An Association Rule Approach for Identifying Physical System-User Interactions and Potential Human Errors Using a Design Repository

2019 ◽  
Author(s):  
Nicolás F. Soria Zurita ◽  
Melissa Anne Tensa ◽  
Vincenzo Ferrero ◽  
Robert B. Stone ◽  
Bryony DuPont ◽  
...  
Keyword(s):  
Data Mining ◽  
Design Process ◽  
Human Error ◽  
Failure Modes ◽  
Functional Model ◽  
Design Stage ◽  
User Interactions ◽  
Early Design ◽  
Design Information ◽  
Rule Approach

Abstract During the design process, designers must satisfy customer needs while adequately developing engineering objectives. Among these engineering objectives, human considerations such as user interactions, safety, and comfort are indispensable during the design process. Nevertheless, traditional design engineering methodologies have significant limitations incorporating and understanding physical user interactions during early design phases. For example, Human Factors methods use checklists and guidelines applied to virtual or physical prototypes at later design stages to evaluate the concept. As a result, designers struggle to identify design deficiencies and potential failure modes caused by user-system interactions without relying on the use of detailed and costly prototypes. The Function-Human Error Design Method (FHEDM) is a novel approach to assess physical interactions during the early design stage using a functional basis approach. By applying FHEDM, designers can identify user interactions required to complete the functions of the system and to distinguish failure modes associated with such interactions, by establishing user-system associations using the information of the functional model. In this paper, we explore the use of data mining techniques to develop relationships between component, functions, flows and user interactions. We extract design information about components, functions, flows, and user interactions from a set of distinct coffee makers found in the Design Repository to build associations rules. Later, using a functional model of an electric kettle, we compared the functions, flows, and user interactions associations generated from data mining against the associations created by the authors, using the FHEDM. The results show notable similarities between the associations built from data mining and the FHEDM. We are suggesting that design information from a rich dataset can be used to extract association rules between functions, flows, components, and user interactions. This work will contribute to the design community by automating the identification of user interactions from a functional model.

Identification of Human–System Interaction Errors During Early Design Stages Using a Functional Basis Framework

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Nicolás F. Soria Zurita ◽  
Robert B. Stone ◽  
H. Onan Demirel ◽  
Irem Y. Tumer
Keyword(s):  
Design Process ◽  
Human Error ◽  
Failure Modes ◽  
Composite Function ◽  
Human System ◽  
Early Design ◽  
Functional Basis ◽  
Potential Failure ◽  
Early Design Stages ◽  
Criticality Analysis

Abstract Engineers have developed different design methodologies capable of identifying failure modes of engineering systems. The most common methods used in industry are failure modes and effects analysis, and failure modes effects and criticality analysis. Nevertheless, such methodologies have a significant limitation regarding incorporating the final user in the analysis and are not suited to identifying potential failure modes caused by physical human–system interactions. Engineering methods usually have a lack of sufficient attention to human–system interactions during the early design stages, even though introducing human factors principles is recognized as an essential analysis during the design process. As a result, designers rely on developing detailed and expensive physical or virtual prototypes to evaluate physical human–system interactions and identify potential failure modes caused by such interactions incorporating design modifications after a prototype is developed can be time-consuming, costly, and if significant changes are needed, the entire prototype requires to be constructed again. Identifying system–user interactions and possible failure modes associated with such interactions before developing a prototype can significantly improve the design process. In previous work, the authors introduced the function–human error design method (FHEDM), a tool capable of distinguishing possible human–system interaction failure modes using a functional basis framework. In this work, we examined the implementation of FHEDM within 148 products extracted from the design repository. The results are grouped in the composite function–user interaction error (FUIE) matrix, which can be used as a preliminary design database presenting information regarding the possible human error present in function-flow combinations.

Identification of Human Errors During Early Design Stage Functional Failure Analysis

2018 ◽  
Author(s):  
Lukman Irshad ◽  
Salman Ahmed ◽  
Onan Demirel ◽  
Irem Y. Tumer
Keyword(s):  
Failure Analysis ◽  
Human Error ◽  
System Level ◽  
Design Stage ◽  
Human Factors Engineering ◽  
Human Errors ◽  
Functional Failure ◽  
Early Design ◽  
Early Design Stage ◽  
Early Design Stages

Detection of potential failures and human error and their propagation over time at an early design stage will help prevent system failures and adverse accidents. Hence, there is a need for a failure analysis technique that will assess potential functional/component failures, human errors, and how they propagate to affect the system overall. Prior work has introduced FFIP (Functional Failure Identification and Propagation), which considers both human error and mechanical failures and their propagation at a system level at early design stages. However, it fails to consider the specific human actions (expected or unexpected) that contributed towards the human error. In this paper, we propose a method to expand FFIP to include human action/error propagation during failure analysis so a designer can address the human errors using human factors engineering principals at early design stages. To explore the capabilities of the proposed method, it is applied to a hold-up tank example and the results are coupled with Digital Human Modeling to demonstrate how designers can use these tools to make better design decisions before any design commitments are made.

