A systematic approach for product modelling and function integration to support adaptive redesign of product variants

To provide systematic support for function integration between product variants, this article addresses three research questions:In this context, a systematic approach is expected to provide several benefits. First, a systematic approach is helpful to model existing designs in a structured way and better appreciate how the design solutions work (Otto and Wood 1998; Tang et al. 2010a). A systematic approach is particularly useful when modelling products having moving parts that are each involved in multiple functions, such that function realisation is complex. Second, a systematic process for modelling design information supports the sharing and reuse of that information (Gietka et al. 2002; Tang et al. 2010b). Reusing existing design information should support the development of reliable products, since it facilitates adoption of proven principles and solutions (Smith et al. 2012). Third, a systematic approach allows for detailed planning of the (re)design process and may reduce design rework, since designers will be less likely to initially overlook some redesign activities, requiring later correction (Tang et al. 2010b). This may be especially useful if the redesign work needs to be coordinated between several team members, or if a designer is not fully familiar with the designs at hand. Finally, decomposing the redesign process into well-defined smaller steps may suggest opportunities for computer support of that process.

To address the research questions a representative redesign task based on a simple product (redesigning a ballpoint pen) was generated as a test case. Existing approaches from the literature were assessed in light of this test case. From this, limitations of the existing product models and function integration methods were appreciated. This led to the generation of a new approach for product modelling, and based on it, a new systematic approach for function integration. The new approaches were then applied to a series of more complex consumer products to test and further improve them. Overall, the development was highly iterative. Literature review also proceeded concurrently with the activities described above.

The rest of this article proceeds as follows. In Section 2, literature is reviewed and the research gap is pinpointed. Section 3 introduces the basis of our solution, which is to model existing variants using a new product model called the Detailed Design Model (DDM). Section 4 then explains the Adaptive Redesign Method (ARM) itself. Section 5 discusses some application cases and insights drawn from them. Section 6 discusses advantages, limitations and some suggestions for further work. Concluding remarks are offered in Section 7.

Most product modelling approaches in literature are based on hierarchical decomposition of a design (typically involving tree diagramming), on block diagramming, and/or on matrix-based modelling. Some models of these types are discussed in the next subsections.

Having discussed a number of product modelling approaches, this subsection moves on to discuss how such approaches have been used as the basis of methods to support function integration. Our review revealed that there are relatively few methods of this type in literature. In one publication, Kalyanasundaram and Lewis (2014) proposed a product integration method to support the derivation of re-configurable products and multi-functional products from two existing products. Their method combines the functionality of two existing products to form a new design, using a matrix model to compare low-level functions between parts (as required by MR3) and interactions between physical parts (partially addressing MR2). However, this approach does not model products at the feature level and therefore cannot identify physical features of a product variant involved in function integration (MR1). Since it does not capture parts at a features level, the method also does not provide detailed guidelines for part modification (MR4). A similar method was developed by Kang and Tang (2013) for developing multi-functional products. This method also uses matrices to model existing products, resulting in similar advantages and drawbacks. However, it does consider supporting parts for primary parts that realise the functions, which better addresses MR2 in comparison to the aforementioned method. Lu et al. (2017) also combined existing products to produce multi-functional products using matrix models. However, they used a different approach to model the function of existing products. Instead of modelling products using the functional decomposition approach by Pahl and Beitz (1996), they derive their function structures from detailed flows and parts. This increases the consistency of the product model which should make product comparison more effective (MR3). Liu et al. (2014) proposed a method for integrating products with interrelated functions. They compared the functions of existing parts using a table, listing the functions of products as the row headings and the products being compared as the column headings. The corresponding parts of each product that realise the function are recorded in the cell of the table. An advantage of this comparison table format is that it enables more than one variant to be compared. However, it cannot represent multiple functions for a given part. As a result, the approach cannot accurately compare the function realisations across product variants, which is needed to determine the redesign activities required for a new variant design (MR4). The method does, however, consider conflicts between product functions which occur when a newly added function reduces the performance of other functions. In this scenario, the researchers suggested using the Theory of Inventive Problem Solving (TRIZ) to resolve the performance conflicts. Alternatively, the Advanced Systematic Inventive Thinking (ASIT) approach can be used to generate ideas for deriving the new combination design (Moon et al. 2012). Finally, Smith et al. (2012) proposed a method to combine more than two existing products to derive a new product. Instead of selecting a single product as a foundation for the new variant design, they selected parts from different products based on customers’ needs and combined them to form a new design. Their method is similar to a morphological analysis. A drawback is that it does not model the architectures of the products. Therefore, it can only determine parts to carry over to the new variant design based on the user's needs. It does not compare product parts functionally to determine the physical modifications required for a part to fulfil the desired functions, as is required by MR4.

