Furniture Assembly Research Articles

Continuous Improvement of Productivity and Quality with Applying Lean Six Sigma: A case study

Any business that wants to compete may need to constantly raise both its output and quality. An effective and popular approach to achieving continuous improvement is known as Lean Six Sigma. This paper demonstrates a successful implementation through a case study in wood manufacturing. The enhancement greatly benefited from the optimization of the production process. The seven quality control tools were integrated into specific systematic steps of the PDCA cycle and DMAIC process, along with lean technology. As a result, this led to an increase in product quality and a decrease in manufacturing errors. Customer satisfaction and market competitiveness have risen as a result. A wood production line's productivity and quality were both improved, and this case study demonstrates the positive effects. It could be considered for implementation in various production or assembly lines in other fields such as electronic, clothing and furniture assembly lines.

Comparative study on the bending moment capacity and stiffness of innovative and traditional furniture corner joints

ABSTRACT This study compares the bending moment capacity under compression and bending moment capacity under tension values of eight selected structural furniture joints. Specimens (type L) with a dimension of 150 × 150 × 366 mm were made from three-layer, 18 mm thick particleboard. The corner joints were tested in angular bending in compression and tension. Both classic and new joining elements (plastic cam, zinc cam, one-part plastic connector, screw cam, plastic connector with euroscrew, and birch dowel pre-glued) for the simple assembly of furniture were used in the study. Cam joints with wooden dowels can withstand high stress. There are only minimal differences between joints made of plastic or zinc cams with dowels. If birch dowels with an adhesive coating are added to the plastic joint from sample with plastic connector with euroscrew, this joint will reach 126% higher values in angular bending tests – compressive stress.

Additive manufacturing advancement through large-scale screw-extrusion 3D printing for precision parawood powder/PLA furniture production

In this paper, a large-scale screw-extrusion 3D printer specifically tailored for additive manufacturing applications is introduced, primarily focusing on crafting parawood powder/polylactic acid (PLA) furniture. Boasting a large build volume (700 × 700 × 700 mm3), the printer incorporates a meticulously designed screw extruder to ensure the precise feeding of composite material pellets. The investigation delves into the nuanced relationship between variations in the extruder nozzle orifice diameter and the resulting impact on the extrusion rate, directly correlating these variations with the motor speed. Additionally, the influence of the parawood powder/PLA ratio is explored through comprehensive mechanical property testing of the printed specimens. Optimal outcomes were attained with a 15 %w/w parawood powder composition, yielding an impressive ultimate strength of 54 MPa under specific printing conditions. The efficacy of the large-scale screw-extrusion 3D printer was robustly validated through the successful production of a parawood powder/PLA stacking chair, meeting the criteria stipulated in the Thai industrial standard. Furthermore, an identified parawood powder/PLA component, characterized by a rectangular cylinder with a cross-sectional area of 19.4 × 24.0 mm2, holds promising potential for versatile applications in furniture assembly. This innovative extrusion 3D printing approach, combined with meticulously optimized parameters, has unequivocal potential for manufacturing a diverse array of parawood powder/PLA furniture, elevating the value of parawood byproducts and contributing to waste reduction during processing.

Prediction of service time for home delivery services using machine learning

With the rise of ready-to-assemble furniture, driven by international giants like IKEA, assembly services were increasingly offered by the same retailers. When planning orders with assembly services, the estimation of the service time leads to additional difficulties compared to standard delivery planning. Assembling large wardrobes or kitchens can take hours or even days while assembling a chair can be done in a few minutes. Combined with the usually vast amounts of offered products, a lot of knowledge is required to plan efficient and exact delivery routes. This paper shows how an artificial neural network (ANN) can be used to accurately predict the service time of a delivery based on factors such as the goods to be delivered or the personnel providing the service. The data used include not only deliveries with assembly of furniture, but also deliveries of goods without assembly and delivery of goods requiring electrical installation. The goal is to create a solution that can predict the time needed based on criteria such the type of furniture, the weight of the goods, and the experiences of the service technicians. The findings show that ANNs can be applied to this scenario and outperform more classical approaches, such as multiple linear regression or support vector machines. Still existing problems are largely due to the provided data, e.g., a large difference between the number of short and longer duration orders, which made it harder to accurately predict orders with longer duration.

