scholarly journals Real Time Implementation of Learning-Forgetting Models for Cycle Time Predictions of Manual Assembly Tasks after a Break

2020 ◽  
Vol 12 (14) ◽  
pp. 5543
Author(s):  
Steven Hoedt ◽  
Arno Claeys ◽  
El-Houssaine Aghezzaf ◽  
Johannes Cottyn
Keyword(s):  
Real Time ◽  
Cycle Time ◽  
Mixed Model ◽  
Manufacturing Companies ◽  
Manual Assembly ◽  
Time Prediction ◽  
Curve Model ◽  
Assembly Operations ◽  
Assembly Tasks ◽  
Mixed Model Assembly

Industry 4.0 provides a tremendous potential of data from the work floor. For manufacturing companies, these data can be very useful in order to support assembly operators. In literature, a lot of contributions can be found that present models to describe both the learning and forgetting effect of manual assembly operations. In this study, different existing models were compared in order to predict the cycle time after a break. As these models are not created for a real time prediction purpose, some adaptations are presented in order to improve the robustness and efficiency of the models. Results show that the MLFCM (modified learn-forget curve model) and the PID (power integration diffusion) model have the greatest potential. Further research will be performed to test both models and implement contextual factors. In addition, since these models only consider one fixed repetitive task, they don’t target mixed-model assembly operations. The learning and forgetting effect that executing each assembly task has on the other task executions differs based on the job similarity between tasks. Further research opportunities to implement this job similarity are listed.

A Launching Pattern in a Mixed Model Assembly Line

2014 ◽  
Vol 875-877 ◽  
pp. 1160-1164
Author(s):  
Suksan Prombanpong ◽  
S. Somboonsilp
Keyword(s):  
Cycle Time ◽  
Assembly Line ◽  
Mixed Model ◽  
Production Plan ◽  
Fixed Rate ◽  
Manual Assembly ◽  
Production Requirement ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly ◽  
Production Demand

This paper aims to sequence the production plan of a condenser unit of an air-conditioned assembly line, which is a manual assembly line. In this case there are six different models with different required production rate that must be assembled simultaneously. The assembly line consists of twenty four workstations with thirty four workers. Due to the fact that the cycle time of each condenser model is varied. Thus, it is difficult to design a launching pattern so that the production requirement of each model is exactly met at each production hour. In turn, the production demand of some models can be satisfied while other models cannot be met. In order to solve this problem, the fixed rate launching algorithm is applied and the result is considered satisfactory.

Effects of Non-Geometric Features and Incentive Schemes on Manual Assembly of System Variants: An Experimental Study

2007 ◽  
Author(s):  
Ramaprasad E. Lakshminarayana ◽  
Shun Takai
Keyword(s):  
Assembly Line ◽  
Mixed Model ◽  
Cognitive Effects ◽  
Automated Assembly ◽  
Design Guideline ◽  
Manual Assembly ◽  
Incentive Schemes ◽  
Assembly Operations ◽  
Mixed Model Assembly

Although numerous firms have been shifting toward automated assembly, most still rely on manual assembly when complex assembly operation is required for large-scaled systems. Furthermore, because firms design variants of a system to satisfy diverse customer needs, they may manufacture these system variants in the same assembly line. This type of operation, called mixed model assembly, may improve the utilization of existing manufacturing facilities; however, it may also increase assembly errors due to interchanging geometrically similar parts between system variants. Design for Assembly (DFA) is a design guideline that assists engineers in designing systems that are easier to assemble. However, because DFA guidelines group geometrically similar parts in the same part category, it may be impossible to distinguish geometrically similar but functionally different parts (modules) used in different systems. This paper proposes experimenting how cognitive effects of non-geometric part features influence the productivity and quality in mixed model assembly operations. Furthermore, because the productivity and quality of manual assembly may be influenced by the motivation of operators, this paper examines how productivity and quality may be influenced by different incentive schemes.

scholarly journals Edge Computing Driven Scene-aware Intelligent Augmented Reality for Manual Assembly

2021 ◽  
Author(s):  
Mingyu Fu ◽  
Wei Fang ◽  
Shan Gao ◽  
Jianhao Hong ◽  
Yizhou Chen
Keyword(s):  
Augmented Reality ◽  
Cognitive Load ◽  
High Efficiency ◽  
Computing System ◽  
Edge Computing ◽  
Assembly Process ◽  
Manual Assembly ◽  
Worker Center ◽  
Assembly Operations ◽  
Assembly Tasks