To recap, a relatively small number of function integration methods have been proposed in literature, based on analysis of products using hierarchical models, flowchart-based models and matrix models. Hierarchical models offer systematic procedures for identifying detailed functions and parts of products. However, they do not clearly (or sometimes at all) represent the relationship between detailed functions and parts as required by MR1 and MR2. Block diagram models capture flow information and allow function elements to be placed in their sequence of operation to depict how a product operates. However, they typically do not capture the parts and features that realise the functions, and are not graphically well-suited to this due to the dense structures of dependency involved. Hence, block diagramming-based models are not ideal to meet MR1, MR2 and MR3. Overall, neither tree- nor block diagramming-based approaches are ideally suited to reflect the complexity of function-form relationships in a product. In comparison, matrices are very well suited to graphically represent the dense structures of relationships between functions and parts/features, which allows existing design solutions to be comprehensively represented, extracted, compared and traced without specialised software. These characteristics make a matrix-based product model the most suitable basis for a function integration method.

Regarding the methods themselves, the majority of function integration methods in literature simply merge all high-level functions of existing products by comparing the corresponding low-level functions of existing products to determine which parts to carry over into the new design. They do not capture design information at the features level which prevents them from identifying redesign activities at the level of specific modifications to parts. Overall, none of the existing approaches fully address the requirements set out at the start of Sect. 2. This gap, addressed by the current article, is summarised in Table 1.

The DDM is a matrix-based product model that represents a design in both functional and physical domains, in terms of functions, flows, parts, features, states and state transitions, as well the interactions among these elements. It provides enough detail to capture how all these elements and their interactions contribute to the design’s functionality in use.

The following product elements are included in the DDM:All these elements of the DDM are required for the Adaptive Redesign Method (ARM), that is to be described in Sect. 4.

A DDM representation of a product variant is developed in six steps, described below.

As input to the ARM, a DDM matrix must be formed for each of the two product variants to be combined. Although this is quite time-consuming, each DDM matrix is a reuseable resource that could be drawn upon in future each time a new variant is required. As additional variants are modelled, a data library would be built up, broadening the possibilities for forming new variants without additional modelling effort.

The DDM for the first pen variant to be used in the running example was already discussed and is shown in Fig. 6. Fig. 8 shows the DDM matrix for the second variant, namely the basic pen. Note how the basic pen has a different set of operating functions than the retracting design—the only common operating function between the two pens is To deposit ink. It is important that technical function descriptors are used consistently when modelling the different product variants, so that the variants can be compared. For example, noting that the number 6 is used to describe import functions in the retracting pen DDM, the same number is used to describe this type of interaction in the basic pen DDM.

In the method, the variant to be adapted/redesigned is described as the base variant. The variant from which additional operating functions are to be drawn is described as the source variant. Parts and features from the source variant that realise a desirable operating function are to be integrated into the base variant. For the running example, the basic pen is chosen as the base variant while the retracting pen (offering the desired retraction and extension functionality) is chosen as the source variant.

The second phase of the method involves systematic analysis of the DDM matrices for these two variants to identify the features of the base variant that will need to be removed, those that will need to be carried across from the source variant, and which parts need to be redesigned to generate the new variant design. The phase comprises eight steps, described in the next subsections.

The final phase of the method is for the human designer/method user to execute the identified redesign activities to form the new variant design on CAD. Firstly, undesired features of the base variant are removed by removing CAD features from the CAD model. Before a CAD feature is removed, it is important to check that there are no dependent sketches and features built upon it in the model tree that are desired to be retained. Secondly, features from the source variant that realise the desired operating function are added to the base variant. To integrate these features to the base variant, redesign of existing parts of the base variant are required. An example of two steps from the basic pen’s barrel being redesigned is provided in Fig. 12. Features of the base variant which do not require feature modification can be reused in the new variant design, although some adjustment to dimensions may be required.

Figure 13 shows the completed design of the new pencil-shaped pen with the retractable function, that was generated by following the redesign steps laid out in the redesign activities table.

The detailed design modelling procedure was applied to generate DDM matrices for the two product variants, as shown in Fig. 15. More detail is provided in the Supplementary Materials. In overview, the DDM of the PT camera comprises 37 parts, 157 features with 155 interactions, 59 technical functions, and 7 operating functions. Of the seven operating functions, the automatic panning function will be carried across from the PT camera into the fixed camera. The DDM revealed that this operating function requires 25 features to realise it. The equivalent information for the fixed position IP camera is provided in Fig. 15. In comparison to the ballpoint pens case study, there are energy and signal flows in both camera variants.

