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.

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.

Ontological Knowledge Representation and Inference Rules for MTM Decision Support System

2014 ◽  
Author(s):  
Rahul Renu ◽  
Gregory Mocko
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Knowledge Representation ◽  
Support System ◽  
Time Estimation ◽  
Time Measurement ◽  
Inference Rules ◽  
Assembly Time ◽  
Time Estimates ◽  
Definition Of

The objective of the research presented is to develop and implement an ontological knowledge representation for Methods-Time Measurement assembly time estimation process. The knowledge representation is used to drive a decision support system that provides the user with intelligent MTM table suggestions based on assembly work instructions. Inference rules are used to map work instructions to MTM tables. An explicit definition of the assembly time estimation domain is required. The contribution of this research, in addition to the decision support system, is an extensible knowledge representation that models work instructions, MTM tables and mapping rules between the two which will enable the establishment of assembly time estimates. Further, the ontology provides an extensible knowledge representation framework for linking time studies and assembly processes.

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.

scholarly journals Aplikasi Mobile untuk Sistem Antrian Praktek Dokter Dilengkapi dengan Analisis Perhitungan Estimasi Waktu Menggunakan Markov Chain dan Algoritma PageRank

2020 ◽  
Vol 5 (3) ◽  
Author(s):  
Cato Chandra ◽  
David Sanjaya ◽  
Julio Narabel ◽  
Nucky Vilano ◽  
Setia Budi
Keyword(s):  
Markov Chain ◽  
Mobile Applications ◽  
Mobile Application ◽  
Rapid Development ◽  
Time Estimation ◽  
Time Estimate ◽  
Pagerank Algorithm ◽  
Final Project ◽  
Time Estimates ◽  
Manual Methods

Along with the increasingly rapid development of technology, especially in the field of computers, ways to overcome the problem of patient queues have been developed. One of them is the use of a mobile application to get a time estimate until a patient gets a turn to consult with a doctor. Many industries still use manual methods to overcome this queue problem. Based on this fact, this final project with title "Mobile Applications for Doctor Examination Queue System Equipped with Analysis of Time Estimation Calculation Using the Markov Chain and PageRank Algorithm" has aims to get time estimates for the patients so that time can be more efficient.

Time Estimation Methods—Do They Influence Prospective Duration Estimates?

Perception ◽  
1993 ◽  
Vol 22 (1) ◽  
pp. 91-101 ◽  
Author(s):  
Dan Zakay
Keyword(s):  
Information Processing ◽  
Positive Relationship ◽  
Negative Relationship ◽  
Estimation Method ◽  
Time Estimation ◽  
Estimation Methods ◽  
Clock Time ◽  
Processing Load ◽  
Attentional Model ◽  
Time Length

The validity of an attentional model of prospective time estimation was tested in three experiments. In the first experiment two variables were manipulated: (1) nontemporal information processing load during the estimated interval, and (2) time estimation method, ie production of time simultaneously with the performance of a second task, or reproduction of time immediately upon termination of a task whose duration has to be measured. As predicted, a positive relationship between produced time length and information processing load demanded by a simultaneous task, and a negative relationship between reproduced time length and information processing load during the estimated interval, were found. The results were replicated in a second experiment in which verbal estimates of time were also measured and the objective duration of the estimated interval was varied. The pattern of results obtained for verbal estimates was similar to that obtained for reproduced ones. The results of a third experiment indicated that produced and reproduced times were positively correlated with clock time. The results are interpreted as supporting an attentional model of prospective time estimation.

Estimation of Truck Traffic Volume from Single Loop Detectors with Lane-to-Lane Speed Correlation

2003 ◽  
Vol 1856 (1) ◽  
pp. 106-117 ◽  
Author(s):  
Jaimyoung Kwon ◽  
Pravin Varaiya ◽  
Alexander Skabardonis
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
The United States ◽  
Truck Traffic ◽  
Single Loop ◽  
Loop Detectors ◽  
Time Estimates ◽  
Surveillance Technology ◽  
Traffic Volumes ◽  
Urban Freeway

An algorithm for real-time estimation of truck traffic in multilane freeways was proposed. The algorithm used data from single loop detectors—the most widely installed surveillance technology for urban freeways in the United States. The algorithm worked for those freeway locations that have a truck-free lane and exhibit high lane-to-lane speed correlation. These conditions are met by most urban freeway locations. The algorithm produced real-time estimates of the truck traffic volumes at the location. It also can be used to produce alternative estimates of the mean effective vehicle length, which can improve speed estimates from single loop detector data. The algorithm was tested with real freeway data and produced estimates of truck traffic volumes with only 5.7% error. It also captured the daily patterns of truck traffic and mean effective vehicle length. Applied to loop data on Interstate 710 near Long Beach, California, during the dockworkers’ lockout October 1 to 9, 2002, the algorithm found a 32% reduction in five-axle truck volume.

Soft Transformer Energization: Ramping Time Estimation Method for Inrush Current Mitigation

2021 ◽  
Author(s):  
Abdulrahman Alassi ◽  
Khaled Ahmed ◽  
Agusti Egea-Alvarez ◽  
Colin Foote
Keyword(s):  
Estimation Method ◽  
Time Estimation ◽  
Inrush Current
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