Identifying Failure Modes and Effects Through Design for Assembly Analysis

2018 ◽  
Author(s):  
Phyo Htet Hein ◽  
Nate Voris ◽  
Jiaying Dai ◽  
Beshoy W. Morkos
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Estimation Method ◽  
Time Estimation ◽  
Handling Time ◽  
Insertion Time ◽  
Design For Assembly ◽  
Assembly Time ◽  
Time Required ◽  
The Ideal

Design for Assembly (DFA) time estimation method developed by G. Boothroyd and P. Dewhurst allows for estimating the assembly time of artifacts based on analysis of component features using handling and insertion tables by an assembler, who is assumed to assemble the artifact one-part-at-a-time. Using the tables, each component is assigned an assembly time which is based on the time required for the assembler to manipulate (handling time) and the time required for it to interface with the rest of the components (insertion time). Using this assembly time and the ideal assembly time (i.e. the absolute time it takes to assemble the artifact, assuming each component takes the ideal time of three seconds to handle and insert), this method allows to calculate the efficiency of a design’s assembly process. Another tool occasionally used in Design for Manufacturing (DFM) is Failure Modes and Effects Analysis (FMEA). FMEA is used to evaluate and document failure modes and their impact on system performance. Each failure mode is ranked based on its severity, occurrence, and detectability scores, and corrective actions that can be taken to control risk items. FMEA scores of components can address the manufacturing operations and how much effort should be put into each specific component. In this paper, the authors attempt to answer the following two research questions (RQs) to determine the relationships between FMEA scores and the DFA assembly time to investigate if part failure’s severity, occurrence, and detectability can be estimated if handling time and insertion time are known. RQ (1): Can DFA metrics (handling time and insertion time) be utilized to estimate Failure Mode and Effects scores (severity, occurrence, and detectability)? RQ (2): How does each response metric relate to predictor metrics (positive, negative, or no relationship)? This is accomplished by performing Boothroyd and Dewhurst’s DFA time estimation and FMEA on select set of simple products. Since DFA metrics are based on combination of designer’s subjectivity and part’s geometric specifications and FMEA scores are based only on designer’s subjectivity, this paper attempts to estimate part failure severity, occurrence, and detectability less subjectively by using the handling time and insertion time. This will also allow for earlier and faster acquisition of potential part failure information for use in design and manufacturing processes.

Reasoning: Source of Variability in the Boothroyd and Dewhurst Assembly Time Estimation Method

2012 ◽  
Author(s):  
Essam Namouz ◽  
Joshua D. Summers ◽  
Gregory M. Mocko
Keyword(s):  
Engineering Students ◽  
Large Range ◽  
Estimation Method ◽  
Time Estimation ◽  
Handling Time ◽  
Time Estimate ◽  
Time Analysis ◽  
Assembly Time ◽  
Time Estimates ◽  
Initial Results

This paper evaluates the effect of making a subjective decision in a design for assembly time analysis. An example is found in the first set of questions for estimating handling time of a part the user chose “parts are easy to grasp and manipulate” as opposed to “parts present handling difficulties”. The subjectivity is explored through a study of assembly time estimates generated by a class of mechanical engineering students in the time analysis of a clicker pen based on the Boothroyd and Dewhurst estimation method. The assembly times calculated by the class ranged from a minimum of 23.64 seconds to a maximum of 44.89 seconds (range of 21.25 seconds). This large range in results serves as motivation in determining the effect that answering a subjective decision has on the resulting assembly time estimate. Initial results indicate that not answering the first level of subjective questions will result in assembly time estimate within 15% of the time had the subjective question been answered. The probability density plots of the time estimates also indicates that 63% of the time, the estimated assembly time without making the subjective decision will fall within the normal distribution had the subjective decision been made. This provides evidence that there is an opportunity to reduce the amount of subjective questions that a user must answer to estimate the assembly time of a product.

scholarly journals Quality Management System in Educational Institutions: Integration of FMEA and PROMETHEE II

