To maintain cost competitiveness when moving production on a popular product from Asia to the US, Reverie needs to increase the automation of the assembly and packaging process. Students on this team will evaluate the design of the EZ-Lift™ Universal Foundation, develop an approach to automate the assembly, and perform the business case analysis to support moving the manufacturing location to the US.
Abstract:
Since 2003, Reverie has been designing and producing the world’s most technologically advanced adjustable bed bases. One of the key components to a good night’s sleep is the box spring or foundation under your mattress. These foundations can come in 5” or 9” heights which are selected based on consumer preference of bed height. It is quite common that consumers don’t know what foundation height they need, and in many states, an open foundation is something that cannot be returned. This led Reverie to develop the EZ-Lift™ Universal Foundation. This simple structure allows in home bed height adjustability.
The EZ-Lift™ Universal Foundation is currently manufactured in Asia utilizing up to a dozen people to assemble and package it and then is shipped to the US. With freight costs increasing, Reverie is investigating a way to automate enough of the assembly of this product to manufacture it in the US using no more than 4 people to complete all operations. Students on the Reverie team will evaluate the design of the EZ-Lift™ Universal Foundation, develop an approach to automate the assembly, and perform the business case analysis to automate part of the full assembly in Asia and/or support moving the manufacturing location to the US.
Impact:
If Reverie can assemble the EZ-Lift™ Universal Foundation in the United States and make their assembly operations efficient in Asia, it can simplify their distribution channels, and become more competitive in the market. Manufacturing in the United States reduces the environmental impact of international freight and brings more jobs back to the US.
Scope:
Minimum Viable Product Deliverable (Minimum level of success)
- Literature and background review including:
- existing design and manufacturing process
- Reverie internal best practices
- relevant patents and competing products
- industry best practice for automated manufacturing
- Baseline business evaluation of existing manual process and freight costs
- Study of which parts of the process are optimal for automation vs human operation.
- Design for hardware and software for automated assembly of quarter scale foundation.
- Evaluation of current foundation design and proposal of design modifications for automated assembly.
- Selection of necessary hardware and software required such as robotic arms, computer vision cameras, sensors, etc.
- Business case evaluation
Expected Final Deliverable (Expected level of success)
- Simulated automated assembly process using no more than 4 people.
- Modification of scale EZ-Lift™ foundation demonstration device to support increased automation of assembly.
- Functional prototype of automated assembly device on quarter scale foundation
Stretch Goal Opportunities: (High level of success)
- Virtual and or mixed reality demonstration of hybrid automated assembly method.
- Evaluate automating quality inspection using computer vision AI technology.
Below are the skills needed for this project. Students with the following relevant skills and interest, regardless of major, are encouraged to apply! This is a team based multidisciplinary project. Students on the team are not expected to have experience in all areas, but should be willing to learn and will be asked to perform a breadth of tasks throughout the two semester project.
Mechanical Design (2 students)
Specific Skills: Techniques for fast prototyping and design for manufacturing. Good hands-on skills for development.
Completion of any of the following courses is a plus:
ME305, ME350, ME450, ME452, ME483
Likely Majors: ME, ROB
Robotic Programming (2 Students)
Specific Skills: Control systems and simulations using ROS. Evaluation and specification of robotic tools and processes for automating assembly including hardware and software, cameras, data collection, etc.
Work will be done in Gazebo and ROS.
Likely Majors: ROB, CS, EE, ECE
Operations Analysis (2 Students)
Specific Skills: Analysis of manufacturing operations. Engineering to maximize profits and minimize costs.
Likely Majors: IOE
Business Case Development (1 Student)
Specific Skills: Analysis of business case for design and manufacturing. Must also have experience in or be willing to learn coding and other tech aspects of the project.
EECS 281 (or equivalent) is required or dual major in ME, ROB, CE, EE.
Likely Majors: CS, BBA, IOE
Additional Desired Skills/Knowledge/Experience
If you have any of these characteristics, highlight them on your Experience and Interest Form and talk about them in your (optional) one way video interview.
- Ability to navigate through challenging situations and approach them with a logical and systematic mindset.
- Creativity to come up with innovative solutions and designs, think outside the box, exploring unconventional approaches as needed.
- Interest in designing the best possible consumer products.
- Experience with video gaming, 3D graphics
- Experience with augmented/virtual/extended reality environments.
- Experience with Gazebo, and/or ROS. Please indicate your level of experience in your application materials
- Understanding of different manufacturing processes (e.g., machining, welding, assembly) and their automation potential.
- Proficiency in programming languages like Python, C/C++, or Java for controlling robotics systems.
- Understanding of mechanics, kinematics, and dynamics. Familiarity with CAD software (e.g., SolidWorks preferred) for designing mechanical components.
- Great hands-on skills; interest in fast prototyping. We particularly value experience working on engineering competition teams.
Sponsor Mentor
Kushan Gajjar
Senior Mechanical Engineer
Kushan Gajjar has been with Reverie in the role of Mechanical Engineer since 2016. He earned a master’s degree in mechanical engineering from the University of Michigan, Ann Arbor, and was previously a member of Reverie’s MDP team from 2014 to 2015. Currently, his main responsibilities include developing new designs for adjustable beds and flat foundations, as well as overseeing Reverie’s supply chain in Asia.
Faculty Mentor
Peter Gaskell
Peter Gaskell is a Lecturer in the Robotics Department. In addition to teaching, he designs and builds robotics platforms for both education and research. He has worked in a wide array of research labs in diverse fields such as Astrophysics, Atomic Optics, Nanoelectronics, Materials Science and Robotics. He is the co-founder and principal designer for a professional audio company and has designed equipment used to record multiple Grammy Award winning albums. He is also on the technical advisory board for a Montréal based start-up focused on using nanomaterials to improve sound quality and efficiency of transducers for the consumer electronics industry. He holds a BS in Physics from the University of Oregon and earned his MEng and PhD in Electrical Engineering from McGill University where his dissertation focused on advanced nanomaterials for lithium-ion electric vehicle batteries.
Weekly Meetings: During the winter 2025 semester, the Reverie team will meet on North Campus on TBD.
Work Location: Most of the work will take place on campus in Ann Arbor. Some testing and integration work will take place at the Reverie site in Bloomfield Hills, Michigan. (MDP will provide transportation)
Course Substitutions: CE MDE, ChE Elective, EE MDE, CoE Honors, IOE Senior Design, MECHENG 490, SI Elective/Cognate
Citizenship Requirements: This project is open to all students. Note: International students on an F-1 visa will be required to declare part time CPT during Winter 2025 and Fall 2025 terms.
IP/NDA: Students will sign IP/NDA document(s) that are unique to Reverie.
Summer Project Activities: No summer activity will take place on the project.
Learn more about the expectations for this type of MDP project