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1. Production Planning for Mass Customization in Additive Manufacturing: Build Orientation Determination, 2D Packing, and Scheduling

   Oh, Yosep; Zhou, Chi; Behdad, Sara (August 2018, Proceedings of the ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2018 August 26-29, 2018, Quebec, Canada)

   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. 

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2. PartGAN: Weakly-supervised Part Decomposition for Image Generation and Segmentation

   Li, Yuheng; Singh, Krishna Kumar; Xue, Yang; Lee, Yong Jae (January 2021, British Machine Vision Conference (BMVC))

   We propose PartGAN, a novel generative model that disentangles and generates background, object shape, object texture, and decomposes objects into parts without any mask or part annotations. To achieve object-level disentanglement, we build upon prior work and maximize the mutual information between the generated factors and sampled latent prior codes. To achieve part-level decomposition, we learn a part generator, which decomposes an object into parts that are spatially localized, disjoint, and consistent across instances. Extensive experiments on multiple datasets demonstrate that PartGAN discovers consistent object parts, which enable part-based controllable image generation. 

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3. MACHINE LEARNING-BASED ROBOTIC OBJECT DETECTION AND GRASPING FOR COLLABORATIVE ASSEMBLY

   Jianjing Zhang, Chuanping Liu (January 2022, Proc. 2022 International Symposium on Flexible Automation (ISFA))

   Hideki Aoyama; Keiich Shirase (Ed.)

   An integral part of information-centric smart manufacturing is the adaptation of industrial robots to complement human workers in a collaborative manner. While advancement in sensing has enabled real-time monitoring of workspace, understanding the semantic information in the workspace, such as parts and tools, remains a challenge for seamless robot integration. The resulting lack of adaptivity to perform in a dynamic workspace have limited robots to tasks with pre-defined actions. In this paper, a machine learning-based robotic object detection and grasping method is developed to improve the adaptivity of robots. Specifically, object detection based on the concept of single-shot detection (SSD) and convolutional neural network (CNN) is investigated to recognize and localize objects in the workspace. Subsequently, the extracted information from object detection, such as the type, position, and orientation of the object, is fed into a multi-layer perceptron (MLP) to generate the desired joint angles of robotic arm for proper object grasping and handover to the human worker. Network training is guided by forward kinematics of the robotic arm in a self-supervised manner to mitigate issues such as singularity in computation. The effectiveness of the developed method is validated on an eDo robotic arm in a human-robot collaborative assembly case study. 

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4. But Will It Build? Assessing Student Engineering Designers’ Use of Design for Additive Manufacturing Considerations in Design Outcomes

   https://doi.org/10.1115/1.4046071  

   Prabhu, Rohan; Miller, Scarlett R.; Simpson, Timothy W.; Meisel, Nicholas A. (September 2020, Journal of Mechanical Design)

   Abstract Additive manufacturing (AM) enables engineers to improve the functionality and performance of their designs by adding complexity at little to no additional cost. However, AM processes also exhibit certain unique limitations, such as the presence of support material. These limitations must be accounted for to ensure that designs can be manufactured feasibly and cost-effectively. Given these unique process characteristics, it is important for an AM-trained workforce to be able to incorporate both opportunistic and restrictive design for AM (DfAM) considerations into the design process. While AM/DfAM educational interventions have been discussed in the literature, few studies have objectively assessed the integration of DfAM in student engineering designers’ design outcomes. Furthermore, limited research has explored how the use of DfAM affects the students’ AM designs’ achievement of design task objectives. This research explores this gap in literature through an experimental study with 301 undergraduate students. Specifically, participants were exposed to either restrictive DfAM or dual DfAM (both opportunistic and restrictive) and then asked to participate in a design challenge. The participants’ final designs were evaluated for (1) build time and build material (2) the use of the various DfAM concepts, and (3) the features used to manifest these DfAM concepts. The results show that the use of certain DfAM considerations, such as part complexity, number of parts, support material mass, and build plate contact area (corresponding to warping tendency), correlated with the build material and build time of the AM designs—minimizing both of which were objectives of the design task. The results also show that introducing participants to opportunistic DfAM leads to the generation of designs with higher part complexity and lower build plate contact area but a greater presence of inaccessible support material. 

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5. OAPS: An Optimization Algorithm for Part Separation in Assembly Design for Additive Manufacturing

   Deka, Angshuman; Behdad, Sara (August 2018, Proceedings of the ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2018 August 26-29, 2018, Quebec City, Quebec, Canada)

   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. 

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