Manual Assembly Research Articles

A Computer Vision-Based Model for Automatic Motion Time Study

Motion time study is employed by manufacturing industries to determine operation time. An accurate estimate of operation time is crucial for effective process improvement and production planning. Traditional motion time study is conducted by human analysts with stopwatches, which may be exposed to human errors. In this paper, an automated time study model based on computer vision is proposed. The model integrates a convolutional neural network, which analyzes a video of a manual operation to classify work elements in each video frame, with a time study model that automatically estimates the work element times. An experiment is conducted using a grayscale video and a color video of a manual assembly operation. The work element times from the model are statistically compared to the reference work element time values. The result shows no statistical difference among the time data, which clearly demonstrates the effectiveness of the proposed model.

Pneumatic automation of the assembly of spherical bearing and pin header into the gearbox housing

With the advent of computers and technology, remarkable changes are observed in the automotive industries in recent decades. The implementation of automation and advanced processing techniques have resulted in revamping of the production systems. Especially assembly processes which are based on repetitive tasks are greatly influenced by automation as it results in the enhancement of productivity by avoiding human intervention. The productivity and profit directly depends on the efficiency of the assembly processes especially in case of small scale industries. In this regard, automation can bring remarkable changes in performance of assembly processes. The present work attempts to design a piece of semi-automatic equipment powered by pneumatics for carrying out the repetitive task of assembling of components at small-scale automotive industries, for achieving enhanced productivity as compared to the currently adapted manual assembly process. Parts to be assembled are a spherical bearing and a pin header into the gearbox housing. This equipment was designed on a CATIA V5 platform. The manuscript presents the design of all the components of the proposed equipment and also the working of the system. The proposed new equipment is expected increase the productivity and also decrease the human errors caused during the manual assembly process.

Development of a Digital Shopfloor Management Board for Manual Assembly Systems

Development of a Digital Shopfloor Management Board for Manual Assembly Systems

ModElec

Integrating electronics with highly custom 3D designs for the physical fabrication of interactive prototypes is traditionally cumbersome and requires numerous iterations of manual assembly and debugging. With the new capabilities of 3D printers, combining electronic design and 3D modeling workflows can lower the barrier for achieving interactive functionality or iterating on the overall design. We present ModElec---an interactive design tool that enables the coordinated expression of electronic and physical design intent by allowing designers to integrate 3D-printable circuits with 3D forms. With ModElec, the user can arrange electronic parts in a 3D body, modify the model design with embedded circuits updated, and preview the auto-generated 3D traces that can be directly printed with a multi-material-based 3D printer. We demonstrate the potential of ModElec with four example applications, from a set of game controls to reconfigurable devices. Further, the tool was reported as easy to use through a preliminary evaluation with eight designers.

Continuous ergonomic risk perception for manual assembly operations using wearable multi-sensor posture estimation

PurposeThis paper aims to perform the real-time and accurate ergonomics analysis for the operator in the manual assembly, with the purpose of identifying potential ergonomic injuries when encountering labor-excessive and unreasonable assembly operations.Design/methodology/approachInstead of acquiring body data for ergonomic evaluation by arranging many observers around, this paper proposes a multi-sensor based wearable system to track worker’s posture for a continuous ergonomic assessment. Moreover, given the accurate neck postural data from the shop floor by the proposed wearable system, a continuous rapid upper limb assessment method with robustness to occasional posture changes, is proposed to evaluate the neck and upper back risk during the manual assembly operations.FindingsThe proposed method can retrieve human activity data during manual assembly operations, and experimental results illustrate that the proposed work is flexible and accurate for continuous ergonomic assessments in manual assembly operations.Originality/valueBased on the proposed multi-sensor based wearable system for posture acquisition, a real-time and high-precision ergonomics analysis is achieved with the postural data arrived continuously, it can provide a more objective indicator to assess the ergonomics during manual assembly.

