MS MFGE Curriculum

Program Background

The Master of Science in Manufacturing Engineering degree is designed for the professional development of engineers, particularly those whose wish to carve a career in research and development in the field of manufacturing engineering. The program requires mastering of a field of specialization through course work and master’s thesis.
The MS MFGE program aims to produce engineers with technological knowledge in state-of-the art integrated manufacturing systems. This will enhance the ability of graduates to increase the performance and profitability of manufacturing companies using modern automated systems and management. This will provide the basis for effective career as managers who can meet the challenges of rapidly changing manufacturing industry.

Master of Science in Manufacturing Engineering (MSMFGE)

Course Requirements
Term 1 2 3 4 5 6
Advanced Mathematics 6 units 6
Core Courses 15 units 3 6 6
Cognates / electives 6 units 3 3
Methods of Research 3 units 3
Comprehensive Exam 0 unit 0
Thesis 6 units 6 0
Orientation for Non-DLSU graduates (1 unit) (1)
Total 36(1)units 9(1) 9 9 3 6

Thesis Writing: MFE851M, MFE852M,…,MFE859M
Form: EN-19

Application for Dissertation Defense
Form: EN-18

Publication Requirement

One (1) publication in a refereed journal and one (1) conference proceeding

Advanced Mathematics (MS MFGE)

Course Code Course Title Brief Description
COE5310 Advanced Mathematical Methods Review of linear algebra and linear differential equation, existence and uniqueness, autonomous systems, phase portraits, nonlinear system, linearization, stability, perturbation, chaos and bifurcation.
COE5320 Numerical Methods with Computer Programming and Application Matrix computations, roots of linear and non-linear system, interpolation, numerical integration and differentiation, predictor connector, and Runge Kutta Methods, finite difference methods and introduction to finite element methods.
COE5100 Statistical Analysis and Design Basic Research Methods; Analysis of variance and covariance; Experimental Design; Advanced Regression Analysis; Non-Parametric Test
COE5110 Machine Learning for Engineers This course offers a comprehensive introduction to the field of machine learning, equipping students with fundamental concepts and practical skills needed to develop and apply machine learning models. Through a combination of theoretical lectures, hands-on exercises, and real-world projects, students will leverage various machine learning algorithms and techniques to solve complex problems and make data-driven decisions.
The topics will be discussed and demonstrated using the MATLAB environment, emphasizing the proper application of these techniques alongside statistical measures and analysis in addressing engineering problems.

MS MFGE Core Courses (Advanced Robotics and Automation)

