• Learn principles and philosophies of engineering design.
• Understand the impact and implications of design decisions on subsequent stages of product development.
• Integrate the distribution and life cycle of a product into early product design.
• Study the fundamental concepts of sustainable design and the impact it has on product development and design decisions

This course will teach the basic principles and philosophies of engineering design, with emphasis on mechanical engineering, given its broad scope of applications, including but not limited to stress analysis and fracture, heat transfer, kinematics, and systems packaging. 

Recognizing that design is a first step in a sequence of decisions and steps (development, manufacture, distribution, and the life cycle of a product), this course will examine the influence of materials, manufacturing, and other downstream decisions and actions on a product's overall design.

What we design and how we develop it also impacts our environment for years to come, so importantly, this course will also cover various aspects of sustainable design from a product life- cycle perspective. Thus, students will be exposed to the fundamental concepts of Design for "X" and specifically the impact that DfM (Design for Manufacturing) and DfA (Design for Assembly) have on product development and how they affect design decisions. Students will also be exposed to the fundamental concepts of Sustainable Design (also known as Design for Sustainability — DfS, EcoDesign, Sustainability Engineering, LCA and others) and specifically the impact that DfS has on product development and how it affects design decisions. This class includes lectures and lab activities.

Pre/Co-requisite: 49-602 - Designing for Production & Sustainability

• Learn the capabilities and limitations of current technologies such as AI, data science and interactive connected products.
• Learn to frame a problem, choose data sources, and perform exploratory data analysis.
• Practice basic modeling and prototyping, then communicate the results.
• Complete a final project incorporating elements of connected products and smart systems.

Driven by the combination of increased access to data, local data storage, computational power, interconnectivity, and improved algorithms, mainstream technologies increasingly incorporate sensing (IoT) and data science (AI) "smarts." As examples, thermostats, locks, power sockets, and lights are being imbued with sensors and connectivity. There is great potential to layer in machine learning, natural language and speech processing, expert systems, robotics, and vision. 

Historically, early applications of these capabilities were designed to operate on their own, on very narrow tasks, based on pre-programmed knowledge. Today, we have the ability to design human-computer systems in which both humans and computers act intelligently, adapt to the world, and learn from experience, improving their performance over time.

After an introduction of some basic concepts and techniques in AI, data science and interactive connected products, the course illustrates both the potential and current limitations of these techniques with examples from a variety of applications. Topics explored will include awareness, real-time sensing and communication, embedded intelligence, and designing experiences for the Internet of Things. We will also spend some time on understanding the strengths and weaknesses of human decision-making and learning, specifically in combination with AI systems that would be resident in product applications.

Students will be introduced to technologies and AI algorithms through a series of hands-on exercises, collaborative projects, in-depth discussions, and instructor-led tutorials.

Students also complete a final project that takes a project from start to finish, incorporating elements of connected products and smart systems (framing the problem, choosing data sources and required sensors, exploratory data analysis, basic modeling, basic prototyping, communicating results). The course is designed such that students can complete this course with only a basic familiarity with statistics, without any knowledge of programming, electronics, or systems.