2020 ◽  
pp. 1-10
Author(s):  
Renata Maciel de Melo ◽  
Everton Ramos dos Santos ◽  
Maria Helena Lasserre Ferreira ◽  
Luciano Pereira da Silva Santos
Keyword(s):  
Public Schools ◽  
Quality Management ◽  
Failure Mode ◽  
Management System ◽  
Failure Modes ◽  
Quality Management System ◽  
Structured Interview ◽  
Educational Institutions ◽  
Promethee Ii ◽  
Time Required

Competitiveness is increasingly rooted in organizations. Therefore, the pursuit of excellence in the provision of services has been a challenge for those wishing to occupy prominent positions. This reality is usual in public schools that make decisive decisions in the face of constraints, as resources are limited and need to be well managed. Quality management has been a strategy adopted by many educational institutions in the pursuit of process improvement and has inspired many organizations in thisregard. This work intends to be an opportunity for schools experiencing difficulties in implementing and maintaining a Quality Management System (QMS) based on ISO 21001:2018. For this, we propose amodel, which aimed to integrate the Failure Mode Effects Analysis (FMEA) of the process and the PROMETHEE II method and the adoption of two new criteria (Difficulty for failure mode resolution and Time required for fault mode to be “solved”). The model was composed of alternatives that represent the potential failure modes of Traditional FMEA, which were raised in the literature and through a semi-structured interview with the decisionmaker.

Design of Experiments to Support Automated Assembly Planning

2016 ◽  
Author(s):  
Yue Liu ◽  
Weifeng Huang ◽  
Nima Rafibakhsh ◽  
Matthew I. Campbell ◽  
Christopher Hoyle
Keyword(s):  
Experimental Data ◽  
Design Of Experiments ◽  
Prediction Model ◽  
Estimation Method ◽  
Time Estimation ◽  
Quadratic Model ◽  
Assembly Planning ◽  
Estimation Model ◽  
Assembly Time ◽  
Lawn Mower

Assembly time estimation is a key factor in evaluating the performance of the assembly process. The overall goal of this study is to develop an efficient assembly time estimation method by generating the prediction model from an experimental design. This paper proposes a way to divide an assembly operation into four actions which consist of a) part movement, b) part installation, c) secure operations, and d) subassembly rotations. The focus of this paper is to design a time estimation model for the secure operation. To model secure times, a design of experiments is applied to collect experimental data based on the physical assembly experiments performed on products that are representative of common assembly processes. The Box-Behnken design (BBD) is an experiment design to support response surface methodology to interpret and estimate a prediction model for the securing operations. The goal is to use a quadratic model, which contains squared terms and variable interactions, to study the effects of different engineering parameters of securing time. The experiment is focused on individual-operator assembly operations. Various participants perform the experiment on representative product types, including a chainsaw, a lawn mower engine, and an airplane seat. In order to optimize the assembly time with different influence factors, mathematical models were estimated by applying the stepwise regression method in MATLAB. The second-order equations representing the securing time are expressed as functions with six input parameters. The models are trained by using all combinations of required data by the BBD method and predict the hold back data within a 95% confidence interval. Overall, the results indicate that the predicted value found was in good agreement with experimental data, with an Adjusted R-Squared value of 0.769 for estimated securing time. This study also shows that the BBD could be efficiently applied for the assembly time modeling, and provides an economical way to build an assembly time model with a minimum numbers of experiments.

Reasoning: Installation Process Step Instructions as an Automated Assembly Time Estimation Tool

2012 ◽  
Author(s):  
Michael Miller ◽  
David Griese ◽  
Matthew Peterson ◽  
Joshua D. Summers ◽  
Gregory M. Mocko
Keyword(s):  
Life Cycle ◽  
Mechanical Design ◽  
Estimation Method ◽  
Time Estimation ◽  
Automated Assembly ◽  
Process Step ◽  
Assembly Time ◽  
Production Phase ◽  
Require Information

Assembly time estimation is an important aspect of mechanical design and is important for many users throughout the life-cycle of a product. Many of the current assembly time estimation tools require information which is not available until the product is in the production phase. Furthermore, these tools often require subjective inputs which limit the degree of automation provided by the method. The assembly of a vehicle depends on information about the product and information describing the process. The research presented in this paper explains the development and testing of an assembly time estimation method that uses process language as the input for the analysis.

