COURSES @ NORTHEASTERN

Manufacturing Systems & Techniques + Lab

IE 4530/31

Programming Console

NSF IMPEL

Intelligent Manufacturing

Algorithms in Engineering Applications

 

Manufacturing Systems & Techniques + Lab

Focuses on digital design and manufacturing, and transformative industrial automation methods and technologies that drive different stages of the product development cycle, with special reference to conceptual design, product engineering, and manufacturing engineering. Topics include fundamentals of design and manufacturing automation, Computer-Aided Design (CAD) modeling and assembly, subtractive manufacturing, Computer-Aided Manufacturing (CAM), and Computer Numerical Control (CNC) programming, additive manufacturing and 3D printing, industrial robotics and robot programming, Programmable Logic Controllers (PLCs) and ladder logic programming, and an overview emerging methods and technologies in cyber manufacturing. Students learn three world-class industrial software packages including Autodesk Fusion 360™ for CAD modeling, assembly, and CAD/CAM CNC programming, OCTOPUZ® for offline, CAD/CAM robot programming and integrated factory simulation, and LogixPro 500 (Allen-Bradley’s RSLogix 500 simulator) for ladder logic PLC programming. Students also conduct digital manufacturing lab experiments to gain more in-depth gain hands-on experience in CNC machining, 3D printing, robot programming, and PLC programming.

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Learning Objectives

  • Learn the fundamentals of industrial automation, the Automation Pyramid, and its major driving technologies.

  • Learn hands-on CAD modeling and assembly for turning product design ideas and concepts into precise 3D CAD models and full-fledged digital prototypes using Fusion 360.

  • Learn subtractive manufacturing of complex 3D CAD models through hands-on CAD/CAM programming of 2D/3D CNC milling and CNC turning machines using Fusion 360.

  • Learn the additive manufacturing fundamentals, major processes, as well as the generative design and mesh editing tools of Fusion 360 for modifying and producing complex 3D CAD models through 3D printing.

  • Learn to program industrial robots for automated manipulation of manufactured parts (e.g., part transfer, welding, painting, CNC machine tending) through offline, CAD/CAM programming using OCTOPUZ.

  • Learn to program integrated manufacturing automation equipment enhanced with sensors and actuators with PLCs and advanced ladder logic programming instructions using LogixPro 500.

  • Apply the fundamental knowledge and digital tools learned in class into a Digital Design & Manufacturing (DDM) group projects that (1) turn students’ product design ideas into precise digital models, and (2) build digital, integrated manufacturing automation cells ready to be implemented on its real-world replica.

Intelligent Manufacturing

 

Focuses on intelligent design and manufacturing, the transformative industrial automation methods and technologies that drive different stages of the product development cycle, and the roles and applications of Artificial Intelligence (AI) and Machine Learning (ML) techniques for augmenting product design and manufacturing processes. Topics include fundamentals of design and manufacturing automation, Computer-Aided Design (CAD) and intelligent design, subtractive manufacturing, Computer-Aided Manufacturing (CAM), Computer Numerical Control (CNC) programming, and intelligent machining, additive manufacturing and intelligent 3D printing, industrial robot programming and intelligent robot control, Programmable Logic Controllers (PLCs), ladder logic programming, and IoT-based PLCs, and an overview emerging methods and technologies in cyber manufacturing. Students learn three world-class industrial software packages including Autodesk Fusion 360™ for CAD modeling, assembly, and CAD/CAM CNC programming, OCTOPUZ® for offline, CAD/CAM robot programming and integrated factory simulation, and LogixPro 500 (Allen-Bradley’s RSLogix 500 emulator) for ladder logic PLC programming. Students conduct a group project on intelligent design and manufacturing to gain more in-depth hands-on experience in CAD/CAM, CNC, 3D printing, industrial robotics, and PLC programming.

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Learning Objectives

  • Learn the fundamentals of industrial automation, the Automation Pyramid, and intelligent industrial automation.

  • Apply CAD modeling and assembly for turning product design concepts into full-fledged 3D CAD prototypes using Fusion 360, and learn the fundamentals of intelligent design based on AI/ML techniques.

  • Perform subtractive manufacturing of complex 3D CAD models through hands-on CAD/CAM programming of 2D/3D CNC milling and CNC turning machines using Fusion 360, and learn the fundamentals of intelligent machining based on AI/ML techniques.

  • Learn the additive manufacturing principles and major processes, perform 3D printing of complex 3D CAD models using Fusion 360, and learn the fundamentals of intelligent 3D printing based on AI/ML techniques.

  • Perform offline, CAD/CAM programming of industrial robots for automated manipulation of manufactured parts (e.g., part transfer, welding, painting, CNC machine tending) using OCTOPUZ, and learn the fundamentals of intelligent robot manipulation based on AI/ML techniques.

  • Perform PLC/ladder logic programming of automated manufacturing systems enhanced with sensors and actuators using LogixPro 500, and learn the fundamentals of distributed and cooperative control of manufacturing systems.

  • Apply the fundamental knowledge and digital tools learned in class into an Intelligent Design & Manufacturing (IDM) group projects to (1) conceptualize, design, and generate CAD model and assembly of a mechanical product, (2) build, program, and simulate a digital, integrated manufacturing automation cell to produce the parts, and (3) explore various AI and machine learning techniques to enhance the efficiency, adaptability, and autonomy of the digital design and manufacturing processes.

 

The course covers theory and algorithms of optimization with applications to manufacturing and production operations, with emphasis on design and analysis of efficient algorithms and data structures. The topics include modeling of discrete and continuous optimization problems, graphs, network flow, divide and conquer methods, as well as greedy, approximate, randomized, and meta-heuristic/bio-inspired algorithms. Topics include algorithm foundations (mathematical preliminaries, algorithm analysis, and data structures), graphs (connectivity and traversal, bipartiteness, directed acyclic graph), greedy algorithms (scheduling, task partitioning, path planning, minimum spanning trees, clustering), divide and conquer algorithms (sorting, counting inversion, closest pair of points, fast Fourier transform), dynamic programming (scheduling, knapsack problem, sequence alignment, shortest path, distance vector protocol), network flow (max-flow min-cut problem, shortest augmenting path, bipartite matching, scheduling, project selection, assignment), intractability (poly time reductions, P, NP, NP-Complete, NP-Hard), approximation and randomized algorithms (load balancing, center selection, pricing, knapsack problem), and meta-heuristic/bio-inspired algorithms (e.g., genetic algorithm, particle swarm optimization, ant colony optimization, simulated annealing, tabu search). Engineering applications of algorithms in several areas including manufacturing, logistics, transportation, healthcare, and service sectors are discussed.

Programming Console

Learning Objectives

  • Analyze the computational tractability, asymptotic order of growth, and worst-case running times of algorithms, and standard implementation of data structures.

  • Apply graphs to solve engineering problems that require graph computations as their central components.

  • Apply greedy, divide and conquer, dynamic programming, and network flow algorithms to model, solve, and analyze engineering problems.

  • Analyze the complexity of engineering problems with respect to the complexity classes P, NP, NP-complete, and NP-hard.

  • Apply metaheuristic algorithms to model, solve, and analyze engineering problems.

  • Using Python, analyze and evaluate various algorithms to solve numerical problems in various engineering contexts and applications.