Optimization of Industry 4.0 Implementation Selection Process towards Enhancement of a Manual Assembly Line

Last year’s developments are characterized by a dramatic drop in customer demand leading to stiff competition and more challenges that each enterprise needs to cope with in a globalized market. Production in low-mix/high-volume batches is replaced with low-volume/high-variety production, which demands excessive information flow throughout production facilities. To cope with the excessive information flow, this production paradigm requires the integration of new advanced technology within production that enables the transformation of production towards smart production, i.e., towards Industry 4.0. The procedure that helps the decision-makers to select the most appropriate I4.0 technology to integrate within the current assembly line considering the expected outcomes of KPIs are not significantly been the subject of the research in the literature. Therefore, this research proposes a conceptual procedure that focus on the current state of the individual assembly line and proposes the technology to implement. The proposed solution is aligned with the expected strategic goals of the company since procedure takes into consideration value from the end-user perspective, current production plans, scheduling, throughput, and other relevant manufacturing metrics. The validation of the method was conducted on a real assembly line. The results of the validation study emphasize the importance of the individual approach for each assembly line since the preferences of the user as well as his diversified needs and possibilities affect the optimal technology selection.

Solving Multi-Objective Paired Single Row Facility Layout Problem Using Hybrid Variable Neighborhood Search

The footwear industry is distinguished by its manual assembly line and a high proportion of shared workstation configuration. This study focuses on a subset of the single row facility layout problem known as the paired single row facility layout problem. As one of type of single-row facility layout, the paired single row facility layout problem cannot be solved quickly. Further, different objectives also need to be considered in the decision-making process. Therefore, multi-objective approaches are proposed to minimize the penalty of material handler usage while maximizing the adjacency function based on each workstation's closeness rating. A Single Row Facility Layout is an NP-hard problem; this problem also belongs to the NP-hard problem class. As a result, we propose a hybrid method combining variable neighborhood search (VNS) and genetic algorithm (GA) to solve the problem of obtaining the optimal configuration of a multi-objective paired single-row assembly line. A heuristic approach was used to create the schematic representation solution. To obtain the neighborhood solutions, a hybrid VNSGA was used. The schematic representation solution employs crossover and variable neighborhood descent. Using the concept of VNS, the neighborhood was changed in each generation.

Hand Gesture Recognition of Methods-Time Measurement-1 Motions in Manual Assembly Tasks Using Graph Convolutional Networks

ABSTARCT Gesture recognition is gaining popularity in many fields, including gesture control, robotics, or medical applications. However, the technology is barely used in industrial manufacturing processes due to high costs, a time-consuming configuration, and changes in the workflow. This paper proposes a minimally invasive approach to recognize workers' hand motions in manual assembly tasks. The novelty of this approach is the use of only one camera instead of any other sensors and the application of state-of-the-art graph neural networks. The method relies on monocular RGB video data to predict the basic motions of the industry standard motion-time system Methods-Time Measurement-1. Our two-stage neural network composed of hand key point extraction and adaptive graph convolution delivers accurate classification results in real-time. To train and validate the model, we created a dataset containing 22,000 frames of real-world assembly tasks. The data produced by this method in a production line can be used for motion time verification, assembly-line design, or assembly cost estimation. In a use-case study, we show that the proposed approach can generate Methods-Time Measurement analysis tables. These have so far only been accurately created by human experts. Source code: https://github.com/alexriedel1/Hand-Gesture-Recognition-in-manual-assembly-tasks-using-GCN

A deep learning-based worker assistance system for error prevention: Case study in a real-world manual assembly

Modern assembly systems adapt to the requirements of customised and short-lived products. As assembly tasks become increasingly complex and change rapidly, the cognitive load on employees increases. This leads to the use of assistance systems for manual assembly to detect and avoid human errors and thus ensure consistent product quality. Most of these systems promise to improve the production environment but have hardly been studied quantitatively so far. Recent advances in deep learning-based computer vision have also not yet been fully exploited. This study aims to provide architectural, and implementational details of a state-of-the-art assembly assistance system based on an object detection model. The proposed architecture is intended to be representative of modern assistance systems. The error prevention potential is determined in a case study in which test subjects manually assemble a complex explosion-proof tubular lamp. The results show 51 % fewer assembly errors compared to a control group without assistance. Three of the four considered types of error classes have been reduced by at least 42 %. In particular, errors by omission are most likely to be prevented by the system. The reduction in the error rate is observed over the entire period of 30 consecutive product assemblies, comparing assisted and unassisted assembly. Furthermore, the recorded assembly data are found to be valuable regarding traceability and production improvement processes.

