Effect of cognitive automation in a material handling system on manufacturing flexibility

Choe, Pilsung; Tew, Jeffrey D.; Tong, Songzhen

doi:10.1016/j.ijpe.2015.01.018

Cited by 36 publications

(16 citation statements)

References 34 publications

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“…The second experiment is inspired by challenges for material handling robots in more flexible and therefore less predictable manufacturing processes [13]. Such robots play a central role in industrial assembly lines by transporting parts or subassembly parts between workstations.…”

Section: Value-based Decision Making In a Materials Handling Taskmentioning

confidence: 99%

A neural integrator model for planning and value-based decision making of a robotics assistant

Wojtak

Ferreira

Vicente

et al. 2020

Neural Comput & Applic

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Modern manufacturing and assembly environments are characterized by a high variability in the built process which challenges human-robot cooperation. To reduce the cognitive workload of the operator, the robot should not only be able to learn from experience but also to plan and decide autonomously. Here, we present an approach based on Dynamic Neural Fields that applies brain-like computations to endow a robot with these cognitive functions. A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations. The decision to act is modeled by a competitive dynamics between neural populations linked to different motor behaviors. They receive the persistent activation pattern of the integrators as input. In the first experiment, a robot learns rapidly by observation the sequential order of object transfers between an assistant and an operator to subsequently substitute the assistant in the joint task. The results show that the robot is able to proactively plan the series of handovers in the correct order. In the second experiment, a mobile robot searches at two different workbenches for a specific object to deliver it to an operator. The object may appear at the two locations in a certain time period with independent probabilities unknown to the robot. The trial-by-trial decision under uncertainty is biased by the accumulated evidence of past successes and choices. The choice behavior over

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Section: Value-based Decision Making In a Materials Handling Taskmentioning

confidence: 99%

A neural integrator model for planning and value-based decision making of a robotics assistant

Wojtak

Ferreira

Vicente

et al. 2020

Neural Comput & Applic

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“…It is neither possible, nor convenient, to totally eliminating human contribution to processes, even when a high degree of automation is pursued, such as in manufacturing industries [24]. Industries with high production volumes may consider machines and computers as faster and more reliable than humans in leading automatic operations.…”

Section: Human Factors and Ergonomics In Industrymentioning

confidence: 99%

Managing Human Factors to Reduce Organisational Risk in Industry

Carpitella

Carpitella²,

Certa

et al. 2018

MCA

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Human factors are intrinsically involved at virtually any level of most industrial/business activities, and may be responsible for several accidents and incidents, if not correctly identified and managed. Focusing on the significance of human behaviour in industry, this article proposes a multi-criteria decision-making (MCDM)-based approach to support organizational risk assessment in industrial environments. The decision-making trial and evaluation laboratory (DEMATEL) method is proposed as a mathematical framework to evaluate mutual relationships within a set of human factors involved in industrial processes, with the aim of highlighting priorities of intervention. A case study related to a manufacturing process of a real-world winery is presented, and the proposed approach is applied to rank human factors resulting from a previous organisational risk evaluation from which suitable inference engines may be developed to better support risk management.

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“…Many aspects exist on the MHS design. Most of the research works mainly focus on some MHS sub-problems, such as the optimization of material ow [7], the de nition of the appropriate Level of Automation (LoA) [3], or the Material Handling Equipment (MHE) Selection Problem [13]. However, the design of the MHS has to be done through solving all these interdependent sub-problems.…”

Section: Introductionmentioning

confidence: 99%

Field studies analysis for new Material Handling System Design approach

Soufi¹,

David²,

Yahouni³

2021

Preprint

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Mastering the Material Handling System (MHS) is still a crucial issue, according to Hellmann et al the costs of Material Handling activities ranges from 15–70% of total manufacturing costs depending on the kind of production [6]. The introduction of industry 4.0 technologies for Material Handling, is renewing the decisions to take in the design of MHS. The practices in MHS design and management are very diversified in industry and no consensus clearly exist on how efficiently designing such system. This paper discusses the need to understand the relation between the different aspects of Material Handling System design, and propose to identify the key challenges to be addressed. Through field studies that were conducted with five distinct companies and an analysis of the literature, five MHS aspects are examined; Material Handling Activities, Material Handling and automation, Material Handling Control System, Material Handling Equipment Selection, and Material Handling System. Based on the results of the analysis, a cross analysis is performed to identify the differences and the common patterns between the literature and the field study. As a result, research directions for new MHS design approach are proposed. It is composed of four main challenges; Material handling specifications, MHE selection, MHE deployment, and MHS analysis. Each challenge is linked to other potential subjects.

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Effect of cognitive automation in a material handling system on manufacturing flexibility

Cited by 36 publications

References 34 publications

A neural integrator model for planning and value-based decision making of a robotics assistant

A neural integrator model for planning and value-based decision making of a robotics assistant

Managing Human Factors to Reduce Organisational Risk in Industry

Field studies analysis for new Material Handling System Design approach

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