Course Code Course Title Brief Description
MFG577M Switching Theory This graduate course covers topics on Number system and codes, Boolean algebra, combinational circuit minimization, sequential circuit analysis and synthesis, and sequential circuit state minimization.
MFG610M Basic Robotics This graduate course explores the principles, design, and control of pick and place robots, with an emphasis on system engineering for robotics applications. Students will gain a comprehensive understanding of robot structure, kinematics, programming, and control strategies. It also covers both inverse and forward kinematics analysis, providing the theoretical foundation and practical skills necessary for designing and implementing robotic solutions for industrial automation, manufacturing, and research applications.
MFG611M Advanced Robotics with Lab. 1 This graduate course provides an in-depth exploration of the principles, design, and control of pick and place robots, emphasizing system engineering for industrial and research applications. The course covers robot structure, actuation, programming, motion planning, and control strategies, integrating theoretical concepts with hands-on implementation. Special attention is given to forward and inverse kinematics, dynamic modeling, and real-time trajectory planning.
MFG612M Intelligent Systems with Lab. This graduate course deals with the algorithms used in developing learning and adaptive systems, artificial intelligence, theories and applications of neural networks, fuzzy logic, genetic algorithm, and knowledge-based systems.
MFG614M Soft Robotics This graduate course offers a thorough introduction to soft robotics, a quickly developing discipline that focuses on creating robots out of compliant materials. The basic ideas of soft materials, different actuation and sensing methods, and fabrication procedures unique to soft robotic systems will all be covered by the students. Students will learn how to design, model, and build soft robots for a variety of applications, such as healthcare, bio-inspired robotics, and human-robot interaction, through lectures, practical projects, and analysis of recent research. The emphasis will be on the special benefits of compliance and adaptability in robotic design.
MFG615M Robot Motion Planning and Control This graduate course explores the basic ideas and useful algorithms of robot motion planning, giving students the tools they need to create and put into practice plans for autonomous robots to maneuver through challenging settings. While taking into account constraints like obstacle avoidance, kinematic limitations, and dynamic constraints, we will investigate a variety of planning techniques, such as sampling-based methods (PRM, RRT), graph search algorithms (A*), and optimization-based approaches. Students will gain the ability to design motion planning problems, assess various planning methodologies, and apply these ideas to practical robotic applications through theoretical analysis and practical programming projects.
MFG616M Advanced Robotics with Lab. 2 This graduate course explores advanced topics in robotics, focusing on robotic kinematics, manipulator modeling, and sensory integration. Graduate students will analyze the mathematics of robot manipulation, path planning, and navigation for autonomous mobile robots. Special attention is given to multi-robot cooperative systems, including real-time applications, vision-based perception, and decentralized control. The course also covers robotic control architectures, motion planning algorithms, and AI-based robotic decision-making.
MFG618M Modeling and Simulations in Bioinspired Robotics This graduate course focuses on the modeling, analysis, and simulation of robotic systems inspired by biological organisms. Topics include kinematic and dynamic modeling, control strategies, and the integration of biological principles into robotic designs for applications in healthcare, agriculture, and autonomous systems. Emphasis is placed on the use of computational tools for simulating the behavior and performance of bioinspired robots, including soft robotics, swarm robotics, and autonomous navigation systems. The design of feedback in surgery and inclusion of tactile information in describing mechanical properties of tissue will be discussed. Various haptic interfaces and their specifications will also be analyzed. Students will explore how biological mechanisms such as locomotion, sensing, and adaptation can be translated into robotic systems.

MS MFGE Core Courses (Biomanufacturing and Sustainable Materials)

Course Code Course Title Brief Description
MFG670M Advanced Materials Processing This graduate course explores how advanced manufacturing technologies are applied to process and utilize advanced materials. It discusses advanced manufacturing technologies including additive manufacturing, integrated circuit processing, electronics assembly and packaging, micro/nanofabrication technologies, automation manufacturing systems.
MFG672M Manufacturing Materials This graduate course discusses the atomic structure, assemblies of atoms, solidification, bonding, the structure of solids, alloy systems, crystal imperfections, mechanical properties; brittleness and toughness, diffusion, creep and wear, fatigue, ceramics and glasses, polymers, composites, thermal properties, corrosion, selection and utilization of materials. Electrical properties of materials; electrical conduction in metals, free electron theory, photoelectric and thermoelectric effects in metals. Bond theory of solids, properties of insulators, metal and semiconductors. Intrinsic and extrinsic semiconductors. Compound semiconductors and magnetic materials.
MFG673M Polymer Science This interdisciplinary graduate course explores the advanced principles of polymer science with a focus on applications in manufacturing and biomedical engineering. The course covers the synthesis, processing, structure-property relationships, and functional performance of polymers. Students will study cutting-edge polymer technologies, including high-performance polymers, biodegradable and bioresorbable materials, nanocomposites, and smart polymers. The course will address the role of polymers in additive manufacturing, coatings, and advanced composites for industrial applications. Additionally, biomedical applications such as polymeric biomaterials for drug delivery, tissue engineering, and medical implants will be examined. Polymer characterization techniques, degradation mechanisms, and sustainability in polymer design will also be discussed.
MFG720M Advanced Manufacturing Sustainability Tools and Strategies This course offers a thorough investigation of Flexible Manufacturing Systems (FMS) by fusing theoretical ideas with real-world, interactive lab experiences. Students will get a comprehensive understanding of FMS’s function in contemporary manufacturing as they dig into its design, implementation, and administration. The integration of robots, automated material handling systems (AMHS), computer-aided design (CAD), computer-aided manufacturing (CAM), and computer numerical control (CNC) machines in a flexible manufacturing setting is covered in the program. Through the design, programming, and operation of a scaled-down FMS, the laboratory component of this course will enable students to put their theoretical knowledge into practice, strengthening concepts through hands-on experience.
MFG690M Product Design This graduate course includes topics on total products design and project management through traditional and concurrent engineering practices, machine learning application in product design, designing and creation of innovative products for new markets, product evaluation through engineering design analysis, value analysis, failure mode and failure analysis, and sustainable and green design strategy with environmental context. This course also deals with the development of three-dimensional design, use of MATLAB programming language, Analytical Hierarchy Analysis, and Life Cycle Assessment.