Failure Analysis of Rapid Prototyped Tooling in Sheet Metal Forming—Cylindrical Cup Drawing

2005 ◽  
Vol 127 (1) ◽  
pp. 126-137 ◽  
Author(s):  
Y. Park ◽  
J. S. Colton
Keyword(s):  
Failure Analysis ◽  
Failure Mode ◽  
Failure Modes ◽  
Estimation Method ◽  
Statistical Design ◽  
Statistical Design Of Experiments ◽  
Mode Estimation ◽  
Cylindrical Cup ◽  
Drawing Dies ◽  
Cup Drawing

This paper presents a failure analysis of cylindrical cup drawing dies machined from a polymer composite rapid tooling material. Cup drawing is a complicated process, involving both bending and stretching. In this study, possible die failure modes are identified experimentally. Finite element analyses (FEA) are performed to obtain the stress–strain responses, which are used to determine the dominant failure mode. As cup drawing involves interactions among process parameters, the statistical design of experiments is employed to perform a parametric study. The resulting die failure mode estimation method is verified through experiments.

Multi criteria FMECA for coal-fired thermal power plants using COPRAS-G

2014 ◽  
Vol 31 (5) ◽  
pp. 601-614 ◽  
Author(s):  
Debasis Das Adhikary ◽  
Goutam Kumar Bose ◽  
Dipankar Bose ◽  
Souren Mitra
Keyword(s):  
Failure Mode ◽  
Power Plants ◽  
Failure Modes ◽  
Thermal Power ◽  
Uncertain Data ◽  
Estimation Method ◽  
Straight Tube ◽  
Thermal Power Plants ◽  
Content Type ◽  
Criticality Analysis

Purpose – The purpose of this paper is to present a multi criterion failure mode effect and criticality analysis for coal-fired thermal power plants using uncertain data as well as substituting the traditional risk priority number estimation method. Design/methodology/approach – Grey-complex proportional assessment (COPRAS-G) method, a multi criteria decision making tool is applied to evaluate the criticalities of the failure modes (alternatives). In this model the criteria (criticality factor) against each alternative are expressed in grey number instead of crisp values. Findings – Rupture failure of the straight tube of economizer (ECO) due to erosion is the highest critical failure mode whereas rupture failure of the stub of ECO due to welding defect is the lowest critical failure mode. Originality/value – This paper incorporates human and environmental factors as additional factors which also influence the failure modes significantly. The COPRAS-G method is modified according this problem. Uncertainty in the scoring of criticality factors against each failure mode by various maintenance personnel is expressed in grey numbers.

Combine Nanoprobing Technique with Difference Analysis to Identify Nonvisual Failures

2006 ◽  
Author(s):  
Cha-Ming Shen ◽  
Tsan-Cheng Chuang ◽  
Jie-Fei Chang ◽  
Jin-Hong Chou
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Electrical Characteristic ◽  
The Novel ◽  
Difference Analysis ◽  
Original Idea ◽  
Deductive Method ◽  
Complete Measurement

Abstract This paper presents a novel deductive methodology, which is accomplished by applying difference analysis to nano-probing technique. In order to prove the novel methodology, the specimens with 90nm process and soft failures were chosen for the experiment. The objective is to overcome the difficulty in detecting non-visual, erratic, and complex failure modes. And the original idea of this deductive method is based on the complete measurement of electrical characteristic by nano-probing and difference analysis. The capability to distinguish erratic and invisible defect was proven, even when the compound and complicated failure mode resulted in a puzzling characteristic.

Case Study and Fault Modeling for Wrong Redundancy Evaluation on DRAM Devices

2007 ◽  
Author(s):  
Martin Versen ◽  
Dorina Diaconescu ◽  
Jerome Touzel
Keyword(s):  
Failure Mode ◽  
Failure Modes ◽  
Fault Modeling ◽  
Mode Analysis ◽  
Root Cause ◽  
Failure Mode Analysis

Abstract The characterization of failure modes of DRAM is often straight forward if array related hard failures with specific addresses for localization are concerned. The paper presents a case study of a bitline oriented failure mode connected to a redundancy evaluation in the DRAM periphery. The failure mode analysis and fault modeling focus both on the root-cause and on the test aspects of the problem.

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