A Dimensional Engineering Process for Shipbuilding

A Dimensional Engineering Process for Shipbuilding

Mark H. Spicknall* and Ramesh Kumar†

*University of Michigan Transportation Research Institute, Marine Systems Division; and Business Performance Group LLC†Dimensional Control Systems, Inc.

Dimensional engineering practices and tools used in other industries are highlyevolved and broadly applicable to shipbuilding. However, because of relatively smallproduct volume per design and relatively high variability in the dimensional attributesof intermediate products, detailed dimensional engineering of each ship design is noteconomically viable. This paper describes a viable dimensional engineering processfor shipbuilding.

Introduction

DIMENSIONAL management and engineering practices, com-monly referred to as accuracy control in shipbuilding (Yokata1963, Levingston 1980, Storch 1984, IHI 1985), are fairly stan-dard and well established in other industries such as automotiveand aerospace. These practices include statistical process control(SPC) on the shop floor (Shewhart 1939, Grant 1996), and dimen-sional variation simulation analysis and geometric dimensioningand tolerancing (GD&T) (ASME 1994, Drake 1999) in detail/production design. The combination of these practices is oftenreferred to in other industries as a dimensional control system.This paper focuses primarily on dimensional variation simulationanalysis practices, which are most often referred to as dimensionalengineering.

Dimensional engineering practices utilized within other indus-tries are generally applicable to the shipbuilding industry. How-ever, a ship design and production system differs from the systemsthat produce, say, automobiles in ways that will impact dimen-sional engineering practices. For example, very few products aretypically produced per design within the shipbuilding industry, soa ship is most often a semi-custom or custom product. And al-though each ship is made up of a very large number of interme-diate products (IPs), because of the semi-custom nature of shipsthe number of standard intermediate products is small. Therefore,to gain economies of scale, group technology is utilized to thegreatest extent possible. Group technology exploits the similaritiesof common types of intermediate products to gain the productionvolume necessary to justify the creation of intermediate-product-

type-specific process lanes and work centers. But still within eachintermediate product type/group/family there will be some degreeof variability in dimensional attributes.

Given this variability both between and within products, to fullyanalyze the impact of IP dimensional variation on final productform and function would require a substantial dimensional engi-neering effort for each ship design. While such an effort is eco-nomically viable within the automobile industry because manyproducts will be produced from a single design, such a dimen-sional engineering effort is not economically viable for a shipbecause of the few ships typically built per design (Kumar &Spicknall 2000). An alternative approach for applying dimen-sional engineering practices to shipbuilding is as follows:

� Model and simulate a representative sample of intermedi-ate products of each type/group/family and then utilize stan-dard parametric dimension and tolerance optimization resultsdeveloped from those simulations to establish GD&T for allfuture intermediate products within that type/group/family,

� Model and simulate in detail only the portions of the shipthat are both unique and likely to be dimensionally critical.

The remainder of this paper describes this dimensional engineer-ing process in detail using an example of a typical ship’s doublebottom.

Dimensional engineering process for shipbuilding

Figure 1 shows the dimensional engineering process for ship-building. Each step of the process is described below.

1. Select build strategy alternative—The build strategy identi-fies the product structure, including all intermediate products from

Presented at the Ship Production Symposium, Ypsilanti, Michigan, June13–15, 2001.

Journal of Ship Production, Vol. 18, No. 2, May 2002, pp. 105–115

MAY 2002 8756/1417/02/1802-0105$00.47/0 JOURNAL OF SHIP PRODUCTION 105

major assemblies to piece-parts, and their assembly relationships.The build strategy should take into consideration the combinationof product design features and process parameters that meet allprogram requirements and functional product objectives. It shouldbalance major assembly, subassembly, and some detailed buildrequirements against total product requirements. Build strategydevelopment must be coordinated with design and engineering.