The impact of the collaborative workplace on the production system capacity: Simulation modelling vs. real-world application approach

In recent years, there have been more and more collaborative workplaces in different types of manufacturing systems. Although the introduction of collaborative workplaces can be cost-effective, there is still much uncertainty about how such workplaces affect the capacity of the rest of production system. The article presents the importance of introducing collaborative workplaces in manual assembly operations where the production capacities are already limited. With the simulation modelling method, the evaluation of the introduction impact of collaborative workplaces on manual assembly operations that represent bottlenecks in the production process is presented. The research presents two approaches to workplace performance evaluation, both simulation modelling and a real-world collaborative workplace example, as a basis of a detailed time study. The main findings are comparisons of simulation modelling results and a study of a real-world collaborative workplace, with graphically and numerically presented parameters describing the utilization of production capacities, their efficiency and financial justification. The research confirms the expediency of the collaborative workplaces use and emphasise the importance of further research in the field of their technological and sociological impacts.

Cognitive compatibility in modern manual mixed-model assembly systems

The compatibility concept is widely used in psychology and ergonomics. It describes the fit between elements of a sociotechnical system which is a prerequisite to successfully cooperate towards a common goal. For at least three decades, cognitive compatibility is of increasing importance. It describes the fit of externally presented information, information processing, and the required motor action. However, with increasing system complexity, probability for incompatibility increases, too, leading to time losses, errors and overall degraded performance. The elimination of cognitive incompatibilities through ergonomic measures at the workplace requires a lot of creativity and effort. Using practical examples from mixed-model assembly, improved information management and the use of informational assistance systems are discussed as promising ergonomic approaches. The ultimate goal is to avoid cognitive overload, for example in part picking or assembly tools choosing. To find a fit between externally mediated work instructions via displays and the subjectively used internal models and competencies is a challenging task. Only if this fit is given the system is perceived as beneficial. To achieve this, the assistance system should be configurable to fit individual needs as far as possible. Successful system design requires early participation and comprehensive integration of the assistance systems into the existing IT infrastructure.Practical relevance: Varied manual assembly requires a high degree of cognitive work. A rise in complexity of the assembly task increases the risk that cognitive incompatibility and thus cognitive overload will occur more frequently. It is shown that such unhealthy conditions can be countered by better information presentation and by the use of individually adaptable informational assistance systems.

A Die-Level, Replaceable Integrated Chiplet (PINCH) Assembly Using a Socketed Platform, Compressible MicroInterconnects, and Self-Alignment

This paper presents a novel die-level, rePlaceable INtegrated CHiplet (PINCH) assembly using a socketed platform. To enable the replaceability of this tightly-integrated system, the appropriate set of enabling technologies is required. To this end, the PINCH assembly incorporates a non-permanent, off-chip interconnection system via Compressible MicroInterconnects (CMIs) and a non-permanent self-alignment technology using Positive-Self-Alignment Structures (PSAS) in a PSAS-to-PSAS configuration. As the PSAS-to-PSAS self-alignment technology is substrate agnostic, the PINCH assembly can also incorporate heterogeneous integrated systems. This paper discusses this PINCH assembly and it provides a design analysis for building this socketed system. The fabrication of all the components of the PINCH assembly (e.g., chiplet, interposer) is then performed, followed by a manual assembly process using the PSAS-to-PSAS self-alignment technology. Four-point resistance of the 150 μm pitch and 200 μm pitch CMIs are then measured after the socket is secured into the socket base of the PINCH assembly.

