Robotics and Computer-integrated Manufacturing 

About: Robotics and Computer-integrated Manufacturing is an academic journal. The journal publishes majorly in the area(s): Robot & Machining. It has an ISSN identifier of 0736-5845. Over the lifetime, 2515 publication(s) have been published receiving 74983 citation(s).

From cloud computing to cloud manufacturing

Abstract: Cloud computing is changing the way industries and enterprises do their businesses in that dynamically scalable and virtualized resources are provided as a service over the Internet. This model creates a brand new opportunity for enterprises. In this paper, some of the essential features of cloud computing are briefly discussed with regard to the end-users, enterprises that use the cloud as a platform, and cloud providers themselves. Cloud computing is emerging as one of the major enablers for the manufacturing industry; it can transform the traditional manufacturing business model, help it to align product innovation with business strategy, and create intelligent factory networks that encourage effective collaboration. Two types of cloud computing adoptions in the manufacturing sector have been suggested, manufacturing with direct adoption of cloud computing technologies and cloud manufacturing-the manufacturing version of cloud computing. Cloud computing has been in some of key areas of manufacturing such as IT, pay-as-you-go business models, production scaling up and down per demand, and flexibility in deploying and customizing solutions. In cloud manufacturing, distributed resources are encapsulated into cloud services and managed in a centralized way. Clients can use cloud services according to their requirements. Cloud users can request services ranging from product design, manufacturing, testing, management, and all other stages of a product life cycle.

Preparation of medical devices by solid free-form fabrication methods

Abstract: Solid free-form techniques for making medical devices for controlled release of bioactive agent and implantation and growth of cells are described using computer aided design. Examples of SFF methods include stereo-lithography (SLA), selective laser sintering (SLS), ballistic particle manufacturing (BPM), fusion deposition modeling (FDM), and three dimensional printing (3DP). The macrostructure and porosity of the device can be manipulated by controlling printing parameters. Most importantly, these features can be designed and tailored using computer assisted design (CAD) for individual patients to optimize therapy.

Multiple-attribute decision making methods for plant layout design problem

Abstract: The layout design problem is a strategic issue and has a significant impact on the efficiency of a manufacturing system. Much of the existing layout design literature that uses a surrogate function for flow distance or for simplified objectives may be entrapped into local optimum; and subsequently lead to a poor layout design due to the multiple-attribute decision making (MADM) nature of a layout design decision. The present study explores the use of MADM approaches in solving a layout design problem. The proposed methodology is illustrated through a practical application from an IC packaging company. Two methods are proposed in solving the case study problem: Technique for order preference by similarity to ideal solution (TOPSIS) and fuzzy TOPSIS. Empirical results showed that the proposed methods are viable approaches in solving a layout design problem. TOPSIS is a viable approach for the case study problem and is suitable for precise value performance ratings. When the performance ratings are vague and imprecise, the fuzzy TOPSIS is a preferred solution method.

Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments

Abstract: In this paper, a neural network modeling approach is presented for the prediction of surface roughness (Ra) in CNC face milling The data used for the training and checking of the networks’ performance derived from experiments conducted on a CNC milling machine according to the principles of Taguchi design of experiments (DoE) method The factors considered in the experiment were the depth of cut, the feed rate per tooth, the cutting speed, the engagement and wear of the cutting tool, the use of cutting fluid and the three components of the cutting force Using feedforward artificial neural networks (ANNs) trained with the Levenberg–Marquardt algorithm, the most influential of the factors were determined, again using DoE principles, and a 5×3×1 ANN based on them was able to predict the surface roughness with a mean squared error equal to 186% and to be consistent throughout the entire range of values

RFID-enabled real-time manufacturing execution system for mass-customization production

Abstract: Mass-customization production (MCP) companies must fight with shop-floor uncertainty and complexity caused by wide variety of product components. The research is motivated by a typical MCP company that has experienced inefficient scheduling due to paper-based identification and manual data collection. This paper presents an RFID-enabled real-time manufacturing execution system (RT-MES). RFID devices are deployed systematically on the shop-floor to track and trace manufacturing objects and collect real-time production data. Disturbances are identified and controlled within RT-MES. Planning and scheduling decisions are more practically and precisely made and executed. Online facilities are provided to visualize and manage real-time dynamics of shop-floor WIP (work-in-progress) items. A case study is reported in a collaborating company which manufactures large-scale and heavy-duty machineries. The efficiency and effectiveness of the proposed RT-MES are evaluated with real-life industrial data for shop-floor production management in terms of workers, machines and materials.