When deciding the appropriate build strategies, due attentionmust be paid to issues like:

� Actual versus required production capabilities� Cost� Schedule� Risk

2. Identify dimensionally critical intermediate products and de-termine if a GD&T standard exists for each—As discussed earlier,the low number of ships built per design, the very high totalnumber of intermediate products per ship, and the moderate to low

Fig. 1

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number and generally nonstandard nature of intermediate productsof any given type, make detailed dimensional modeling of everyship design economically nonviable. An alternative approach is toidentify the dimensionally critical intermediate products in thetotal product structure, and utilize existing dimensioning and tol-erance standards if they exist to generate the final GD&T schemefor the intermediate product (Step 5). If such a standard does notalready exist, one must be created via detailed dimensional engi-neering analysis (Step 3). Eventually as these standards are gen-erated and reused, detailed dimensional analysis for a new shipdesign will include only the portions of the product that are bothdimensionally critical and unique.

3. Carry out detailed dimensional engineering analysis for di-mensionally critical IPs for which no standards exist—If a stan-dard GD&T scheme does not exist for the type/group/family of aparticular dimensionally critical intermediate product, it is firstnecessary to define the detailed production processes for this IP.Then critical dimensions are defined and functional design toler-ances are allocated. Functional design tolerances describe in di-mensional terms the allowable limits in build variation that willproduce a product that meets overall requirements for fit andfunction. Functional design tolerances are acquired from the func-tional design/engineering group.

After identifying the critical dimensions and assigning designtolerances, it is now time to apply production variances to the partsand assemblies. These data are acquired from a process perfor-mance standards database, which is continually being updatedfrom shop-floor SPC data. Production variances data might alsocome from probabilistic structural engineering analyses for IPsthat have never been built before and for which there is no statis-tical production data.

The next step is to analytically determine if the product andprocesses as specified by design would meet overall dimensionalbuild objectives.

� Conduct sensitivity analysis to show large contributors tovariation.

Identify and rank the effect critical characteristics will haveon assembly functional requirements.Develop a corrective action for each root cause (i.e., productand/or process).� Apply preliminary KPC’s to identified tolerances based on

level of contribution to overall dimensional build objectives.

This is an iterative process required to achieve the overall dimen-sional build objectives. The general goal is for tolerances to be aswide as possible while still achieving final dimensional build ob-jectives. Build strategy and design/process/tolerance requirementscould possibly be refined in order to achieve a tolerance distribu-tion that minimizes production cost while achieving overall di-mensional build objectives.

4. Assess resources to meet requirements—One of the mostimportant steps in the process is the assessment of the availabilityof human resources and capability of production tools and equip-ment. This process essentially validates that production capabili-ties are sufficient to meet the dimensional requirements estab-lished. If shop floor variance data were used in the dimensionalengineering analysis as described above, presumably the capabil-

ity of production tools and equipment is satisfactory. Does theorganization have the human resources at its disposal to achievethe dimensional objectives? If the required human resources arenot available or the capability of production tools and equipmentis not satisfactory, it is time to reevaluate the design, process,tolerances or the build strategy. Any one of them or a combinationof them might require change, modification, or refinement toachieve overall goals. Use simulation analysis to optimize toler-ances.

5. Finalize GD&T and complete and issue production informa-tion for each dimensionally critical intermediate product (defineand maintain GD&T standards by IP type)—Release level GD&Tis developed using either existing standards or the results of di-mensional analyses. Then deliverable production drawings are de-veloped for the dimensionally critical IPs. Design and productionengineering should identify and maintain the configuration of the3-D GD&T schemes.

6. Produce, measure, and analyze dimensional data to validateGD&T schemes—The most important step in the implementationof a dimensional control system is the validation of the final outputand the analysis of the results. If the final product does not meetthe desired overall dimensional specifications, the root causesmust be investigated. Some of the causes are easily tackled andfixed whereas other might require detailed investigation.

The following activities should be carried out to support fabri-cation, assembly, and erection processes. Critical data should becollected and statistically analyzed for further improvement inproduct quality.

� Design—Does the design perform as intended?� Tools/Fixtures—Are tools repeatable and user friendly?� Process—Does the process appear to be capable?Validation of design

Do parts within specifications build assemblies withinspecification?Do assemblies within specification build a product withinspecification?Do parts out of specification build assemblies withinspecification?