Curved and Large Format Insulating Glass in Architectural Applications

To produce insulating glass that meets today's rising demands for thermal insulation, aesthetics and transparency, the traditional way of producing such insulating glass becomes more and more difficult if not impossible, as more and more large formats and complex curved insulating glass is being requested. Standard insulating glass production lines are not designed to handle insulating glass in complete sheet sizes of 6.3 × 3.21 m. Yet, the requirements of designers and constructions that want to fulfil the rising demand for maximum transparency and higher needs for thermal insulation even request glass sheet sizes of sometimes more than 15 m to give architectural projects the openness and the least frame and seal interruptions that are possible. Larger glass sizes also means thicker glass. As it is impossible to process curved glass fully automatically, and even more out of scope to process this to insulating glass, this has to be done in manual assembly operation and with a lot of hands on experience. The larger the glass panes become, the more difficult it gets to find appropriate automatic processing machinery that can handle these large sheets. But even more one wants at least the highest possible machine support on large format plane or flat insulating glass. This is now possible. For both applications, large format insulating glass and curved insulating glass that also can become fairly large and heavy, there are now edge seal systems in place and proven in both artificial aging and in real service life experience available. This even make the production of complex and unordinary insulating glass easy. These insulating glass systems define new borders of sizes and design. A number of actual and recent projects in the presentation show what is possible and lead the way to further stress the borders of the possibilities in architectural design.

A Substrate-Agnostic, Submicrometer PSAS-to-PSAS Self-Alignment Technology for Heterogeneous Integration

This paper presents a novel substrate-agnostic self-alignment technology with submicron alignment accuracy targeted for heterogeneous integrated systems. This self-alignment technology, referred to as PSAS-to-PSAS self-alignment, incorporates positive self-alignment structures (PSAS) or reflowed patterned photoresist structures that couple with other PSAS to achieve submicron alignment accuracy without the need for an advanced placement tool. The PSAS-to-PSAS self-alignment technology is non-substrate invasive and hence does not consume any substrate real estate. These PSAS structures can also be fabricated atop any material substrate; hence, this technology is suitable for heterogeneous integrated systems where a variety of different material substrates may be incorporated. This paper discusses this PSAS-to-PSAS self-alignment technology and it provides a design analysis relating the inter-substrate gap between aligning substrates and the dimensions of the PSAS structures. The fabrication of the PSAS on two separate substrates is then performed, followed by a manual assembly process to couple the PSAS on one substrate to the PSAS on the other aligning substrate. Vernier scales are incorporated on these substrates to measure the misalignment between said substrates; submicron alignment is recorded. As these structures may also enable a non-permanent system configuration, alignment repeatability measurements were also performed.

Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly

Molds are still assembled manually because of frequent demand changes and the requirement for comprehensive knowledge related to their high flexibility and adaptability in operation. We propose the application of human-robot collaboration (HRC) systems to improve manual mold assembly. In the existing HRC systems, humans control the execution of robot tasks, and this causes delays in the operation. Therefore, we propose a status recognition system to enable the early execution of robot tasks without human control during the HRC mold assembly operation. First, we decompose the mold assembly operation into task and sub-tasks, and define the actions representing the status of sub-tasks. Second, we develop status recognition based on parts, tools, and actions using a pre-trained YOLOv5 model, a one-stage object detection model. We compared four YOLOv5 models with and without a freezing backbone. The YOLOv5l model without a freezing backbone gave the optimal performance with a mean average precision (mAP) value of 84.8% and an inference time of 0.271 s. Given the success of the status recognition, we simulated the mold assembly operations in the HRC environment and reduced the assembly time by 7.84%. This study improves the sustainability of the mold assembly from the point of view of human safety, with reductions in human workload and assembly time.

Micromachined two-axis water-immersible scanning mirror using torsional and bending hinges

We report a two-axis water-immersible microscanning mirror using torsional and bending biaxially oriented polyethylene terephthalate hinges. Two different designs based on a four- or single-coil electromagnetic actuator are investigated. A micromachining-based fabrication process is developed to enable high patterning resolution and alignment accuracy and to reduce the amount of manual assembly. With a torsional hinge, the fast axis has a resonance frequency of 300 to 500 Hz in air and 200 to 400 Hz in water. With a bending hinge, the slow axis has a resonance frequency of 60 to 70 Hz in air and 20 to 40 Hz in water. 2D B-scan and 3D volumetric ultrasound microscopy are demonstrated using the hybrid-hinge scanning mirror. The ability of scanning the slow axis at DC or very low frequencies allows a dense raster scanning pattern to be formed for improving both the imaging resolution and field of view.