MS MFGE Core Courses (Biosystems Technologies)

Course Code Course Title Brief Description
MFG719M Machine Learning for Green Production Machine Learning for Green Production is a graduate-level course that provides students with the latest knowledge and skills by combining sustainable production practices with machine learning. This course provides students with the requisite knowledge, skills, and tools for using machine learning techniques in designing, developing, and implementing solutions for industrial processes that are more sustainable on a global scale. The curriculum includes various topics, such as real-time monitoring and control, machine learning-based predictive modeling, manufacturing process optimization, and data collection and preprocessing for industrial applications.
MFG721M Digital Phenomics Digital Phenomics is an interdisciplinary graduate course that explores the integration of digital technologies with phenomics to enhance our understanding of phenotypes in various organisms. This course delves into the acquisition, analysis, and interpretation of phenotypic data using advanced digital tools and computational techniques. Graduate students will gain hands-on experience with light microscopy, satellite imaging for vegetation indices, sensor-based data analysis, electrical impedance spectroscopy, image analysis and computer vision, machine learning, and data integration for phenotypes at different scales from cellular localization, organ level, individual organism or plant level for temporal-spatial change, and farm and forest stand groups.
MFG722M Biological Systems Engineering Biological Systems Engineering is an interdisciplinary graduate course that integrates principles of engineering with biological sciences to design, develop, and manage systems and technologies that solve biological and environmental problems. This course emphasizes the application of engineering concepts to enhance the efficiency and sustainability of biological systems, including agricultural, ecological, and biomedical systems.
MFG597M Evolutionary Optimization for Complex Systems Evolutionary Optimization for Complex Systems graduate course explores the principles and applications of evolutionary computing algorithms in addressing complex optimization and decision-making challenges in manufacturing engineering and related fields. The course provides a comprehensive understanding of genetic algorithms (GA), evolutionary strategies (ES), genetic programming (GP), differential evolution (DE), and other nature-inspired metaheuristic techniques. Theoretical and practical insights will be discussed into how these algorithms mimic natural evolutionary processes to solve high-dimensional, nonlinear, and multi-objective problems. Sub-topics include adaptive search mechanisms, selection strategies, mutation and crossover techniques, fitness landscapes, hybridization with conventional optimization methods, and parallel implementations for scalability.
MFG797M Biomedical Informatics with computer Lab. This graduate course provides an overview of the field of biomedical and health informatics. It includes topics on biomedical data acquisition, data storage and encoding, information retrieval, data analysis, and visualizations. It aims to develop and evaluate computational methods for biomedical discovery and decision making; enhance clinical care and biomedical engineering research through information management; and integrate research, training, and adoption of information technology in biomedical engineering and health systems. Other topics include biomedical imaging informatics, biomedical signal processing, clinical information systems, data mining, electronic health records, hospital information systems, machine learning applications, patient monitoring systems, and public health systems. Special topics on biomedical data standards, privacy, security, ethics, health data governance, and risk management are also covered.