Validation of Tools and FixturesGR&R—gage repeatability and reproducibility

Validation of processCan parts and assemblies within specification be processedin such a way that a completed product can be producedwithin specification? If not . . .� Root cause analysis

Are parts right?Are tools right and repeatable?Are datums valid (paper and physical coordination vali-dation)?What does the data say (data quality validation)?

If the above problems could not be fixed, this warrants revisit-ing the build strategy and redefining the objectives. After validat-ing the final product with the design criteria, the data should bestored in a database for future use and update. These design andproduction data will be used to refine future processes.

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Definitions and examples

Portions of a ship double bottom with some pipe and machineryinstalled will be shown below to provide examples of some of theconcepts discussed in the dimensional engineering process de-scribed.

Build strategy

A build strategy is represented by a physical product structureto the piece-part level, sometimes called an assembly tree. Figure2 shows a portion of a product structure for a typical vessel.

GD&T schemes

Figure 3 shows a plate-to-fixture GD&T scheme. Figure 4shows a plate-to-plate GD&T scheme. Figure 5 shows a stiffener-to-plate and fixture GD&T scheme. Figure 6 shows a stiffenedpanel GD&T scheme.

Critical dimensions

The critical dimension for this stiffened panel IP could be thetransverse distance between the outboard plate edges, the mea-surement of each stiffener face from the edge of the plate (Fig. 7),and the flushness offset between the plates (Fig. 8).

Dimensionally critical IP build/assembly process

There are usually several ways to assemble any given IP. Eachprocess of assembly might have unique consequences for dimen-sional variation. Following are two example alternative assemblyprocesses for one of the stiffened panels in a double bottom.

1. An individual plate is brought to a station (fixture) andaligned to its datum. The next plate is brought to the samefixture and aligned to the joining face of the first plate. Thefirst and second plates are joined together. A third plate isbrought to the same fixture and its side is aligned to thejoining face of the second plate. The third plate is thenwelded to the earlier welded plates. This subassembly is thenmoved to a different station (fixture) where it will be

stiffened. A stiffener fixture is used and is aligned on theassembled plate/station. The stiffener is then placed andaligned to the faces of the stiffener fixture and the plate andthen fit and welded. In a similar fashion, the rest of thestiffeners are fitted and welded to the assembled plates. Fig-ures 9 through 11 show this process.

2. An individual plate is brought on a station (fixture) andaligned to its datum. A stiffener fixture is brought andaligned on the plate/station. The stiffener is then placed andaligned to the faces of stiffener fixture/plates and fitted andwelded. In a similar way another stiffener is brought andfitted and welded to the plate. On different stations, otherindividual plates are similarly stiffened. The first stiffenedplate is then moved to a different station (fixture) where it isaligned to its datum. The second stiffened plate is brought tothe same fixture and aligned to the joining face of the firststiffened plated. The first and second stiffened plates arejoined together and so on. Figures 12 through 14 below showthis process.

Dimensional variation simulation analysis

After assigning the design tolerances and production variances,dimensional variation simulation analysis is carried out. Exampleresults are shown below. The purpose of simulation analysis is todetermine the variation in process output with the intent of evalu-ating the ability of the process to consistently produce assemblieswithin the required specifications. The analysis simulates the as-sembly of a number of identical assemblies. The differences inassembly dimensions arise from the unique combination of vari-ous component and subassembly production variances from simu-lated build to simulated build.

It is these different builds that generate the statistical resultsabout the consistency of the assembly process. The output con-tains all the statistical data of the measurements associated withthe assembly model. The sensitivity analysis shows whether aparticular dimension is within the desired specification limits. Italso shows various other statistical results (e.g., Std. Deviation,Ppk, Nominal, Mean, Median, Range, % Out). Sensitivity andGeoFactor analyses show the percentage contribution of each ofthe design/build tolerances that have affect on that particular di-

Fig. 2 Example of build strategy or physical product structure


mension. If the analysis results meet the build requirements, thefinal GD&T is created and production information is issued afterassessment of the resources.

Following are descriptions of many of the outputs from dimen-sional variation simulation analyses.