Decision-Making Framework for Implementing Safer Human-Robot Collaboration Workstations: System Dynamics Modeling

Human-Robot Collaboration (HRC) systems are often implemented seeking for reducing risk of Work-related Musculoskeletal Disorders (WMSD) development and increasing productivity. The challenge is to successfully implement an industrial HRC to manage those factors, considering that non-linear behaviors of complex systems can produce counterintuitive effects. Therefore, the aim of this study was to design a decision-making framework considering the key ergonomic methods and using a computational model for simulations. It considered the main systemic influences when implementing a collaborative robot (cobot) into a production system and simulated scenarios of productivity and WMSD risk. In order to verify whether the computational model for simulating scenarios would be useful in the framework, a case study in a manual assembly workstation was conducted. The results show that both cycle time and WMSD risk depend on the Level of Collaboration (LoC). The proposed framework helps deciding which cobot to implement in a context of industrial assembly process. System dynamics were used to understand the actual behavior of all factors and to predict scenarios. Finally, the framework presented a clear roadmap for the future development of an industrial HRC system, drastically reducing risk management in decision-making.

A digital twin-driven human–robot collaborative assembly-commissioning method for complex products

The process of complex product assembly-commissioning has the characteristics of high flexibility and firm dynamics. To overcome the drawbacks of manual assembly, deploying automated and intelligent techniques can greatly boost efficiency, improve flexibility, and enhance the quality control. The human–robot collaborative (HRC) technology combines the advantages of human capabilities and the efficiency and precision of robots. However, current HRC technology lacks of perception and cognitive ability, especially in dynamic environments. Therefore, this paper proposed a digital twin-driven HRC assembly-commissioning framework. In this framework, a virtual-physical mapping environment for HRC is constructed. In order to improve the cognitive ability of robot units to tasks, a motion intention recognition approach is proposed which integrates the feature of part into human joint sequences. To improve the adaptability of the robot unit to tasks, the assembly-commissioning task knowledge graph is developed to extract the action sequence of the robot unit in a timely manner. Moreover, the deep deterministic policy gradient (DDPG) is used to adaptively adjust the robot unit movement path in the process of assembly-commissioning. Finally, the effectiveness of the proposed method is verified by taking a particular type of automobile generator as a case study product.

Labour productivity in mixed-model manual assembly 4.0

Manual assembly lines productivity is threatened by the increased complexity brought by mass customisation demand trends. Industry 4.0 offers potential solutions to address this situation, but the methodology to implement it is still a subject of study. As a preliminary step, this article aims to identify the dominant factors affecting the Key Performance Indicators of mixed-model assembly lines. To do so, parametric and discrete-events simulation models were developed, and Design of Experiments techniques were used. The results show that the key drivers for assembly line performance are number of work stations and batch size, and that increasing the work content ratio of the products assembled does not interact negatively with other factors. The results presented here pave the way for developing Industry 4.0 projects that address specifically the most relevant factors that affect assembly lines performance.

Applying a fusion of wearable sensors and a cognitive inspired architecture to real-time ergonomics analysis of manual assembly tasks

High value manufacturing systems still require ergonomically intensive manual activities. Examples include the aerospace industry where the fitting of pipes and wiring into confined spaces in aircraft wings is still a manual operation. In these environments, workers are subjected to ergonomically awkward forces and postures for long periods of time. This leads to musculoskeletal injuries that severely limit the output of a shopfloor leading to loss of productivity. The use of tools such as wearable sensors could provide a way to track the ergonomics of workers in real time. However, an information processing architecture is required in order to ensure that data is processed in real time and in a manner that meaningful action points are retrieved for use by workers.In this work, based on the Adaptive Control of Thought—Rational (ACT-R) cognitive framework, we propose a Cognitive Architecture for Wearable Sensors (CAWES); a wearable sensor system and cognitive architecture that is capable of taking data streams from multiple wearable sensors on a worker’s body and fusing them to enable digitisation, tracking and analysis of human ergonomics in real time on a shopfloor. Furthermore, through tactile feedback, the architecture is able to inform workers in real time when ergonomics rules are broken. The architecture is validated through the use of an aerospace case study undertaken in laboratory conditions. The results from the validation are encouraging and in the future, further tests will be performed in an actual working environment.