Computational Functional Failure Analysis to Identify Human Errors During Early Design Stages

2019 ◽  
Vol 19 (3) ◽  
Author(s):  
Lukman Irshad ◽  
Salman Ahmed ◽  
H. Onan Demirel ◽  
Irem Y. Tumer
Keyword(s):  
Failure Analysis ◽  
Human Error ◽  
Human Action ◽  
System Level ◽  
Design Stage ◽  
Human Factors Engineering ◽  
Human Errors ◽  
Functional Failure ◽  
Early Design ◽  
Early Design Stages

Detection of potential failures and human error and their propagation over time at an early design stage will help prevent system failures and adverse accidents. Hence, there is a need for a failure analysis technique that will assess potential functional/component failures, human errors, and how they propagate to affect the system overall. Prior work has introduced functional failure identification and propagation (FFIP), which considers both human error and mechanical failures and their propagation at a system level at early design stages. However, it fails to consider the specific human actions (expected or unexpected) that contributed toward the human error. In this paper, we propose a method to expand FFIP to include human action/error propagation during failure analysis so a designer can address the human errors using human factors engineering principals at early design stages. The capabilities of the proposed method is presented via a hold-up tank example, and the results are coupled with digital human modeling to demonstrate how designers can use these tools to make better design decisions before any design commitments are made.

Linking Creativity Measurements to Product Market Favorability: A Data-Mining Approach

2017 ◽  
Author(s):  
Christian E. Lopez B. ◽  
Xuan Zheng ◽  
Scarlett R. Miller
Keyword(s):  
Data Mining ◽  
Early Stage ◽  
Product Market ◽  
New Product ◽  
Design Stage ◽  
Future Market ◽  
Early Design ◽  
Early Design Stage ◽  
Data Mining Approach ◽  
The Future

While creative ideas can lead to market success and payoff, they are also associated with high risks and uncertainties. One way to reduce these uncertainties is to provide decision makers with valuable information about the innovative potential and future success of an idea. Even though several metrics have been proposed in the literature to evaluate the creativity of early design-stage ideas, these metrics do not provide information about the future product success or market favorability of new product ideas. Hence, existing metrics fail to link the creativity of early-stage ideas to their future market favorability. In order to bridge this gap, the current work proposes a new metric to estimate early design-stage ideas’ favorability and analyzes its relationship with current creativity metrics. A data-mining driven method to assess the future favorability of new product ideas using customers’ reviews of current market products that shared similar features with the new ideas of interest is presented. The results suggest that the new product idea favorability is positively correlated with relative creativity metrics and existing product market favorability ratings. This method can be used to help designers gain a better insight into the creativity and market favorability potential of new product ideas in early design-stages via a systematic approach; hence, helping reduce the risks and uncertainties associated with early-phase ideas during the screening and selecting process.

Information generation during design: Information importance and design effort

2005 ◽  
Vol 19 (1) ◽  
pp. 19-32 ◽  
Author(s):  
A.J. DENTSORAS
Keyword(s):  
Theoretical Analysis ◽  
Design Process ◽  
Quantitative Relationship ◽  
Design Knowledge ◽  
Belt Conveyor ◽  
Early Design ◽  
Design Information ◽  
Early Design Stages ◽  
Information Generation ◽  
The Relationship

The present paper studies the process of information generation during design and focuses on the relationship between the information importance and the required effort for its generation. Multiple associative relationships among design entities (handled as design descriptors) are used to represent the design knowledge. The characteristics of the dependent and the primary descriptors are examined and their distinct roles in the design process are discussed. Term definitions concerning the information importance and the design effort are also introduced. The descriptors are used to form a matrix. A number of operations on this matrix results in its transformation, with the final matrix reflecting the quantitative relationship between the information importance and the design effort. From the aforementioned matrix, a unique sorted list for the primary design descriptors is produced. Following this list during descriptor instantiation ensures the production of design information of maximum importance with the least effort in the early design stages. The design of a belt conveyor is used as a basis for a better understanding of the theoretical analysis and for a demonstration of the use of the suggested descriptor list.

scholarly journals Integration of Sustainability Into Early Design Through Working Knowledge Model and Visual Tools

2009 ◽  
Author(s):  
Srikanth Devanathan ◽  
Pranav Koushik ◽  
Fu Zhao ◽  
Karthik Ramani
Keyword(s):  
Design Process ◽  
Environmental Sustainability ◽  
Modern Society ◽  
Design Stage ◽  
Knowledge Model ◽  
Early Design ◽  
Environmentally Sustainable ◽  
Early Design Stage ◽  
And Performance

