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Many industries, such as human-centric product manufacturing, are calling for mass customization with personalized products. One key enabler of mass customization is 3D printing, which makes flexible design and manufacturing possible. However, the personalized designs bring challenges for the shape matching and analysis, owing to the high complexity and shape variations. Traditional shape matching methods are limited to spatial alignment and finding a transformation matrix for two shapes, which cannot determine a vertex-to-vertex or feature-to-feature correlation between the two shapes. Hence, such a method cannot measure the deformation of the shape and interested features directly. To measure the deformations widely seen in the mass customization paradigm and address the issues of alignment methods in shape matching, we identify the geometry matching of deformed shapes as a correspondence problem. The problem is challenging due to the huge solution space and nonlinear complexity, which is difficult for conventional optimization methods to solve. According to the observation that the well-established massive databases provide the correspondence results of the treated teeth models, a learning-based method is proposed for the shape correspondence problem. Specifically, a state-of-the-art geometric deep learning method is used to learn the correspondence of a set of collected deformed shapes. Through learning the deformations of the models, the underlying variations of the shapes are extracted and used for finding the vertex-to-vertex mapping among these shapes. We demonstrate the application of the proposed approach in the orthodontics industry, and the experimental results show that the proposed method can predict correspondence fast and accurate, also robust to extreme cases. Furthermore, the proposed method is favorably suitable for deformed shape analysis in mass customization enabled by 3D printing. 

This chapter addresses problems that arise during product design for sustainability 5 and the life cycle. A description of the problem itself is provided from an industrial 6 engineering viewpoint. The first section describes the problem elements, including 7 the need to expand the set of conflicting objectives under consideration, the need to 8 consider the entire product life cycle, the need to employ new data acquisition tools, 9 and the need to investigate the complex role of consumer behavior before, during, 10 and after the point of purchase. Subsequent sections summarize work the authors 11 have done towards solving these problems. A general mathematical programming 12 framework is first presented. This chapter highlights several instances of the benefits 13 of bringing the logic and mathematical rigor of industrial engineering methods 14 to these problems. The authors’ previous contributions to sustainable design are 15 presented and include defining the concept of the product life cycle from a decision- 16 based design point of view, developing different types of decision-making tech- 17 niques for engineering design (both subjective and objective), normative decision 18 analytic methods (e.g., multiattribute utility, constrained optimization), methods 19 for environmentally conscious design to cover new environmental objectives (e.g., 20 connection of design with the end-of-use phase), and immersive computing tech- 21 nologies to address challenges with information-intensive design procedures. The 22 final section presents methods to consider heterogeneous consumer behavior during 23 product selection, use, and disposal. 

The study investigates the impact of build orientation policies on the production time in additive manufacturing (AM) for mass customisation business models. Two main orientation policies are considered: (1) Laying Policy (LP) that focuses on reducing the height of parts; and (2) Standing Policy (SP) that aims to minimise the projection base plane of parts to reduce the number of jobs. While LP minimises the build time per job since parts have low height, it could increase the total completion time as the number of parts increases. On the other hand, SP takes longer build time per job due to the high height of parts, where it could lead to a fewer number of jobs. Several numerical experiments have been conducted based on Stereolithography (SLA). The results show that, when the number of parts is experimentally about 40, SP could be more preferred than LP for minimising the completion time where the shape tendency of parts is likely to affect the extent of preference for the policies. When 40 parts with long and flat shape are considered, SP reduces the completion time by 15.7% over the default policy, the initial orientation of a part, while LP reduces by only 6.6%. 

The purpose of this study is to optimize production planning decisions in additive manufacturing for mass customization (AMMC) systems in which customer demands are highly variable. The main research question is to find the optimal quantity of products for scheduling, the economic scheduling quantity (ESQ). If the scheduling quantity is too large, the time to collect customer orders increases and a penalty cost occurs due to the delay in responding to consumer demands. On the other hand, if the scheduling quantity is too small, the number of parts per jobs decreases and parts are not efficiently packed within a workspace and consequently the build process cost increases. An experiment is provided for the case of stereolithography (SLA) and 2D packing to demonstrate how the build time per part increases as the scheduling quantity decreases. In addition, a mathematical framework based on ESQ is provided to evaluate the production capacity in satisfying the market demand. 