Abstract Wearable augmented reality (AR) can superimpose virtual models or annotation on real scenes, and which can be utilized in assembly tasks and resulted in high-efficiency and error-avoided manual operations. Nevertheless, most of existing AR-aided assembly operations are based on the predefined visual instruction step-by-step, lacking scene-aware generation for the assembly assistance. To facilitate a friendly AR-aided assembly process, this paper proposed an Edge Computing driven Scene-aware Intelligent AR Assembly (EC-SIARA) system, and smart and worker-centered assistance is available to provide intuitive visual guidance with less cognitive load. In beginning, the connection between the wearable AR glasses and edge computing system is established, which can alleviate the computation burden for the resource-constraint wearable AR glasses, resulting in a high-efficiency deep learning module for scene awareness during the manual assembly process. And then, based on context understanding of the current assembly status, the corresponding augmented instructions can be triggered accordingly, avoiding the operator’s cognitive load to strictly follow the predefined procedure. Finally, quantitative and qualitative experiments are carried out to evaluate the EC-SIARA system, and experimental results show that the proposed method can realize a worker-center AR assembly process, which can improve the assembly efficiency and reduce the occurrence of assembly errors effectively.

Labour Productivity in Mixed-model Manual Assembly Changeovers

2020 ◽  
Vol 8 ◽  
Author(s):  
Adrian Miqueo ◽  
Marta Torralba ◽  
José Antonio Yagüe
Keyword(s):  
Industry 4.0 ◽  
Parametric Analysis ◽  
Mixed Model ◽  
Labour Productivity ◽  
Assembly Lines ◽  
Manual Assembly ◽  
Productivity Losses ◽  
Mixed Model Assembly Lines ◽  
Implementation Methodology ◽  
Mixed Model Assembly

In a demand context of mass customisation, Industry 4.0 technologies open new possibilities of increased productivity and flexibility for mixed-model assembly lines. Analytical and parametric analysis is used to better understand the productivity losses of model changeovers as a preliminary step to develop an Assembly 4.0 implementation methodology.

Evolution Balancing of Mixed Model-Assembly Line Considering Task Reassignment

2018 ◽  
Vol 17 (01) ◽  
pp. 3-21
Author(s):  
Y. M. Wu ◽  
L. ZH. Dai ◽  
L. F. Luo
Keyword(s):  
Information Exchange ◽  
Production Efficiency ◽  
Assembly Line ◽  
Mixed Model ◽  
Market Demand ◽  
Product Evolution ◽  
Operating Level ◽  
Assembly Tasks ◽  
Mixed Model Assembly Line ◽  
Mixed Model Assembly

Market demand and technological progress drive continuous product evolution, upgrade and innovation, which necessitate readjustment and evolution balancing of the mixed model assembly line (MMAL) for improving production efficiency. In the evolution process of MMAL, the rational matching between difficulty of assembly tasks and operating level are mainly considered, the mathematical model of evolution balancing optimization for MMAL is established, and an improved particle swarm optimization algorithm (IPSO) based on leapfrog algorithm is designed. In the process of optimization, the single population is divided into several subgroups for searching, information exchange between species is executed to get better particles and the strategy of returning to the beginning is introduced, in which particle diversity and global search capability are increased and improved, respectively. Finally, the effectiveness and feasibility of the method were validated by evolution balancing planning of MMAL.

scholarly journals Modeling of RFID-Enabled Real-Time Manufacturing Execution System in Mixed-Model Assembly Lines

2015 ◽  
Vol 2015 ◽  
pp. 1-15
Author(s):  
Zhixin Yang ◽  
Wei Xu ◽  
Pak-Kin Wong ◽  
Xianbo Wang
Keyword(s):  
Real Time ◽  
Production Planning ◽  
Production Management ◽  
Mixed Model ◽  
Manufacturing Execution System ◽  
Assembly Lines ◽  
Safety Stock ◽  
Mixed Model Assembly Lines ◽  
Mixed Model Assembly ◽  
Manufacturing Execution

To quickly respond to the diverse product demands, mixed-model assembly lines are well adopted in discrete manufacturing industries. Besides the complexity in material distribution, mixed-model assembly involves a variety of components, different process plans and fast production changes, which greatly increase the difficulty for agile production management. Aiming at breaking through the bottlenecks in existing production management, a novel RFID-enabled manufacturing execution system (MES), which is featured with real-time and wireless information interaction capability, is proposed to identify various manufacturing objects including WIPs, tools, and operators, etc., and to trace their movements throughout the production processes. However, being subject to the constraints in terms of safety stock, machine assignment, setup, and scheduling requirements, the optimization of RFID-enabled MES model for production planning and scheduling issues is a NP-hard problem. A new heuristical generalized Lagrangian decomposition approach has been proposed for model optimization, which decomposes the model into three subproblems: computation of optimal configuration of RFID senor networks, optimization of production planning subjected to machine setup cost and safety stock constraints, and optimization of scheduling for minimized overtime. RFID signal processing methods that could solve unreliable, redundant, and missing tag events are also described in detail. The model validity is discussed through algorithm analysis and verified through numerical simulation. The proposed design scheme has important reference value for the applications of RFID in multiple manufacturing fields, and also lays a vital research foundation to leverage digital and networked manufacturing system towards intelligence.

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