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The purpose of this paper is to analyse the use of a product‐oriented layout and a work‐cell strategy in order to maximise efficiency. These two categories of layout strategies are discussed separately, and are then used collectively in an analysis of the company. The aim is to understand how improvements on layout design could positively impact the future efficiency of the case study company.

A model was developed and measured using 26 weeks of data between the fourth quarter of 2009 and the first quarter of 2010 during layout transformations at the case study company based in upstate New York. The model compared variables such as the distance traveled to retrieve parts, average daily output of engines, labour cost per unit produced, and the amount of time the engine remains in each cell; the aim of which is to increase the efficiency of the facility.

The findings indicate that there is a strong correlation between the variables improved at both the cell‐structures and the product‐structures of the facility and the overall efficiency of the manufacturing facility itself. The results also show that an overall higher efficiency allows for the facility to handle much larger workloads and also drives down both short‐run and long‐run costs. The outcomes also allow for a suggestive redesign of the facility in order to further maximise efficiency. However, it was found that the amount of time a product remains in each cell on the assembly line does not have an effect on the overall output of diesel engines.

Various studies have been conducted focusing on the “facility layout problem,” yet thorough analyses of the redesigning of layout in regards to efficiency are not as available. Instead, an understanding of the topic was derived through sources focusing on the specificities of manufacturing layout.

This paper describes layout efficiency through redesigns and layout using work‐cells in a product‐oriented environment. This study would be useful to manufacturers having low variability in their product and having the ability to use work‐cell layout within their facility.

In the vast majority of the individual robot installations, the robot arm is just one piece of a complex puzzle of components, such as grippers, jigs or external axis, that together compose an industrial robotic cell. The success of such installations is very dependent not only on the selection of such components but also on the layout and design of the final robotic cell, which are the main tasks of the system integrators. Consequently, successful robot installations are often empirical tasks owing to the high number of experimental combinations that could lead to exhaustive and time-consuming testing approaches.

A newly developed optimized technique to deal with automatic planning and design of robotic systems is proposed and tested in this paper.

The application of a genetic-based algorithm achieved optimal results in short time frames and improved the design of robotic work cells. Here, the authors show that a multi-layer optimization approach, which can be validated using a robotic tool, is able to help with the design of robotic systems.

The usage of the proposed approach can be valuable to industrial corporations, as it allows for improved workflows, maximization of available robotic operations and improvement of efficiency.

To date, robotic solutions lack flexibility to cope with the demanding industrial environments. The results presented here formalize a new flexible and modular approach, which can provide optimal solutions throughout the different stages of design and execution control of any work cell.

The purpose of this paper is to present an improved concept of software‐based laboratory exercises, namely a Virtual Laboratory for Engineering Sciences (VLES).

The implementation of distance learning and e‐learning in engineering sciences (such as Mechanical and Electrical Engineering) is still far behind current practice in narrative disciplines (Economics, Management, etc.). This is because education in technical disciplines requires laboratory exercises, providing skill‐acquisition and hands‐on experience. In order to overcome this problem for distance‐learning developers and practitioners, a new modular and hierarchically organized approach is needed.

The concept involves simulation models to emulate system dynamics, full virtual reality to provide visualization, advanced social‐clubbing to ensure proper communication, and an AI tutor to supervise the lab work. Its modularity and hierarchical organization offer the possibility of applying the concept to practically any engineering field: a higher level provides the general framework – it considers lab workplaces as objects regardless of the technical field they come from, and provides communication and supervision – while the lower level deals with particular workplaces. An improved student's motivation is expected.

The proposed concept aims rather high, thus making the work truly challenging. With the current level of information and communication technologies, some of the required features can only be achieved with difficulty; however, the rapid growth of the relevant technologies supports the eventual practicality of the concept. This paper is not intended to present any final results, solutions, or experience. The idea is to promote the concept, identify problems, propose guidelines, and possibly open a discussion.

The purpose of this paper is to visualize the prioritization among essential factors of cellular manufacturing system (CMS) implementation using the analytic hierarchy process (AHP) and analytic network process (ANP) methods.