The objective of this study is to develop a mathematical framework for determining the minimum number of parts required in a product to satisfy a list of functional requirements (FRs) given a set of connections between FRs. The problem is modeled as a graph coloring technique in which a graph G with n nodes (representing the FRs) and m edges (representing the connections between the FRs) is studied to determine the graph’s chromatic number c(G), which is the minimum number of colors required to properly color the graph. The chromatic number of the graph represents the minimum number of parts needed to satisfy the list of FRs. In addition, the study calculates the computational efficiency of the proposed algorithm. Several examples are provided to show the application of the proposed algorithm. 

Prior studies have already predicted that enforcement of IP on the additive manufacturing industry will not be successful due to the widespread use of file-sharing technologies, similar to the entertainment and music industry. This paper discusses the capabilities of Blockchain technology for protecting IP in the design and manufacturing area. A conceptual framework for a digital platform is defined in this paper and further, a survey study of engineering design and manufacturing students has been conducted to identify the main motivation behind developing these platforms and the types of features that should be included in Blockchain-based IP platforms for asset protection, particularly for product design. In addition, respondents provided their opinions about the type of industry that might be affected more by the threat of counterfeiting products and the role of Blockchain-based IP systems on the growth and development of innovation. 

Additive manufacturing (AM) has the potential to improve productivity especially processing time, cost and surface roughness. In similar lines, part separation for assembly-based design in additive manufacturing can help in improving productivity. This paper discusses an optimization technique for part separation in assembly based part design in additive manufacturing. The technique improves productivity by decreasing the processing time of printed parts, which is the sum of the build time and the assembly time. The technique uses optimal cutting planes for part separation that has distinct advantages compared to random cutting planes. The work discusses a Genetic Algorithm (GA) technique for part separation using planar cuts. The optimization technique provides the optimal number of parts for assembly and their corresponding build orientations for the minimum processing time. Three examples have been provided to demonstrate the application of the proposed method. Finally, the results from two examples are compared to the already established hill climbing optimization method for part separation. 

The efficient production planning of Additively Manufactured (AM) parts is a key point for industry-scale adoption of AM. This study develops an AM-based production plan for the case of manufacturing a significant number of parts with different shapes and sizes by multiple machines with the ultimate purpose of reducing the cycle time. The proposed AM-based production planning includes three main steps: (1) determination of build orientation; (2) 2D packing of parts within the limited workspace of AM machines; and (3) scheduling parts on multiple AM machines. For making decision about build orientation, two main policies are considered: (1) laying policy in which the focus is on reducing the height of parts; and (2) standing policy which aims at minimizing the projection area on the tray to reduce the number of jobs. A heuristic algorithm is suggested to solve 2D packing and scheduling problems. A numerical example is conducted to identify which policy is more preferred in terms of cycle time. As a result, the standing policy is more preferred than the laying policy as the number of parts increases. In the case of testing 3,000 parts, the cycle time of standing policy is about 6% shorter than laying policy. 

This study proposes a graph partitioning method to facilitate the idea of physical integration proposed in Axiomatic Design. According to the physical integration concept, the design features should be integrated into a single physical part or a few parts with the aim of reducing the information content, given that the independence of functional requirements is still satisfied. However, no specific method is suggested in the literature for determining the optimal degree of physical integration of a design artifact. This is particularly important with the current advancement in Additive Manufacturing technologies. Since additive manufacturing allows physical elements to be integrated, new methods are needed to help designers evaluate the impact of the physical integration on the design success. The objective of this paper is to develop a framework for determining the best way that functional requirements can be assigned to different parts of a product. 

Additive Manufacturing (AM) provides the advantage of producing complex shapes that are not possible through traditional cutting processes. Along with this line, assembly-based part design in AM creates some opportunities for productivity improvement. This paper proposes an improved optimization algorithm for part separation (OAPS) in assembly-based part design in additive manufacturing. For a given object, previous studies often provide the optimal number of parts resulting from cutting processes and their corresponding orientation to obtain the minimum processing time. During part separation, the cutting plane direction to generate subparts for assembly was often selected randomly in previous studies. The current work addresses the use of random cutting planes for part separation and instead uses the hill climbing optimization technique to generate the cutting planes to separate the parts. The OAPS provides the optimal number of assemblies and the build orientation of the parts for the minimum processing time. Two examples are provided to demonstrate the application of OAPS algorithm.