Bridge the Talent Gap. Lead Industry 4.0.
Industry 4.0 is revolutionizing manufacturing with AI, automation, digital twins, and smart factories, but the talent to implement these technologies is falling behind. Lawrence Technological University’s new Doctor of Engineering in Advanced Manufacturing was built to close that gap.
Designed for today’s engineering professionals, this program equips you with the meaningful skills to lead innovation using simulation, IoT, additive manufacturing, big data, and more. Whether you’re advancing your career or transforming your company, LTU will prepare you to be the expert Industry 4.0 demands.
Course Name
Course #
Credits
This course equips doctoral students with the essential knowledge and skills to design, conduct, and critically evaluate research in advanced manufacturing. Topics include hypothesis generation and validation, experimental design, development of data collection instruments, and various validation techniques. Students will explore the theoretical foundations and practical applications of quantitative, qualitative, and mixed-method research approaches. Emphasis is placed on developing original research contributions at the doctoral level. Lecture 3 hrs. 3 credit hours.
AME7103
3
This course explores the transformative role of artificial intelligence in modern manufacturing, focusing on predictive analytics of manufacturing systems and intelligent decision-making. Students will gain applied knowledge of AI-driven solutions for smart factories and connected systems. Key topics include the integration of the Internet of Things (IoT), cloud computing, fuzzy logic, neural networks, genetic algorithms, and deep learning for manufacturing process improvement. The course also examines AI applications in manufacturing execution systems as part of the broader Smart Manufacturing framework. This course explores advanced digital manufacturing technologies, with a focus on additive manufacturing (3D printing) and digital twin applications. Students will examine process optimization strategies, emphasizing global product development and innovation. The course highlights how both plastic and metal additive manufacturing can overcome key limitations of traditional manufacturing. Lecture 3 hrs. 3 credit hours.
AME7203
3
This course introduces Discrete-Event Simulation as a design and analysis tool for manufacturing systems. Students will learn how to conduct a simulation project. Topics in simulation methodology include: building valid models, selecting input probability distribution, statistical analysis of output, design of simulation experiments, optimization and performance improvements. Additionally, it covers the principles, applications, and implementation strategies of digital twins for process modeling, simulation, and optimization. Digital twinning facilitates virtual analysis, enabling seamless interaction between simulations and real-world systems to identify and address potential issues before they arise. Each student will conduct a real-life simulation project. Lecture 3 hrs. 3 credit hours.
AME7303
3
This course covers advanced statistical analysis and quality control methodologies for manufacturing and process optimization. Topics include data analytics, regression analysis, design of experiments (DOE), analysis of variance (ANOVA), and the Taguchi method for robust design. Emphasis is placed on process improvement strategies for variation reduction and quality enhancement. The course also explores Design for Six Sigma (DFSS) principles in product development, incorporating industry standards such as the Production Part Approval Process (PPAP) and Advanced Product Quality Planning (APQP). Lecture 3 hrs. 3 hours credit.
AME7403
3
This course provides an in-depth exploration of advanced optimization techniques and their applications in manufacturing systems. Topics include advanced formulation methods for large-scale linear programming models, integer and combinatorial optimization, and nonlinear programming. Students will also study data mining techniques, heuristic search methods, numerical methods, matrices, and systems of linear equations. Emphasis is placed on leveraging these techniques to enhance efficiency, decision-making, and problem-solving in complex manufacturing environments. Lecture 3 hrs. 3 hours credit.
AME7503
3
Total Credits:
15
In addition to the 15 credit hours of coursework, the program has a research component requiring a minimum of 15 credit hours of independent (candidate-conducted) studies resulting in the production of a “thesis.” This part of the degree requirement, which can be done in parallel to coursework, is conducted under the supervision of an LTU faculty member (preferably, jointly with an industry expert), resulting in publishable outcomes.
Course Name
Course #
Credits
College Composition develops students’ acquisition of the fundamental principles of academic writing. This course focuses on the development of writing thesis statements and main arguments, topic sentences, transitional words and phrases, supporting paragraphs, use of evidence, essay organization, and research skills. Extensive writing and research practice is required.
DES1213
3
College Composition develops students’ acquisition of the fundamental principles of academic writing. This course focuses on the development of writing thesis statements and main arguments, topic sentences, transitional words and phrases, supporting paragraphs, use of evidence, essay organization, and research skills. Extensive writing and research practice is required.
DES1213
3
College Composition develops students’ acquisition of the fundamental principles of academic writing. This course focuses on the development of writing thesis statements and main arguments, topic sentences, transitional words and phrases, supporting paragraphs, use of evidence, essay organization, and research skills. Extensive writing and research practice is required.
DES1213
3
Total Credits:
15
To be eligible for admission to LTU’s Doctorate of Engineering in Advanced Manufacturing, applicants must satisfy the following requirements:
The LTU program director may use discretion to waive one or more of the above requirements.
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