MS MFGE Core Courses (Digital Manufacturing and Industry 4.0 and 5.0 Technologies)

Course Code Course Title Brief Description
MFG411M Cloud Deep Learning Services and Solutions This course explores cloud-based deep learning services and solutions, focusing on how cloud computing enables the development, training, and deployment of deep learning models. Students will learn to use cloud platforms for handling large datasets, training models with GPU acceleration, and deploying AI applications. The course covers image recognition, natural language processing, and cognitive computing to power intelligent systems. Emphasis is placed on scalable, cost-effective deep learning solutions for manufacturing applications.
MFG612M Computer Vision with Lab. This graduate course introduces computer vision technology to graduate students majoring in Manufacturing Engineering. Emphasis is given to theory and algorithms used in computer vision applications for quality control in various manufacturing industries. Previous programming experience is required to complete course requirements, which involve implementing image processing algorithms and machine learning to solve real world problems. Topics include camera models, multi-view geometry, reconstruction, some low-level image processing, and high-level vision tasks like image classification and object detection.
MFG650M Digital Technology and Machine Control-Automated Manufacturing Systems This graduate course covers an in-depth study of digital technology and machine control in automated manufacturing systems. It explores software interfacing, programming language levels, operating systems, and artificial intelligence applications. The course explores computer interfacing techniques for machine control, including input/output methods, interrupts, and handshaking mechanisms. Additionally, it examines interfacing with digital and analog actuators and sensors, real-time control systems, industrial controllers, and distributed computing. The laboratory component reinforces theoretical concepts through hands-on implementation using industry-standard tools and controllers.
MFG692M Quality and Reliability This graduate course provides an in-depth understanding of quality management and reliability engineering principles in manufacturing and engineering systems. Topics include Total Quality Management (TQM), Continuous Improvement, ISO 9000 certification procedures, supplier quality assurance, and statistical process control. Students will explore quality function deployment, benchmarking techniques, and Taguchi methods for quality engineering. The reliability component covers techniques for improving reliability, failure mode theories, field testing, and fault analysis methods such as Failure Mode Effect and Critical Analysis (FMECA), Fault Tree Analysis (FTA), and Event Tree Analysis (ETA).
MFG712M Flexible Manufacturing Systems with Lab. This course offers a thorough investigation of Flexible Manufacturing Systems (FMS) by fusing theoretical ideas with real-world, interactive lab experiences. Students will get a comprehensive understanding of FMS’s function in contemporary manufacturing as they dig into its design, implementation, and administration. The integration of robots, automated material handling systems (AMHS), computer-aided design (CAD), computer-aided manufacturing (CAM), and computer numerical control (CNC) machines in a flexible manufacturing setting is covered in the program. Through the design, programming, and operation of a scaled-down FMS, the laboratory component of this course will enable students to put their theoretical knowledge into practice, strengthening concepts through hands-on experience.
MFG713M Information Modeling with Lab. This graduate course provides a modern approach to manufacturing systems development, focusing on information modeling and database design. It covers the role of systems analysts, the systems development life cycle (SDLC), and structured systems analysis. Students will explore functional, data, and process modeling methodologies. The course also introduces principles of database design and management, including relational database concepts, data definition and manipulation, query processing, and object-oriented database methodology. The laboratory component provides hands-on experience in designing, implementing, and optimizing database systems for manufacturing applications.
MFG718M Cyber-Physical Systems This graduate course explores the intersection of computational intelligence, physical processes, and networked systems that define Cyber-Physical Systems (CPS). Graduate students will study modeling, simulation, and control of CPS, including finite state machines, physical process modeling, and cyber-physical interaction. The course also introduces Cyber-Physical Fabric (CPF), which is an integrated system of AI, IoT, digital twins, virtual labs, AR/VR environments, and smart infrastructures, to address real-world challenges for manufacturing, biomedical and related industries. Special topics include Game Theory (GT) for strategic decision-making in autonomous systems and Digital Twin (DT) technology for predictive analysis and real-time system monitoring. Through simulation, programming, and research, students will develop CPS solutions for industry, healthcare, smart cities, and intelligent infrastructure.