Based on literature review, 4 enabler dimensions and 17 CM factors were identified which were validated by experts from academia and industry. Then, AHP and ANP models are proposed in evaluating CMS implementation dimensions and factors. The results are validated using sensitivity analysis.

These models give firms a straightforward and simple to utilize way to deal with CMS efficiently. The two strategies were appeared to be powerful in choosing a strategy for CMS implementation. The two strategies brought about nearly similar outcomes. Both methods consider the particular necessities of the organization through its own accessible ability.

The techniques exhibited in this paper can be utilized by a wide range of organizations for adopting CMS that have a higher impact on performance and thus overall productivity. The two techniques are explained in a step-by-step approach for easier adoption by practitioners.

The strength of the present study is that it is one of the first few to be conducted in perspective for CM implementation factors analysis.

This paper provides a synthesis of world class manufacturing literature by identifying eight critical factors of world class status in a manufacturing environment. These factors can be used individually or collectively to assess a profile or organization‐wide world class manufacturing implementation practices. Researchers can use the critical factors to build theories and models that relate these factors to world class status and an organization's relative position to others in the same environment. Decision makers can isolate the critical factors that are necessary for world class implementation.

The purpose of this paper is to present a novel automatic planning and coordinated control method of redundant dual‐arm space robot for inner space‐station operation based on multiple sensors information by stages.

In order to improve the coordinated control capability of dual‐arm robot system, a four‐layer hierarchical control structure is designed based on the theory of centralization and decentralization. At the high‐level planning of dual‐arm system, a task decomposition strategy based on task knowledge and a task allocation strategy in terms of the robotic capability are proposed, respectively. Moreover, a control method by stages based on the information of multiple sensors is introduced to object recognition, task planning, path planning and trajectory planning. Finally, a 3D simulation and experiment of screwing nut and bolt are implemented on a dual‐arm robot system, and the feasibility and applicability of this control strategy are verified.

The automatic planning can be accomplished by means of sensors information by stages, and by this method, the autonomy and intelligence of dual‐arm space robot system can be further improved.

A new automatic planning strategy integrated with multiple sensors information by stages is proposed, and can be implemented on a dual‐arm robot system for inner space‐station operations. This method specializes in heterogeneous dual‐arm robot system.

A task decomposition strategy based on task knowledge and a task allocation strategy in terms of the robotic capability are proposed, respectively. Moreover, a control method by stages based on the information of multiple sensors is introduced to object recognition, task planning, path planning and trajectory planning of dual‐arm robot system.

The purpose of this paper is twofold: first, a case study on applying lean principles in manufacturing operations to redesign and optimize an electronic device assembly process and its impact on performance and second, introducing cardboard prototyping as a Kaizen tool offering a novel approach to testing and simulating improvement scenarios.

The study employs value stream mapping, root cause analysis, and brainstorming tools to identify root causes of poor performance, followed by deploying a Kaizen event to redesign and optimize an electronic device assembly process. Using physical models, bottlenecks and opportunities for improvement were identified by the Kaizen approach at the workstations and assembly lines, enabling the testing of various scenarios and ideas. Changes in lead times, throughput, work in process inventory and assembly performance were analyzed and documented.

Pre- and post-improvement measures are provided to demonstrate the impact of the Kaizen event on the performance of the assembly cell. The study reveals that implementing lean tools and techniques reduced costs and increased throughput by reducing assembly cycle times, manufacturing lead time, space utilization, labor overtime and work-in-process inventory requirements.

This paper adds a new dimension to applying the Kaizen methodology in manufacturing processes by introducing cardboard prototyping, which offers a novel way of testing and simulating different scenarios for improvement. The paper describes the process implementation in detail, including the techniques and data utilized to improve the process.

Presents an approach to improved performance and flexibility in industrial robotics by means of sensor integration and feedback control in task‐level programming and task execution. Also presents feasibility studies in support of the ideas. Discusses some solutions to the problem using six degrees of freedom force control together with the ABB S4CPlus system as an illustrative example. Consider various problems in the design of an open sensor interface for industrial robotics and discusses possible solutions. Finally, presents experimental results from industrial force controlled grinding.