Haptic Interface With Hybrid Actuator for Virtual Tissue Cutting

Surgical training is an important and recent application where haptic interfaces are used to enhance the realism of virtual training simulators. Tissue cutting with surgical scissors is a common interaction mode in the simulations. The haptic interface needs to render a range of tissue properties and resistance forces accurately. In this research, we developed a hybrid haptic device made up of a DC servomotor and a magnetorheological (MR) brake. The motor can provide fast dynamic response and compensate for inertia and friction effects of the device. But it cannot supply high force levels and the sensation of stiff interaction with hard tissues such as tendons. On the other hand, the MR-brake can provide very high and stiff interaction forces yet cannot reflect fast dynamics that are encountered as the virtual scissors go through the tissue. Design details of the hybrid actuator and the haptic device are presented. A control scheme was developed to decompose the actuator command signal into two branches considering each actuator’s capabilities. Virtual tissue cutting experiments were conducted using three different scissor types and four types of rat tissue. Results are presented and discussed. Forces in a wider amplitude range compared to just using a DC motor could be generated by the hybrid actuator. It also enabled simulation of multiple scissor types using the same haptic interface due to the extended force amplitude range.

Computer Aided Ergonomics Through Parametric Biomechanical Simulation

Computer aided ergonomics and particularly biomechanical simulations hold high potential for the implementation of the virtual product development paradigm in the field of human-centric design. Unfortunately, the relation between efforts to be invested to the insights gained by musculoskeletal simulations is still not sufficient for a widespread industrial application. This contribution shows how parametric biomechanical simulations can be used to gain specific indications on how interaction points of human-centric products are to be designed to meet the competencies of a given target user. This is demonstrated using cycling and rowing as two exemplary activities involving the entire human body. These activities are empirically well studied and electromyographic as well as force measurements are available. The comparison of the biomechanical simulations to the real-world scenario permits the validation of the proposed parametric approach as well as the applied models. This is a prerequisite for its application along the product engineering process.

Designing a Self-Replicating Robotic Manufacturing Factory

This paper presents a Multiagent Systems based design approach for designing a self-replicating robotic manufacturing factory in space. Self-replicating systems are complex and require the coordination of many tasks which are difficult to control. This paper presents an innovative concept using Multiagent Systems to design a robotic factory for space exploration. Specifically presented is an approach for coordinating a conceptual model of a self-replicating system. The arrival of a set of agents on an unknown planet is simulated, whereby these simple agents will expand into a self-replicating factory using the regolith gathered from the surface of the planet. NASA is currently investing in space exploration missions that consider using the resources on the surface of other planets, asteroids or satellites. The challenge of the project is in the implementation of a learning algorithm that allows a large number of different agents to complete simultaneous tasks in order to maximize productivity. The simulation in this work is able to present the coordination of the agents during the construction of the factory as the parameters of the learning algorithm are changed. System performance is measured with a pre-programmed method, using local and difference rewards. The results show the advantage of using a learning algorithm to better build the robotic factory.

Engineering Design for Robot Aesthetics

This paper presents a show case of aesthetic robot design considering technical function constraints and using engineering performance analysis method. First a project goal was determined based on a robot aesthetic analysis. Decision matrices were used to evaluate the aesthetic satisfaction in both component and assembly levels of the design while the scores were assigned subjectively through panel discussion. 3D printing technique was used to get the physical models for rapid verification of the design and to facilitate the design evolution. Examples are given for robot component design as well as the overall evaluation of the robot aesthetics.