MS MFGE Core Courses (Intelligent Production Systems)

Course Code Course Title Brief Description
MFG574M Neural Networks Neural Networks course provides a comprehensive study of artificial neural networks (ANNs) and deep learning techniques, focusing on their applications in manufacturing and biomedical engineering. The course covers in-depth foundation in neural network models, supervised and unsupervised learning paradigms, and modern deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Advanced topics include physics-informed neural networks (PINNs), graph neural networks (GNNs), and other advanced hybrid models for intelligent decision-making. Graduate students will develop and implement neural network models using tools like MATLAB, TensorFlow and PyTorch, with applications in manufacturing engineering, biomedical engineering, and process optimization.
MFG575M Artificial Intelligence in MEM – Simulation and Modeling Artificial Intelligence in MEM – Simulation and Modeling course provides computational and programming-based approach to AI-driven simulation and modeling in manufacturing engineering, biomedical engineering and related fields. Topics include machine learning (ML), neural networks, heuristic optimization, stochastic models, and reinforcement learning to simulate and optimize manufacturing processes, biomedical and production systems. The course focuses on Python (NumPy, SciPy, TensorFlow, PyTorch) and MATLAB for implementation. Graduate students will develop AI-based models for predictive maintenance, quality control, scheduling, and production system optimization.
MFG576M Fuzzy Logic Fuzzy Logic course provides fundamental and advanced fuzzy logic systems, emphasizing applications in manufacturing automation, biomedical signal processing, and intelligent decision-making systems. Topics include Type-1 and Type-2 Fuzzy Logic, Adaptive Fuzzy Systems, Fuzzy Neural Networks, and Quantum Fuzzy Systems. It covers theoretical foundations, develop fuzzy controllers, and apply hybrid fuzzy models in real-world case studies using MATLAB/Python.
MFG651M Conventional Control with Lab. This graduate course discusses theories and application of controls applied to robotics and manufacturing processes. Conventional controllers (P, PI, PD, PID), Modern controllers; matrix, vector and differential equations.
MFG652M Programmable Logic Control This graduate course discusses the Programmable Logic Controller (PLC) applied for controlling manufacturing processes and assembly processes. It includes discussions of PLC design, structure, operation, and applications. This course covers foundational knowledge on Boolean algebra, logic gates, relay, switching theories, and pneumatic controls.
MFG691M Manufacturing Productive Control This course covers manufacturing systems, work design, mechanization, and automation. It focuses on system performance, production flow, inventory control, and scheduling. Topics include management policies, resource allocation, forecasting, and Manufacturing Resource Planning (MRP). It also explores Just-in-Time (JIT) production, Kanban systems, and the role of Information Technology in production planning. The course includes facility layouts, cellular manufacturing, part family formation, and coding systems for process optimization.
MFG695M Intelligent Production Systems Modeling This graduate course deals with the principles and methodologies for modeling, analyzing, and optimizing intelligent production systems in both manufacturing and biomedical engineering contexts. It explores the efficient allocation of scarce resources such as money, manpower, energy, and other related factors necessary for good decision making in manufacturing, production, service, and biomedical sectors. It also includes the fundamental concepts of static and dynamic models, linear and nonlinear programming, integer and non-integer.

MS MFGE Core Courses (Intelligent Transportation Systems)