Shown in Fig. 15 are the histogram and statistical results fromthe analysis of a particular critical dimension. Figure 16 shows theresults of a sensitivity analysis, and Fig. 17 shows the results of aGeoFactor analysis.

Histogram and associated statistics

A histogram gives the distribution of measurements for a par-ticular critical dimension for the number of builds simulated. Thehighest frequency value is displayed on top of the histogram asMax. 4 Highest Frequency. The Upper Specification Limit andthe Lower Specification Limits are displayed as two lines on thehistogram. The histogram bars outside the Upper and LowerSpecification limits range are shown in red signifying that they areout of range. A histogram is generated for each critical dimensionof the assembly model specified for measurement.

Runs—This is the actual number of Monte Carlo simulationruns or builds that have been performed on the assembly.

Mean—The critical dimension average value of the populationgenerated by the simulation.

Nominal—The nominal value of the measurement is the valuewhen all tolerances associated with the measurement are at theirnominal values.

STD—STD denotes Standard Deviation ( s ), which is a statis-tical measure of variation.

Six sigma (6 s )—It is the Standard Deviation value multipliedby 6. This represents the 99.97% width of the normal curve, whichis equivalent to a Cp of 1.

Pp—Pp is a process performance index, which measures theperformance of the process. This index compares the variation ofthe process to the allowable variation that is set by the specifica-tion limits (USL and LSL). Pp is similar to Process Capabilityindex, Cp . Outside of the context of Statistical Process Control(SPC) there is no difference between Cp and Pp , and only the termCp is used. When sample data are in view, s is estimated by thefamiliar sample standard deviation that is technically denoted as “s.”It is within the context of SPC that the distinction between Cp and Pp

becomes important. The difference is in the estimate of s that is used.In SPC, for Cp , s is estimated by s /d2 , which is the average range ofthe subgroups divided by a scale factor called d2 that is dependent onthe subgroup size. For Pp , s is estimated by “s.” Since the 3DCSanalysis software tool shown does not record measurements in nu-merous small subgroups, but rather one large group, Pp is moreappropriate to use. The Pp index tells us about the capability of aprocess but does not tell us where the process lies within respect to itscenter. When the range between the Upper Design Limit (UDL) andLower Design Limit (LDL) is the same as the 6s spread, the Cp indexwill be 1.0. The Pp index is calculated for a bilateral tolerance,there is no universally accepted way of defining Pp Index for aunilateral tolerance. Controlling factors for Pp are: design speci-fications, standard deviation, and sources of the deviation.

Fig. 3 Example plate-to-fixture GD&T scheme


Fig. 5 Example of stiffener-to-plate and fixture GD&T

Fig. 4 Example plate-to-plate GD&T scheme


Fig. 7 Some critical dimensions of stiffened panel

Fig. 6 Stiffened panel GD&T scheme


Pp k—The Pp k indicator is a Process Performance index thatrepresents how well the process is centered, or how distant themean of the process is compared to its specification limit. The Pp k

index can never be higher than the Pp index value. If the Pp k andPp values are equal, the process is centered. If the Pp k value is 0,the mean of the process is centered about one of its specificationlimits. Controlling factors for Pp k are: design specifications, stan-dard deviation, sources of the deviation, and central tendency ofthe process.

Min—The minimum value of the measurement generated dur-ing a given set of process simulations for that particular measure-ment.

Max—The maximum value of the measurement generated dur-

ing a given set of process simulations for that particular measure-ment.

Range—The range is the difference between the maximumvalue and the minimum value.

LSL—LSL is the Lower Specification Limit number set for thatparticular measurement. This comes from functional engineeringrequirements.

USL—USL is the Upper Specification Limit number set for thatparticular measurement. This comes from functional engineeringrequirements.

Lout—Lout is the actual percentage of measurement valuesfalling below the Lower Specification Limit.

Rout—Rout is the actual percentage of measurement valuesfalling above the upper specification limit.

Fig. 8 More critical dimensions of stiffened panel

Fig. 9 Panel assembly process A Fig. 10 Panel assembly process A


RngLout—RngLout is the actual percentage of statistical mea-surement values that fall between the Lower Specification Limitand -3STD.