Next-Generation Furniture Assembly by AI and Robots: Team SK2Y: A Winner of the Furniture Assembly Competition at AI–Robot Challenge 2021

Next-Generation Furniture Assembly by AI and Robots: Team SK²Y: A Winner of the Furniture Assembly Competition at AI–Robot Challenge 2021

3D Part Assembly Generation With Instance Encoded Transformer

It is desirable to enable robots capable of automatic assembly. Structural understanding of object parts plays a crucial role in this task yet remains relatively unexplored. In this paper, we focus on the setting of furniture assembly from a complete set of part geometries, which is essentially a 6-DoF part pose estimation problem. We propose a multi-layer transformer-based framework that involves geometric and relational reasoning between parts to update the part poses iteratively. We carefully design a unique instance encoding to solve the ambiguity between geometrically-similar parts so that all parts can be distinguished. In addition to assembling from scratch, we extend our framework to a new task called in-process part assembly. Analogous to furniture maintenance, it requires robots to continue with unfinished products and assemble the remaining parts into appropriate positions. Our method achieves far more than 10% improvements over the current state-of-the-art in multiple metrics on the public PartNet dataset. Extensive experiments and quantitative comparisons demonstrate the effectiveness of the proposed framework.

Withdrawal Resistance of T-Nuts in Various Furniture Materials

T-nuts are factory installed in the holes of the ready-to-assemble furniture components. There is a risk that the t-nut spontaneously falls out during transport or storage and get lost; the t-nut can also be pushed into inaccessible spaces during assembly. These complications can make furniture assembly impossible. For this reason, sufficient force to hold the t-nut in the hole is essential. The article presents the test results of the forces holding the t-nuts in five furniture materials (softwood, Oriented Strand Board, plywood, and particleboard in two variants). The M6 t-nuts with four prongs were installed in predrilled 8 mm holes. The resistance to withdrawal of the t-nuts was measured with a universal testing machine. The tested materials could be divided into three groups in terms of the risk of the t-nut falling out: softwood and plywood – low risk, F = 1113.2-1158.0 N; OSB and particleboard – moderate risk, F = 592.3-645.5 N, particleboard with a pad – high risk, F = 645.5 N. The results show that the withdrawal resistance is not correlated with the density of the wood material, and that it decreased with the degree of wood material processing – the less processed the material, the greater the resistance to withdrawal of the t-nuts.

Robotic furniture assembly: task abstraction, motion planning, and control

Robotic furniture assembly: task abstraction, motion planning, and control

Measuring Collaboration Load With Pupillary Responses - Implications for the Design of Instructions in Task-Oriented HRI.

In face-to-face interaction, speakers establish common ground incrementally, the mutual belief of understanding. Instead of constructing “one-shot” complete utterances, speakers tend to package pieces of information in smaller fragments (what Clark calls “installments”). The aim of this paper was to investigate how speakers' fragmented construction of utterances affect the cognitive load of the conversational partners during utterance production and comprehension. In a collaborative furniture assembly, participants instructed each other how to build an IKEA stool. Pupil diameter was measured as an outcome of effort and cognitive processing in the collaborative task. Pupillometry data and eye-gaze behaviour indicated that more cognitive resources were required by speakers to construct fragmented rather than non-fragmented utterances. Such construction of utterances by audience design was associated with higher cognitive load for speakers. We also found that listeners' cognitive resources were decreased in each new speaker utterance, suggesting that speakers' efforts in the fragmented construction of utterances were successful to resolve ambiguities. The results indicated that speaking in fragments is beneficial for minimising collaboration load, however, adapting to listeners is a demanding task. We discuss implications for future empirical research on the design of task-oriented human-robot interactions, and how assistive social robots may benefit from the production of fragmented instructions.

Inventory

A skull as large as a grand piano, chest bones curved into steep underwater cathedrals, vertebrae the size of desks—a whole furniture assembly of a whale—joists and beams and rafters and ridge-poles and sleepers and underpinnings – a whole framework and scaffolding of whale, bleached and white – bones as big as oars, and larger. And yet when Ishmael goes through the subterranean parts of the whale slung on deck, unbuckling his garters, inspecting joints and bones and ribs and the entire unconditional skeleton—and when he has the dimensions tattooed on his right arm—he perhaps forgets the little pebbles of the ear bones, with the involucrum, small enough to nestle in the hand, rolled like a tiny conch shell, and as white – the thick, dense bone forming the inner wall of the tympanic bone – the littleness of these bones that float in hollow pockets of the whale as it moves in the greater pocket of the sea –