The IP cameras contain various electronic components that were not modelled in detail. This is because this article focuses on mechanical considerations. Geometric features relating only to part manufacturing processes were also not modelled in detail because, to recap, this article focuses on operating functions associated with product use.

Overall, this application confirmed that the DDM modelling procedure can be applied to products that are more complex than the ballpoint pens discussed earlier. The matrices generated for the IP cameras are rather large, but also extremely sparse. The majority of interactions are fully-constrained connections between features of the same part (i.e. within the clusters shown in Fig. 15). In total, generating the DDMs for the two camera variants required about 8 h of effort. Additionally, about 24 h was required for the CAD modelling of the two variants, but this would not be needed for an application in an industrial context where CAD models would already be available.

Once the DDM matrices were completed, by following the steps of the Adaptive Redesign Method, it was possible to generate the design activities table in about 1.5 h. An additional 5 h were then required to follow the steps by completing the new variant design in CAD—but this would be necessary whether or not the new method was used. The effort would have been substantially less if the straightforward matrix tracing steps and calculations were automated. This indicates that the method, if implemented in special-purpose software, could potentially help to identify redesign activities for even quite complex products fairly rapidly, provided that existing CAD models were available.

Reflecting on the ballpoint pens example and the more complex IP cameras analysis, the latter added insight by confirming that the ARM could be used to compare parts having substantially different geometric features across the two designs, and still identify redesign activities at the parts level for the new design. It was also observed that the effort required for the first phase of the approach (DDM generation) and the final phase (executing redesign steps) increased significantly with the complexity of the product. At the same time, the majority of this effort (and about 75% of the total effort according to the estimates above) was devoted to CAD activities that would need to be done to generate the variants, regardless of whether the method was used in support of the redesign process. Put another way, the overhead of using the method (without any specialised software) appears to be approximately 33%. Overall the cameras analysis provided a measure of confidence in the DDM and ARM, although more studies will be required to substantiate the benefits set out in Sect. 1.1.

Noting that the DDM and ARM approaches are quite intricate, we sought to assess whether they could be applied by a person other than the authors. In an initial assessment, an undergraduate mechanical engineering student was tasked to apply the emerging method to two cases. In the first case, the student applied the method to implement a sheet-fed scanning function into a budget inkjet printer having a single-sheet scanner. In the second case study, the student applied the method to implement an automatic juicing function into a simple hand-operated juicer. While not a comprehensive evaluation, these cases along with the pen and camera cases provided additional confidence that the method can work with a variety of products and also, that it is useable.

The application cases also highlighted that the method relies on the user to detect whether there are conflicts between operating functions across variants being integrated (this was subsequently included in the method description, see Sect. 4.2.1). A conflict occurs when more than one operating function of the new variant design addresses the same task from a user’s perspective. For example, the ARM does not directly identify that the cap of the basic pen, which is used to protect the ballpoint tip, is not needed when the ballpoint tip of the basic pen is made retractable in the new variant. The underpinning reason is that the DDM deliberately only captures what the operating functions are, not what they are used for, to reduce subjectivity in product modelling.

If a conflict is detected by the method user while performing a function integration, then the features (and potentially parts) related to the redundant operating function should be removed from the base variant. If not identified early on, these conflicts become obvious towards the end of the redesign phase while the physical form of the new design is being manipulated in CAD. In such cases, it is possible to return to steps 2 and 3 of the ARM to identify the features and parts that should be removed. Several iterations among steps of the method may be necessary to finalise the design.

While a comprehensive evaluation of the method with practitioners has not yet been attempted, applications by different researchers to four different types of product (pens, IP cameras, printers and juicers) build confidence that:

To summarise, this article offers the following contributions.

Firstly, the DDM and ARM provide means to systematically extract selected operating functions and their physical realisations from existing product designs and integrate them into other existing product variants. This is achieved by modelling the parts of the products at the geometric level using CAD features and capturing their interactions with other features, flows, states and state transitions to realise operating functions. Function integration is not comprehensively supported by prior methods, that are discussed in Sect. 2.2, because as summarised in Table 1 none analyse a product down to the features, states and state transition levels.

Secondly, the Adaptive Redesign Method (ARM) supports identification of redesign activities required to derive a desired new variant design. In particular, it helps to identify the redesign activities for removing unnecessary operating functions and their physical realisations, as well as the redesign activities required for modifying an existing part. This is achieved by analysing and comparing data at the features level of the DDM for the two product variants under consideration. This level of detail is not offered by previous function integration methods discussed in Sect. 2.2.

