PSS Business Case Map: Supporting Idea Generation in PSS Design

This paper proposes the PSS Business Case Map as a tool to support designers’ idea generation in PSS design. The map visualizes the similarities among PSS business cases in a two-dimensional diagram. To make the map, PSS business cases are first collected by conducting, for example, a literature survey. The collected business cases are then classified from multiple aspects that characterize each case such as its product type, service type, target customer, and so on. Based on the results of this classification, the similarities among the cases are calculated and visualized by using the Self-Organizing Map (SOM) technique. A SOM is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional) view from high-dimensional data. The visualization result is offered to designers in a form of a two-dimensional map, which is called the PSS Business Case Map. By using the map, designers can figure out the position of their current business and can acquire ideas for the servitization of their business.

Topology Optimization of Blank Geometry for the Sheet Forming Process

The newly developed element exchange method (EEM) for topology optimization is applied to the problem of blank shape optimization for the sheet-forming process. EEM uses a series of stochastic operations guided by the structural response of the model to switch solid and void elements in a given domain to minimize the objective function while maintaining the specified volume fraction. In application of EEM to blank optimization, a sheet forming simulation model is developed using Abaqus/Explicit. With the goal of minimizing the variability in wall thickness of the formed component, a subset of solid (i.e., high density) elements with the highest increase in thickness is exchanged with a consistent subset of void (i.e., low density) elements having the highest decrease in thickness so that the volume fraction remains constant. The EEM operations coupled with finite element simulations are repeated until the optimum blank geometry (i.e., boundary and initial thickness) is found. The developed numerical framework is applied to blank optimization of a benchmark problem. The results show that EEM is successful in generating the optimum blank geometry efficiently and accurately.

Pre-Life and End-of-Life Combined Profit Optimization With Predictive Product Lifecycle Design

The Predictive Product Lifecycle Design (PPLD) model that is proposed in this paper enables a company to optimize its product lifecycle design strategy by considering pre-life and end-of-life at the initial design stage. By combining lifecycle design and predictive trend mining technique, the PPLD model can reflect both new and remanufactured product market demands, capture hidden and upcoming trends, and finally provide an optimal lifecycle design strategy in order to maximize profit over the span of the whole lifecycle. The outcomes are lifecycle design strategies such as product design features, the need for buy-backs at the end of its life, and the quantity of products remanufacturing. The developed model is illustrated with an example of a cell phone lifecycle design. The result clearly shows the benefit of the model when compared to a traditional Pre-life design model. The benefit would be increased profitability, while saving more natural resources and reducing wastes for manufacturers own purposes.

Enabling Decision-Based Design Using Solution Trajectory Correction and Backtracking Rules

Optimization is needed for effective decision based design (DBD). However, a utility function assessed a priori in DBD does not usually capture the preferences of the decision maker over the entire design space. As a result, when the optimizer searches for the optimal design, it traverses (or ends up) in regions where the preference order among different solutions is different from the actual order. For a highly non-convex design space, this can lead to convergence to a grossly suboptimal design depending on the initial design. In this article, we propose two approaches to alleviate this issue. First, we map the trajectory of the solution as generated by the optimizer and generate ranking questions that are presented to the designer to verify the correctness of the utility function. We then propose backtracking rules if a local utility function is very different from the initially assessed function. We demonstrate our methodology using a mathematical example and a welded beam design problem.

Plant-Limited Co-Design of an Energy-Efficient Counterbalanced Robotic Manipulator

Modifying the design of an existing system to meet the needs of a new task is a common activity in mechatronic system development. Often engineers seek to meet requirements for the new task via control design changes alone, but in many cases new requirements are impossible to meet using control design only; physical system design modifications must be considered. Plant-Limited Co-Design (PLCD) is a design methodology for meeting new requirements at minimum cost through limited physical system (plant) design changes in concert with control system redesign. The most influential plant changes are identified to narrow the set of candidate plant changes. PLCD provides quantitative evidence to support strategic plant design modification decisions, including tradeoff analyses of redesign cost and requirement violation. In this article the design of a counterbalanced robotic manipulator is used to illustrate successful PLCD application. A baseline system design is obtained that exploits synergy between manipulator passive dynamics and control to minimize energy consumption for a specific pick-and-place task. The baseline design cannot meet requirements for a second pick-and-place task through control design changes alone. A limited set of plant design changes is identified using sensitivity analysis, and the PLCD result meets the new requirements at a cost significantly less than complete system redesign.