RngRout—RngRout is the actual percentage of statistical mea-surement values that fall between the Upper Specification Limitand +3STD.

Sensitivity analysis

Sensitivity or High-Low-Mean (HLM) analysis is executed todetermine the sources of variation that contribute, in terms ofpercentage, to specific measurements/critical dimensions in an as-sembly model. The High-Low-Mean simulation varies each toler-ance to its different levels, one at a time, while holding all othertolerances at their nominal values. The HLM tolerance level num-ber governs the variation of each tolerance—the higher the num-ber the greater the accuracy of the tolerance sensitivity for theparticular measurement. A complete sensitivity report summarizesall the measurements associated with the assembly model and thedifferent tolerances affecting each of these measurements in termsof percentage.

GeoFactor analysis

Similar to the sensitivity analysis, GeoFactor examines the ef-fect of each tolerance on a given measurement. The distinguishing

factor is that the GeoFactor analysis examines the tolerance con-tribution by geometry effect. To represent this effect the result isgiven as a GeoFactor. If a tolerance has a GeoFactor of less thanone, then it has minimal contribution to the variation of a mea-surement point. If the value of the GeoFactor is greater than one,then it has a noticeable effect. In essence the GeoFactor number isthe amplification of each tolerance in the model.

Conclusions

Dimensional engineering practices and tools used in other in-dustries such as automotive and aerospace are highly evolved andalso broadly applicable to shipbuilding. Therefore, U.S. shipyardshave the opportunity to benefit from these well-established meth-ods and tools.

However, because of differences in product volume per designand variability in IP dimensional attributes, complete detailed di-mensional engineering of each ship design is not economicallyviable. Alternatively, shipbuilding dimensional engineering prac-tices should be focused primarily on developing parametric stan-dards by IP type/group/family to be used in establishing the vastmajority of GD&T for future ship designs. Detailed dimensionalvariation simulation analysis should be carried out only for those

Fig. 11 Panel assembly process A

Fig. 12 Panel assembly process B




specific IPs that are determined to be both unique and dimension-ally critical.

At least one dimensional engineering company, DimensionalControl Systems, Inc., is evolving its analysis tools to allow for

easier modeling and simulation of the dimensional effects of weldshrinkage and distortion. This development work is being done tosupport the U.S. shipbuilding industry and is being partiallyfunded by ONR through the SBIR program. Beta testers within the

Fig. 15 Example variance simulation analysis results for a single critical dimension

Fig. 16 Example sensitivity analysis results for a single critical dimension


U.S. shipbuilding industry are being sought to support this devel-opment work.

References

ASME INTERNATIONAL 1994 GD&T Standard ASME Y14.5M.DRAKE, P. 1999 Dimensioning and Tolerancing Handbook, McGraw-Hill

Professional Publishing.GRANT, E. AND LEAVENSWORTH, R. 1996 Statistical Quality Control, Mc-

Graw-Hill.ISHIKAWAJIMA-HARIM A HEAVY INDUSTRIES (IHI) A ND CHIRILLO ,

L. 1985 Process Analysis Via Accuracy Control, Revised, National Ship-building Research Program.

KUMAR, R., AND SPICKNALL, M. 2000 Final Technical Report, DoD NavyN00-091, October.

LEVINGSTON SHIPYARD AND ISHIKAWAJIMA-HARIMA HEAVY INDUSTRIES

(IHI) 1980 Accuracy Control Planning for Hull Construction, U.S. De-partment of Transportation, Maritime Administration.

SHEWHART, W. 1939 Statistical Methods from the Viewpoint of QualityControl, University of Washington, Department of Agriculture.

STORCH, R. 1984 Accuracy control variation merging equations: a casestudy of their application in U.S. shipyards. National ShipbuildingResearchProgram, Ship Production Symposium Proceedings.

YOKATA, T., MINAMIZAKI, K., HORI, S., SHIMOMURA, T., AND MIYAZAKI,M. 1963 A study of accuracy control in hull construction work. JapaneseSociety of Naval Architecture, 14, 242.

Fig. 17 Example GeoFactor analysis for a single critical dimension


A Dimensional Engineering Process for Shipbuilding

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