The issue of environmental sustainability, which is unprecedented in both magnitude and complexity, presents one of the biggest challenges faced by modern society. Engineers, including mechanical engineers, can make significant contribution to the development of solutions to this problem by designing products and processes that are more environmentally sustainable. It is critical that engineers take a paradigm shift of product design i.e. from cost and performance centered to balance of economic, environmental, and societal consideration. Although there have been quite a few design for environment (DfE, or ecodesign) tools developed, so far these tools have only achieved limited industrial penetration: they are either too qualitative/subjective to be used by designers with limited experiences, or too quantitative, costly and time consuming and thus cannot be used during the design process specially during the early design stage. This paper develops a novel, semi-quantitative ecodesign tool that targets specially on early design process. The new tool is a combination of environmental life cycle assessment, working knowledge model, and visual tools such as QFD, functional-component matrix, and Pugh chart. Redesign of staplers is selected as a case study to demonstrate the use of the proposed tool. Efforts are on going to confirm that the new design generated using this new tool does have improved environmental performance.

Application of Simulators and Simulation Tools for the Functional Design of Mechatronic Systems

2010 ◽  
Vol 166-167 ◽  
pp. 1-14 ◽  
Author(s):  
Dieter Schramm ◽  
Wildan Lalo ◽  
Michael Unterreiner
Keyword(s):  
Design Process ◽  
Cost Reduction ◽  
Test Person ◽  
Design Stage ◽  
Functional Design ◽  
Mechatronic Systems ◽  
Early Design ◽  
Early Design Stage ◽  
Simulation Tools ◽  
Real Product

This paper considers the application of simulators or demonstrators in the development of mechatronic products. It is shown at what step of the mechatronic design process a simulator or demonstrator can be used to significantly improve a products quality and thus identify possible errors and provide potential workarounds. Cost reduction is achieved by the use of simulators or demonstrators in the early design stage and less real product tests have to be carried out which also could be hazardous for the test person.

Resilience Allocation for Early Stage Design of Complex Engineered Systems

2016 ◽  
Vol 138 (9) ◽  
Author(s):  
Nita Yodo ◽  
Pingfeng Wang
Keyword(s):  
Distribution System ◽  
Failure Modes ◽  
Early Stage ◽  
Design Stage ◽  
Early Design ◽  
Early Design Stage ◽  
Complex Engineered Systems ◽  
Operating Environments ◽  
Early Stage Design ◽  
Critical Components

The continuous pursuits of developing a better, safer, and more sustainable system have pushed systems to grow in complexity. As complexity increases, challenges consequently arise for system designers in the early design stage to take account of all potential failure modes in order to avoid future catastrophic failures. This paper presents a resilience allocation framework for resilience analysis in the early design stage of complex engineering systems. Resilience engineering is a proactive engineering discipline that focuses on ensuring the performance success of a system by adapting to changes and recovering from failures under uncertain operating environments. Utilizing the Bayesian network (BN) approach, the resilience of a system could be analyzed and measured quantitatively in a probabilistic manner. In order to ensure that the resilience of a complex system satisfies the target resilience level, it is essential to identify critical components that play a key role in shaping the top-level system resilience. Through proper allocation of resilience attributes to these critical components, not only target could resilience requirements be fulfilled, global cascading catastrophic failure effects could also be minimized. An electrical distribution system case study was used to demonstrate the developed approach, which can also be used as a fundamental methodology to quantitatively evaluate resilience of engineered complex systems.

Using Automated Use Case Generation for Early Design Stage Functional Failure and Human Error Analysis

2019 ◽  
Author(s):  
Lukman Irshad ◽  
H. Onan Demirel ◽  
Irem Y. Tumer
Keyword(s):  
Human Error ◽  
Large Scale ◽  
Design Stage ◽  
Test Case ◽  
Use Case ◽  
Human Errors ◽  
Functional Failure ◽  
Early Design ◽  
Early Design Stage ◽  
Complex Engineered Systems

Abstract Human errors and poor ergonomics are attributed to a majority of large-scale accidents and malfunctions in complex engineered systems. Human Error and Functional Failure Reasoning (HEFFR) is a framework developed to assess potential functional failures, human errors, and their propagation paths during early design stages so that more reliable systems with improved performance and safety can be designed. In order to perform a comprehensive analysis using this framework, a wide array of potential failure scenarios need to be tested. Coming up with such use cases that can cover a majority of faults can be challenging or even impossible for a single engineer or a team of engineers. In the field of software engineering, automated test case generation techniques have been widely used for software testing. This research explores these methods to create a use case generation technique that covers both component-related and human-related fault scenarios. The proposed technique is a time based simulation that employs a modified Depth First Search (DFS) algorithm to simulate events as the event propagation is analyzed using HEFFR at each timestep. This approach is applied to a hold-up tank design problem and the results are analyzed to explore the capabilities and limitations.

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