigning business operations in the production of goods or services. It involves the responsibility of ensuring that business operations are efficient in terms of using as few resources as needed and effective in terms of meeting customer requirements. Operations produce products, manage quality and create services. Operations management covers a large variety of manufacturing and service industries.
Operations Research and Decision Analysis:
Operations research (OR) is a group of analytical methods of problem-solving and decision-making that is useful in the management of organizations. In operations research, problems are broken down into basic components and then solved in defined steps by mathematical analysis. Analytical methods used in OR include mathematical logic, simulation, network analysis, queuing theory, game theory, etc.
Decision analysis is a management technique in which statistical tools such as decision tree analysis, multivariate analysis, and probabilistic forecasting are applied to the mathematical models of real-world problems. The objective of a decision analysis is to discover the most advantageous alternative under the circumstances.
Supply Chain Management:
Supply chain management area of research can be divided into few broad sub-research areas: supply management/purchasing/procurement, operations, demand management, and logistics, transportation & distribution management and sustainability in supply chains. Inventory rationalization is main focal point of research within operations side of supply chain. The main focus within supply management, purchasing and procurement is how to deal with upstream suppliers in order to extract highest values from them. The main focus within demand management is to understand customer demands and to forecast for predicting future demands and identifying trends. Logistics, transportation and distribution management research involve looking into optimum solutions for minimizing the total cost of logistics, transportation and distribution operations. Overall, the team of researchers in the department is interested in understanding and exploring issues of supply chain to tackle risk and build resilience.
Uncertainty and Risk Management:
Uncertainty and risk management is the identification, evaluation, and prioritization of risks followed by coordinated and economical application of resources to minimize, monitor, and control the probability or impact of unfortunate events or to maximize the realization of opportunities. The objective is to assure uncertainty does not deflect the endeavor from the business goals. Risks can come from various sources including uncertainty in financial markets, threats from project failures, legal liabilities, credit risk, accidents, natural causes and disasters, deliberate attack from an adversary, or events of uncertain or unpredictable root-cause.
Industrial Automation and Robotics:
Industrial automation is the use of control systems, such as computers or robots, and information technologies for handling different processes and machineries in an industry to replace human beings. It is the second step beyond mechanization in the scope of industrialization. The focus of automation has now shifted to increasing quality and flexibility in a manufacturing process. Robotics is a branch of engineering that involves the conception, design, manufacture, and operation of robots. This field overlaps with electronics, computer science, artificial intelligence, mechatronics, nanotechnology and bioengineering. Robotics deals with the design, construction, operation, and use of robots, as well as computer systems for their control, sensory feedback, and information processing.
Artificial Intelligence and Machine Learning:
AI (artificial intelligence) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions) and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision. Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range. Examples of machine learning algorithm include Artificial Neural Network (ANN), Support Vector Machines (SVM), Naive Bayes, K-Nearest Neighbors, K-Means Clustering, Random Forest and so on. Such algorithms can be applied to a diverse range of problems in the field of industrial and manufacturing engineering.
Ergonomics and Safety Management:
Ergonomics applies the knowledge of human capabilities and limitations to analysis, design and operation of products, services and systems. The properties of interest include anthropometric, physiological, and psychological factors that may impact human-technology interactions. The goals of ergonomics are to increase system performance, safety, and cost effectiveness and to improve the human experience with technology. Safety management is a comprehensive management system designed to manage safety elements in the workplace. It includes policy, objectives, plans, procedures, organization, responsibilities and other measures. It is used in industries that manage significant safety risks, including aviation, petroleum, chemical, electricity generation and others.
Prognostics and Health Management
Prognostics and health management (PHM) predicts the future health state of a system by analyzing the current condition of the system under its actual operating conditions using sensing technologies, physics of failure, machine learning, modern statistics, and reliability engineering, etc. PHM is regarded as an efficient and practical approach for improving the maintenance cycle, reducing the maintenance cost, and extending the overall lifetime through evidence-based scheduled maintenance strategies. There are three different approaches of prognostics available to assess the degradation or extent of deviation from the expected performance- model-based prognostics, data-driven prognostics, and hybrid approach prognostics. In model-based prognostics, a physical model of the system or degradation process of the system is available with mathematical description to estimate the output for some definite inputs. The data-driven prognostics approach deals with previously observed data by using pattern recognition and machine learning techniques to perform prognostification. The necessity of the hybrid approach prognostics arises when the degradation system becomes too complex to express it as a mathematical model, and sufficient observed data is not present and it is harder to recognize the pattern of the data. Several applications of prognostics in practical engineering systems include capacity degradation of batteries, wear in a revolute joint, fatigue crack growth in a panel, fatigue damage in gears and bearings, and more.