Optimum Design of Columns Under Elastic Buckling

In this paper, a generalized approach is developed to optimize column configuration subjected to buckling load. The configuration utilizes B-spline contour to provide more freedom to model the column shape. Previous columns in literature use tapered or parabolic tapered for configuration. This work considers hinged-hinged columns of circular solid cross-sectional area. Two sample applications are optimized using Genetic Algorithm with the finite difference method to satisfy the buckling constraints. The length and load are fixed. The objective is to minimize the volume considering the cross-sectional diameters as the design variables. B-Spline quadratic with three and five control points and cubic with five control points are applied. The proposed configuration is compared with tapered and parabolic tapered columns. Results show that continuity provides a better optimum against column buckling than other tapered columns. Even though volume is more than some configurations by about 1.67%, but those configurations would not satisfy buckling constraints over the entire length of the column.

Identifying Key Product Attributes and Their Importance Levels From Online Customer Reviews

Identifying customer needs and preferences is one of the most important tasks in design process. Typically, a variation of interview based approaches is used to conduct need and preference analysis. In this paper, a new approach based on text mining online (internet based) customer reviews to supplement traditional methods of need and preference analysis is considered. The key idea underlying the proposed approach is to partition online customer generated product reviews into segments that evaluate the individual attributes of a product (e.g zoom capability and support of different image formats in a camcorder). Additionally, the proposed method also identifies the importance (ranking) that customers place on each product attributes. The method is demonstrated on 100 customer reviews submitted for camcorders on epinions.com over a two year period.

Prognosis Informed Design Framework for Operation and Maintenance of Wind Turbines

Advances in high performance sensing and signal processing technology enable the development of failure prognosis tools for wind turbines to detect, diagnose, and predict the system-wide effects of failure events. Although prognostics can provide valuable information for proactive actions in preventing system failures, the benefits have not been fully utilized for the operation and maintenance decision making of wind turbines. This paper presents a generic failure prognosis informed decision making tool for wind farm operation and maintenance while considering the predictive failure information of individual turbine and its uncertainty. In the presented approach, the probabilistic damage growth model is used to characterize individual wind turbine performance degradation and failure prognostics, whereas the economic loss measured by monetary values and environmental performance measured by unified carbon credits are considered in the decision making process. Based on the customized wind farm information inputs, the developed decision making methodology can be used to identify optimum and robust strategies for wind farm operation and maintenance in order to maximize the economic and environmental benefits concurrently. The efficacy of proposed prognosis informed maintenance strategy is compared with the condition based maintenance strategy and demonstrated with the case study.

Towards Robustly Stable Musculo-Skeletal Simulation of Human Gait: Merging Lumped and Component-Based Modeling Approaches

The objective of work presented in this paper is to increase the center-of-mass stability of human walking and running in musculo-skeletal simulation. The approach taken is to approximate the whole-body dynamics of the low-dimensional Spring-Loaded Inverted Pendulum (SLIP) model of locomotion in the OpenSim environment using existing OpenSim tools. To more directly relate low-dimensional dynamic models to human simulation, an existing OpenSim human model is first modified to more closely represent bilateral above-knee amputee locomotion with passive prostheses. To increase stability further beyond the energy-conserving SLIP model, an OpenSim model based upon the Clock-Torqued Spring-Loaded-Inverted-Pendulum (CT-SLIP) model of locomotion is also created. The result of this work is that a multi-body musculo-skeletal simulation in Open-Sim can approximate the whole-body sagittal-plane dynamics of the passive SLIP model. By adding a plugin controller to the OpenSim environment, the Clock-Torqued-SLIP dynamics can be approximated in OpenSim. To change between walking and running, only one parameter representing the preferred period of a stride is changed. The result is a robustly stable simulation of the center-of-mass locomotion for both walking and running that could serve as a first step toward increasingly anatomically accurate and robustly stable musculo-skeletal simulations.

A Classifier-Guided Sampling Method for Computationally Expensive, Discrete-Variable, Discontinuous Design Problems

Metamodel-based design is a well-established method for providing fast and accurate approximations of expensive computer models to enable faster optimization and rapid design space exploration. Traditionally, a metamodel is developed by fitting a surface to a set of training points that are generated with an expensive computer model or simulation. A requirement of this process is that the function being approximated is continuous. However, many engineering problems have variables that are discrete and a function response that is discontinuous in nature. In this paper, a classifier-guided sampling method is presented that can be used for optimization and design space exploration of expensive computer models that have discrete variables and discontinuous responses. The method is tested on a set of example problems. Results show that the method significantly improves the rate of convergence towards known global optima, on average, when compared to random search.