A Hierarchical Architecture for Human–Robot Cooperation Processes

In this article, we propose FlexHRC+, a hierarchical human-robot cooperation architecture designed to provide collaborative robots with an extended degree of autonomy when supporting human operators in high-variability shop-floor tasks. The architecture encompasses three levels, namely for perception, representation, and action. Building up on previous work, here we focus on an in-the-loop decision-making process for the operations of collaborative robots coping with the variability of actions carried out by human operators, and the representation level, integrating a hierarchical and/or graph whose online behavior is formally specified using first-order logic. The architecture is accompanied by experiments including collaborative furniture assembly and object positioning tasks.

The Development of Furniture Assembly Instruction Based on Augmented-Reality

This research utilized Augmented Reality (AR) technology along with interactive multimedia applications to improve visual guidance and assist nonexpert users in assembling furniture. Rather than paperbased or video instructions, AR enhanced learning and manipulative-interactive approach. This research used the Waterfall model for development. It was divided into a couple of stages. It consisted of specifying the problem and requirements, designing the system, implementing and integrating it, and testing the system. Identifying problems and requirements was conducted through an online questionnaire and related works study. The graphics tool used was 3D Studio Max. Unity3d was used to develop the system with the software library from Qualcomm’s Vuforia for the AR implementation. The results show the mobile application called ARsembly. It serves as the early stage of concept model as it only assists the simple assembly. Through system testing performed by both researchers and end-users, ARsembly is proven to be useful and helpful in learning and performing furniture assembly.

Specifying Dual-Arm Robot Planning Problems Through Natural Language and Demonstration

Multi-modal robot programming with natural language and demonstration is a promising technique for efficient teaching of manipulation tasks in industrial environments. In particular, with modern dual-arm robots designed to quickly take over tasks at typical industrial workbenches, the direct teaching of task sequences hardly utilizes the robots’ capabilities. We therefore propose a two-staged approach that combines natural language instructions and demonstration with simultaneous task allocation and motion scheduling based on constraint programming. Instead of providing a task description and demonstrations that are replayed to a large extent, the user describes tasks to be scheduled with all relevant constraints and demonstrates relevant locations relative to workpieces and other objects. With explicitly stated constraints on the partial ordering of tasks, the solver allocates the tasks to the robot arms and schedules them in time while avoiding self-collisions and reducing the makespan in our experiment by $33\%$ . The linguistic concepts of naming and grouping enable systematic reuse of sub-task ensembles. The proposed approach is evaluated with four variants of a gluing use-case from furniture assembly in user studies with ten participants. In these user studies, we observed a speed-up for the task definition of more than six times compared to a textual specification of the planning problems using the Python-based planner API.

Development of a performance measurement system for a home furniture delivery and assembly logistics provider in Thailand

The global furniture retail brand, IKEA, has an extraordinary characteristic. Their customers can tailor the product's components for assembly. A last-mile delivery provider must be capable of providing both home delivery and assembly services. Their operational performance is often less efficient. Therefore, this article aims to develop a performance measurement system (PMS) for last-mile home furniture delivery and assembly services. The PMS was developed based on the case study of a provider that has been outsourced by the IKEA branch in Thailand. All related performance metrics were derived through the SCOR model. The key metrics were identified relying on the real operational data of the case and then prioritised using the analytic hierarchy process. The developed PMS was applied to measure the last-mile service provider's performance of the IKEA branch in Thailand. It benefits a provider for monitoring and controlling the quality of service to gain customer satisfaction.

Dynamic furniture modeling through assembly instructions

We present a technique for parsing widely used furniture assembly instructions, and reconstructing the 3D models of furniture components and their dynamic assembly process. Our technique takes as input a multi-step assembly instruction in a vector graphic format and starts to group the vector graphic primitives into semantic elements representing individual furniture parts, mechanical connectors (e.g., screws, bolts and hinges), arrows, visual highlights, and numbers. To reconstruct the dynamic assembly process depicted over multiple steps, our system identifies previously built 3D furniture components when parsing a new step, and uses them to address the challenge of occlusions while generating new 3D components incrementally. With a wide range of examples covering a variety of furniture types, we demonstrate the use of our system to animate the 3D furniture assembly process and, beyond that, the semantic-aware furniture editing as well as the fabrication of personalized furnitures.