A Hybrid Statistical Method for Accurate Prediction of Supplier Delivery Times of Aircraft Engine Parts

Aircraft engine assembly operations require thousands of parts provided by several geographically distributed suppliers. A majority of the operation steps are sequential, necessitating the availability of all the parts at appropriate times for these steps to be completed successfully. Thus, being able to accurately predict the availabilities of parts based on supplier deliveries is critical to minimizing the delays in meeting the customer demands. However, such accurate prediction is challenging due to the large lead times of these parts, limited knowledge of supplier capacities and capabilities, macroeconomic trends affecting material procurement and transportation times, and unreliable delivery date estimates provided by the suppliers themselves. We address these challenges by developing a statistical method that learns a hybrid stepwise regression — generalized multivariate gamma distribution model from historical transactional data on closed part purchase orders and is able to infer part delivery dates sufficiently before the supplier-promised delivery dates for open purchase orders. The hybrid form of the model makes it robust to data quality and short-term temporal effects as well as biased toward overestimating rather than underestimating the part delivery dates. Test results on real-world purchase orders demonstrate effective performance with low prediction errors and constantly high ratios of true positive to false positive predictions.

A SysML-Based Modeling Language for Mechatronic System Architecture

Model Based Systems Engineering (MBSE) is the mainstream methodology for the design of complex mechatronic systems. It emphasizes the application of the system architecture, which highly depends on a formalized modeling language. However, such modeling language is less researched in previous studies. This paper proposes a general modeling language for representing the system architecture, aiming for representing function, physical effect, geometric information and control behavior which the system should satisfy. It facilitates the communication of designers from different technological domains and supports a series of applications such as automatic reasoning, system simulation, etc. The language is illustrated and verified with a practical mechatronic device finally.

Designing a Fast Adaptive Clustering Approach for Traffic Wave Simulation

Traffic wave, also known as stop wave or traffic shockwave, is travelling disturbance in the distribution of vehicles on the highways. In this paper, we attempt to study this problem using a simulation approach. Largely inspired by an interesting observation from ant chain movement, we explore how such a vivid pattern can be mathematically modeled and whether the similar way of behavior is helpful for dealing traffic wave issue in our highway systems. Therefore, a decentralized fast-adaptive clustering approach is proposed jointly with considerations for traffic optimization. To validate the proposed approach and to better understand its mechanism in lifting traffic flow, simulation study is carried out using real-world traffic data. Results have revealed the applicability and effectiveness of the proposed approach and have also indicated that both road configuration and traffic demand affect the effectiveness of the proposed model.

A Product Feature Inference Model for Mining Implicit Customer Preferences Within Large Scale Social Media Networks

The acquisition and mining of product feature data from online sources such as customer review websites and large scale social media networks is an emerging area of research. In many existing design methodologies that acquire product feature preferences form online sources, the underlying assumption is that product features expressed by customers are explicitly stated and readily observable to be mined using product feature extraction tools. In many scenarios however, product feature preferences expressed by customers are implicit in nature and do not directly map to engineering design targets. For example, a customer may implicitly state “wow I have to squint to read this on the screen”, when the explicit product feature may be a larger screen. The authors of this work propose an inference model that automatically assigns the most probable explicit product feature desired by a customer, given an implicit preference expressed. The algorithm iteratively refines its inference model by presenting a hypothesis and using ground truth data, determining its statistical validity. A case study involving smartphone product features expressed through Twitter networks is presented to demonstrate the effectiveness of the proposed methodology.

Exploring Requirement Change Propagation Through the Physical and Functional Domain

Prior research performed by Morkos [1], culminated in the automated requirement change propagation prediction (ARCPP) tool which utilized natural language data in requirements to predict change propagation throughout a requirements document as a result of an initiating requirement change. Whereas the prior research proved requirements can be used to predict change propagation, the purpose of this case study is to understand why. Specifically, what parts of a requirement affect its ability to predict change propagation? This is performed by addressing two key research questions: (1) Is the requirement review depth affected by the number of relators selected to relate requirements and (2) What elements of a requirement are responsible for instigating change propagation, the physical (nouns) or functional (verbs) domain? The results of this study assist in understanding whether the physical or functional domain have a greater effect on the instigation of change propagation. The results indicated that the review depth, an indicator of the performance of the ARCPP tool, is not affected by the number of relators, but rather by the ability of relators in relating the propagating relationships. Further, nouns are found to be more contributing to predicting change propagation in requirements. Therefore, the physical domain is more effective in predicting requirement change propagation than the functional domain.