Thirdly, the two approaches consider not only the primary features but also the supporting features for each operating function. This is achieved by distinguishing partially-constrained interactions (involving primary features) from fully constrained interactions (involving supporting features). Considering the latter allows identification of supporting features and supporting parts that might need to also be modified when primary features are carried over or removed. Existing function integration methods do not comprehensively consider supporting features.

Finally, the DDM provides a more objective approach to modelling the high-level functions of existing products by forming operating function descriptions based on the possible physical configurations of a product. In other words, the modelling procedure is based on describing what a product can do instead of what a product can be used for. Low-level technical functions are then identified based on the interaction between features and flows based on a set of vocabulary by Hirtz et al (2002). As previously stated in literature, identifying function descriptors based on flows is beneficial to reduce subjectivity of functional modelling (Gietka et al 2002).

In this section, some limitations will be discussed with respect to functional and physical domains of the DDM and ARM.

Some limitations concern mainly the functional domain. Firstly, the DDM does not consider what the user uses the product for, i.e. use case functions. This was a deliberate choice to reduce subjectivity in the modelling and analysis. However as a result, the ARM is unable to identify repeating use cases between different operating functions for the new variant design. Recall from Sect. 5.2, that for the ballpoint pens redesign, the ARM did not detect that the cap of the basic pen was not needed once that pen was made retractable. Therefore, future work is needed to model the relationship between the use case functions and operating functions of existing products to more systematically avoid redundant functionality in the new variant design. A second limitation of the DDM and ARM in the functional domain is that they do not consider the relationships between the technical functions involved in each operating function. In particular, the sequence of operation for the technical functions is not represented and, if it is important, it will need to be considered separately by a designer using the approach.

Other limitations concern the physical domain. Firstly, the features listed in the DDM matrix depend on how each physical part is modelled with CAD and since parts can be modelled in different ways, this list may vary. However, because the ARM outputs a redesign activities list in terms of the features of the input CAD models, the utility of the method is not greatly dependent on CAD modelling choices. Secondly, the DDM does not consider the spatial relationships and interface constraints between features and between parts. As a result, the geometry of features from the source variant that are being integrated into a base variant part may need to be scaled to ensure physical fit with other parts of the base variant. These parametric adjustments are not accounted for in this article. Another constraint-related limitation is that the ARM assumes that all existing parts of a product are possible to redesign. However, in practice some parts of a product cannot be easily modified, e.g. because they are purchased from a supplier or form part of a product platform. Future work could incorporate such constraints in the method.

Regarding the relationships between functional and physical domains, the case studies reported in this article confirmed that these relationships are captured by the DDM in sufficient detail for the function integration task. However, future applications may reveal opportunities for improvement in this area as well.

By considering the limitations above and also by considering the PSI analysis approach developed by Reich and Subrahmanian (2022), three additional areas of future work have been identified.

Firstly, we hope to undertake empirical studies in companies with product families to explore how function integration is done in practice. Additional application studies are also needed to test the practicality of the method and to test it against the expected benefits set out in Sect 1.1.

Secondly, the method could be extended to account for different contexts of use. For instance, in the context of product families there are multiple variants with different operating functions and architectures available for integration. In a product family context, the scope of the method could be expanded to determine which variant to source each desired operating function from, and to determine the best sequence of implementing multiple functions to avoid redesigning the same parts multiple times. Also noting that a design is influenced by many considerations beyond the use of a product, and hence that multiple reference frames are possible for design analysis, the methods reported in this article could potentially be expanded to support product modelling and integration from the viewpoints of other lifecycle phases apart from product use, such as assembly, inspection and repair, etc. To achieve this would require expanding the DDM to map design elements onto considerations in different lifecycle phases.

Finally, future work could investigate opportunities to reduce the data requirements and effort-intensiveness of the DDM and ARM. Data requirements might be reduced by developing an initial assessment approach to determine the parts of a product that need to be modelled down to the feature level and those that only need to be modelled at the parts level for instance, because they are not involved in any function expected to change, or because they cannot be changed—for instance because they are off-the-shelf parts. Effort-intensiveness could be reduced by automating some of the steps in the DDM modelling method and the ARM to reduce the overall analysis time. Automatable steps include (1) extracting features and their interactions from the CAD model tree to form the DDM, (2) calculating similarity between features and (3) identifying redesign activities based on the similarity value. By reducing effort and data requirements, this would also improve the scalability of the DDM and ARM.