MECH DFMA
Name: Joe Torres
Learning Gate: 2g
Cover Sheet
1. Discuss the topics addressed in this assignment in the space provided on the cover sheet. Discuss how
these topics may be applied to design, analysis, or evaluation of machine components. Provide “real
world” examples where appropriate.
Write a short critique of the paper (~1-2 pages).
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•
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Explain what you agree with and disagree with as presented in the paper.
Based on this discussion on subjectivity/objectivity, how does this impact your earlier discussion and
evaluation of the Boothroyd and Dewhurst tool.
How would one use these two different methods?
2. Were you able to complete this assignment?
Yes
No
3. What was the most difficult concept to grasp?
The Boothroyd and Dewhurst tool.
4. Did this assignment contribute to your understanding of the material?
Yes
No
5. What was the most beneficial exercise and why?
Learning all about the comparisons of the Boothroyd and Dewhurst Tool and the DFA Connectivity
Complexity Metrics Method.
6. How long did you spend on this assignment?
0-1 hr.
1-2 hr.
2-3 hr.
3-4 hr.
4-5 hr.
5-10 hr.
10-15 hr.
>15 hr.
Learning Gate 2g
After reading the article about The Boothroyd and Dewhurst tool and The Mathieson-Summers
subjectivity, I agree with multiple things and disagree with one point. Both methods are similar but are
different in their own way. The Boothroyd and Dewhurst tool depends on the user specifies information used
to apply part count minimization rules and different information used to determine the assembly time of each
part. The Mathieson-Summers subjectivity predicts assembly time using only the topological connections
between parts within assemblies. The specified architecture is then represented in bi-partite graphs and the
connective complexity of the architecture is calculated. In the end, both are achieving the same goal, it is just
done in a certain way for each program. Now, after reading the article, I agree that answering these questions
can be tedious and time consuming while still yielding inconsistent results because sixteen of the forty-nine
queries are based on subjective information or the designer’s opinion. This means that one third (33%) of the
total analysis is based on subjective information which is all necessary for it. On Table 1, I like that they had
handling difficulties under the subjective question category. But was surprised that they had the insertion as a
subjective, because to me I feel like more of the insertion should be objective but that is just me so, I will
disagree with it for now, but my opinion could change after seeing this table. Also, when I read the
comparison of the DFMA Software to the and the Connectivity DFA, I agree with it because of what was shown
throughout the report, and it was fascinating how much the software can benefit the designer more than the
Connectivity DFA.
After reading the article, my opinion has changed because of what I saw on Table 1 when it showed all
the number of questions, and I was honestly shocked that not even half of the insertion code is not objective.
But half of the handling code is objective which is also a surprise because I saw it as subjective entirely and not
have any objective. All this time in past modules I carried that mindset but as I see in the report, it is not true. I
can see it takes all the parts, approximates the time required to complete the analysis, predicted assembly
times for each product, and the amounts and types of information required by the user to complete the
analysis. The tool is still effective, it is the way I interpreted the table with the subjectivity and objectivity. My
interpretation changed so I have a new perspective on how to use the table and get better at using the tool.
Using the Boothroyd and Dewhurst Tool, we can figure out two main sections of the analysis which is,
determining the theoretical minimum number of parts and determining assembly times and costs. The
theoretical minimum number of parts is used to identify parts that can be eliminated from the assembly. The
theoretical minimum number of parts is determined first by answering three questions. Does the part move
relative to the other parts during the operation of the product? Does the material of the part have to be
different from the other parts within the assembly? Does the part have to be separated so that other parts can
be assembled or disassembled? This tool can help eliminate some extra parts that do not need to be there. For
example, Table 2 shows the DFMA requires extensive amounts and types of user imputed information which
slows down the analysis time and reduces its repeatability, consistency, and accuracy. It does provide the user
with validated assembly times and eleven areas to focus redesign efforts both of which are critical for a DFA
method to be effective. If you want consistency, accuracy, and feel subjectivity, this tool is the most effective
of both.
Using the DFA Connectivity Complexity Metrics Method, we can calculate the complexity of the part
connections within an assembly, mapping the results to previously predicted assembly times based on the
Boothroyd Dewhurst DFA tables. Thus, the Mathieson-Summers connective-complexity method is based on
the same empirical data on which the Boothroyd Dewhurst method is based. The key difference is not the
source of historical trends, but the usability of the method from the perspective of the engineer that is
running the design for assembly analysis. For example, Table 5 it shows it has more unknowns with the
connective-complexity method. It requires moderate amounts of time to complete the analysis and only
requires the user to provide input based on a few different types of objective questions. This should make the
analysis repeatable and consistent between users. The predicted assembly times that the method provides
have not been fully validated so they cannot be accepted as correct. This method currently does not provide
the user with features to aid in redesigning the part to improve assembly. If you want to crunch the numbers
and make sure they are objectively right, this tool is a good one as well. It all depends on the person’s
preference.