Sex Differences in Furniture Assembly Performance: An Experimental Study

SummaryThis study examined sex differences in furniture assembly performance by manipulating the availability of instructions. Two groups of participants with an equal number of men and women assembled a kitchen trolley from IKEA. One group received step‐by‐step instructions, and the other group a diagram of the finished product. In addition, individual spatial ability was measured with the mental rotation test (MRT) and added to the analyses. Our results showed that men assembled the furniture faster (d = 0.78) and more accurately (d = 0.65) than women. Overall, participants performed better with step‐by‐step instructions than without (d = 0.61), and the time spent on instructions was negatively related to MRT scores, r = −.428, p = .006. Aside from the time spent on instructions, women assembled the furniture nearly as fast as men did, and the sex difference in assembly score could be explained by differences in individual spatial ability. Copyright © 2015 John Wiley & Sons, Ltd.

Computational interlocking furniture assembly

Furniture typically consists of assemblies of elongated and planar parts that are connected together by glue, nails, hinges, screws, or other means that do not encourage disassembly and re-assembly. An alternative approach is to use an interlocking mechanism, where the component parts tightly interlock with one another. The challenge in designing such a network of interlocking joints is that local analysis is insufficient to guarantee global interlocking, and there is a huge number of joint combinations that require an enormous exploration effort to ensure global interlocking. In this paper, we present a computational solution to support the design of a network of interlocking joints that form a globally-interlocking furniture assembly. The key idea is to break the furniture complex into an overlapping set of small groups, where the parts in each group are immobilized by a local key, and adjacent groups are further locked with dependencies. The dependency among the groups saves the effort of exploring the immobilization of every subset of parts in the assembly, thus allowing the intensive interlocking computation to be localized within each small group. We demonstrate the effectiveness of our technique on many globally-interlocking furniture assemblies of various shapes and complexity.

韓国の集合住宅における個別要求に対応した住戸改造に関する実態研究

The purpose of this study is to investigate the actual nature of the customization of apartments in South Korea and the usage of interior components and industrialization in these dwellings. The results are summarized as follows. 1) The residents did work in the customization period in all three stages of occupancy of their unit. 2) The scale of the customization work and the skill of workers performing them differed by construction stage. 3) The assembly of furniture and equipment such as a system kitchen is progressing. But craftsmen commonly intervened in the final stages of work.

Learning grounded finite-state representations from unstructured demonstrations

Robots exhibit flexible behavior largely in proportion to their degree of knowledge about the world. Such knowledge is often meticulously hand-coded for a narrow class of tasks, limiting the scope of possible robot competencies. Thus, the primary limiting factor of robot capabilities is often not the physical attributes of the robot, but the limited time and skill of expert programmers. One way to deal with the vast number of situations and environments that robots face outside the laboratory is to provide users with simple methods for programming robots that do not require the skill of an expert. For this reason, learning from demonstration (LfD) has become a popular alternative to traditional robot programming methods, aiming to provide a natural mechanism for quickly teaching robots. By simply showing a robot how to perform a task, users can easily demonstrate new tasks as needed, without any special knowledge about the robot. Unfortunately, LfD often yields little knowledge about the world, and thus lacks robust generalization capabilities, especially for complex, multi-step tasks. We present a series of algorithms that draw from recent advances in Bayesian non-parametric statistics and control theory to automatically detect and leverage repeated structure at multiple levels of abstraction in demonstration data. The discovery of repeated structure provides critical insights into task invariants, features of importance, high-level task structure, and appropriate skills for the task. This culminates in the discovery of a finite-state representation of the task, composed of grounded skills that are flexible and reusable, providing robust generalization and transfer in complex, multi-step robotic tasks. These algorithms are tested and evaluated using a PR2 mobile manipulator, showing success on several complex real-world tasks, such as furniture assembly.

Vliv koncové polohy demontovatelného nábytkového kování na odolnost vůči ohybovému momentu u korpusového nábytku

The goal of this paper is to investigate the effect of the end distance of cam lock fasteners on the bending moment resistance of knock-down corner joints. The preliminary study of knock-down furniture assembly plans was done in order to discover the manufacturers’ fastener typical usage in case construction. Laminated particleboard, cam fasteners and wooden dowels were used for specimen construction. L-shaped joint specimens 760 mm in length were tested by pressing the joint members together – also called a compression test in the angle plane. The study results showed that cam fasteners with end distance of 60 mm from the member edges perform the best.