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Mixed production is a practical solution to realize the idea of mass product customization. The production system needs to adapt to raw materials of product variants appearing in the production line, and thus to properly complete the manufacturing task. Such system autonomy is a core competence in smart manufacturing. This paper presents a smart manufacturing solution for shoe sole gluing in mixed production environment. The solution automatically guides a 5-axis CNC dispensing machine to perform the gluing task of a sole randomly placed on the worktable without use of fixturing. A single RGB-D camera is used to capture the color and depth data of the work environment. A two-stage algorithm is implemented to provide the intelligence required by the solution using the captured data as input. The LINEMOD method first determines a rough pose from training data of all shoe soles at pre-defined orientations. A trimmed interactive closest point (ICP) method then refines the pose using the rough one as the initial solution. Pre-determined working paths are transformed into the correct location around the sole with the refined pose. The corresponding motion commands drive the dispenser controller to complete the gluing task without collision. Test results of real shoe soles validate the effectiveness of the proposed solution. This work contributes to smart manufacturing in the shoe making industry, which has been less studied in the past.

This research investigates the possibility of incorporating affective factors in 3D model creation using handbags as an exemplary product. We demonstrate a crowd-based experimental approach that establishes the correlations between human emotional responses described by bi-polar adjectives and design factors of a bag. A factorial experiment is conducted to understand which design factors independently and/or interactively influence the responses by systematically varying 3D bag models. In-depth statistical analysis of the experimental results indicates that increasing the bag height makes it look more casual; a black bag tends to look luxurious and formal; a bag made of linen feels more classic and sophisticated. Those insights provide a basis for implementing a prototyping design tool that enables users to create 3D bag models with certain affective features using free-form deformation. The proposed approach demonstrates the preliminary feasibility of improving Kansei Engineering applications with big data obtained from crowdsourcing.

This study presents a programming by demonstration (PbD) interface in augmented reality for motion planning in a three-axis CNC glue dispenser. The interface assists a human planner to effectively determine dispenser motion in a planning task. An augmented reality application prompts the planner with instructive information in a video of real environment while guiding the machine in 3D space to dispense glue on a work part. The purpose involves facilitating the generation of the dispenser’s motion commands by enhancing individual spatial reasoning between the dispenser’s tip and the work part. CAD models are not required prior to the planning task, thus eliminating the use of motion planning software. Test results demonstrate the effectiveness of the proposed interface with respect to accelerating the process of the dispenser PbD. The study shows the practical value of augmented reality as a cost-effective interfacing technology for human–machine collaborations in smart manufacturing.