Course Code Course Title Brief Description
MFG410M Computational Intelligence on Logistics and Supply Chain This course explores how computational intelligence enhances logistics and supply chain management, improving efficiency, decision-making, and overall operations. It examines advanced techniques for solving challenges in transportation, inventory management, scheduling, and network design, with a strong focus on data-driven methods and advanced software solutions.
MFG700M High- and Low-Level Computer Programming Language (with Computer Lab) This course covers high and low-level programming for solving problems in engineering and manufacturing, using languages such as Python and MATLAB. It includes coding fundamentals, data processing, visualization, numerical computing, simulation, and optimization. Low-level programming concepts such as memory management, pointers, and hardware interaction are introduced to provide a foundational understanding of how software interacts with computer systems. The focus is on developing efficient code for data analysis and real-world applications in manufacturing engineering.
MFG710M Software Engineering & Management with Lab. This course explores the software engineering process and the skills needed to undertake software engineering activities. Covers the software engineering process, project management, documentation, requirements engineering, system analysis, design, implementation, testing and trials, and maintenance and inspection. Management of the software development process, including: how projects arise; software lifecycles; the importance of requirements definition; planning; estimating; monitoring progress; risk; configuration management; quality management, and quality metrics.
MFG461M Generative AI and Large Language Models This graduate course provides an in-depth exploration of generative artificial intelligence, focusing on large language models (LLMs). Students will examine the theoretical foundations, technological developments, applications, and ethical considerations of generative AI. The course includes hands-on projects and case studies to understand the practical aspects of designing, training, and deploying large language models in various domains.
MFG463M Technical Computing This is an advanced graduate-level course designed to equip students with the essential skills and knowledge in computational methods for solving problems in manufacturing and biomedical engineering. The course integrates principles of computer science, mathematics, and biomedical engineering to address challenges in data analysis, modeling, simulation, and visualization within the context of manufacturing applications. The course begins with an overview of programming fundamentals, focusing on a high-level programming language such as Python or MATLAB.

MS MFGE Core Courses (Product Design and Computational Manufacturing)

Course Code Course Title Brief Description
MFG572M Manufacturing Process This graduate course deals with the various manufacturing processes including metal forming and sheet metalworking. It also discusses theory and methods on material removal, property enhancing and surface processing operations, and assembly processes.
MFG573M Design of Machine Elements This course provides an in-depth exploration of the engineering design process, emphasizing the critical steps involved in developing mechanical components and systems that meet functional, structural, and performance requirements. Students will gain a strong foundation in advanced strength of materials, including the analysis of combined loading scenarios, beam and column behavior, connection forces, and stress concentration effects. The course also introduces statistical methods for reliability analysis and the fundamentals of finite element analysis (FEA) for assessing mechanical integrity. Topics in material failure, including static failure theories, fracture mechanics, and fatigue design, will be examined to equip students with the analytical tools necessary for predicting and mitigating failure in mechanical systems.
MFG630M Computer Aided Design with Computer Lab. This graduate course will give the students a thorough understanding of Computer-Aided Design (CAD) and the basic skills and knowledge needed to develop, evaluate, and alter digital 3D models. Students will learn how to design parts, assemblies, and technical drawings using industry-standard CAD software through a combination of lectures and practical lab sessions. The course emphasizes real-world engineering and design applications while covering important ideas including parametric modeling, feature-based design, and 3D visualization. In order to prepare for professions in product development, manufacturing, and related sectors, students will obtain experience in producing production-ready documentation, running simulations, and drawing comprehensive designs.
MFG631M Concurrent Engineering with Lab. This graduate course provides a comprehensive introduction to Concurrent Engineering (CE) principles and practices, emphasizing the integrated design and development of products and processes. Students will learn how to streamline product lifecycle management through simultaneous consideration of all stages, from conception to disposal. The course blends theoretical understanding with practical application, featuring a significant laboratory component where students will collaboratively apply CE methodologies to real-world design challenges. Topics include team-based design, design for manufacturability and assembly (DFMA), quality function deployment (QFD), risk management, and the use of computer-aided tools for collaborative design and simulation.
MFG632M Finite Elements Analysis with Lab. This course provides unified and detailed material on the Finite Element Method for engineers to solve primarily linear problems, with the main focus on structural mechanics. The aim of the course is to provide the necessary concepts, theories and techniques of the FEM for students to be able to use a commercial FEM package comfortably to solve practical problems and structural analysis. The course unifies topics on mechanics for solids and structures, energy principles, weighted residual approach, the finite element method, and techniques of modeling and computation. Matlab is used as the programming environment for implementing the Finite Element Method.
MFG633M Computer Numerically Control (CNC) Tech. With Lab. This graduate course focuses on CNC machine tools, the major types, axis labeling, design principles. The design and operation of a controlled axis. Static and dynamic errors , the accuracy and testing of CNC machine tools. Part description, manual programming, work piece modeling and computer assisted part programming. The structure of major programming languages. CNC machine tool utilization. The integration of design and manufacture. Product model concepts and manufacturing code generation. Tool management.
MFG634M Computer Aided Design This course is an introduction to the different modern computer software/tools that is used in today’s modern manufacturing facilities to aid in the product design creation/process. It covers topics such as Computer-Aided Design (CAD), Finite Element Analysis (FEA), Computer Aided Manufacturing (CAM), Robotics Simulation, Digital/Virtual Manufacturing, Computational Fluid Dynamics (CFD), Mechanical Event Simulation, Agent-Based Simulation, and Biomechanical Simulation. It is aimed that at the end of the course, the students will have a good understanding of the roles of each field in Product Lifecycle Management (PLM).
MFG671M Metal Casting This graduate course provides an in-depth study of advanced metal casting techniques, materials (metallurgical considerations: alloy selection, grain refinement, and post-casting treatments), and includes discussion on process optimization strategies. Students will explore the fundamental principles of casting, including solidification behavior, heat transfer, and fluid dynamics while learning about different simulation tools and experimental methods. The course covers modern casting technologies such as investment casting, die casting, sand casting, and centrifugal casting, with a focus on defect analysis, quality control, and sustainability. The integration of additive manufacturing in casting methods will also be discussed together with other new innovations.
MFG690M Product Design This graduate course includes topics on total products design and project management through traditional and concurrent engineering practices, machine learning application in product design, designing and creation of innovative products for new markets, product evaluation through engineering design analysis, value analysis, failure mode and failure analysis, and sustainable and green design strategy with environmental context. This course also deals with the development of three-dimensional design, use of MATLAB programming language, Analytical Hierarchy Analysis, and Life Cycle Assessment.
MFG693M Industrial Design with Lab. This graduate course covers the principles and applications of industrial design with a focus on the development of form, function, color, safety, and ergonomics in engineering and consumer products. Topics include the relationship between form and viability, selection and specification of colors, design strategies for safety, legal considerations, risk assessment, and quality control. The course also covers the role of standardization and modular systems in industrial design. Students will apply ergonomics and CAD tools in product design through case studies, including vehicle interior layouts and consumer product development.
MFG711M Computer Integrated Manufacturing Systems with Lab. This graduate course focuses on fundamental principles of systems theory and its application to design, implementation and management of Computer Integrated Manufacturing Systems (CIMS). It discusses manufacturing operations including automation, material handling, flexible systems, and internet technologies in e-manufacturing. This course utilizes simulation and automation software to plan, analyze and optimize manufacturing processes.

OTHERS

Course Code Course Title Brief Description
COE5000 Engineering Orientation The course includes topics on the DLSU history, mission statement, organizational structure, key officers/offices; the Brothers of the Christian Schools, the life and writings of St. John Baptist de la Salle; Lasallian core values and professional ethics.
COE996M Oral Comprehensive Exam
COE5200 Methods of Research A study of the fundamentals of research designs, analysis and interpretations of data, project feasibility studies, and qualitative research techniques
COE571M Techpreneurship This course takes the participant through entrepreneurship in technology ventures, which is about commercializing technology ideas into viable enterprises. It is about training techies, scientists and researchers in the skills and attitudes of entrepreneurs, about empowering them to realize the opportunities and commercial values arising from their ideas, technologies, technology applications or products. The course examines the development of ideas and how these are translated into opportunities and eventually businesses; it challenges the students to go through the process of writing a business plan, which will be their final output in this course.