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Article
Publication date: 20 April 2012

Mika Salmi, Jukka Tuomi, Kaija‐Stiina Paloheimo, Roy Björkstrand, Markku Paloheimo, Jari Salo, Risto Kontio, Karri Mesimäki and Antti A. Mäkitie

The purpose of this paper is to develop a workflow for 3D modeling and additive manufacturing (AM) of patient‐specific medical implants. The comprehensive workflow consists of…

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Abstract

Purpose

The purpose of this paper is to develop a workflow for 3D modeling and additive manufacturing (AM) of patient‐specific medical implants. The comprehensive workflow consists of four steps: medical imaging; 3D modelling; additive manufacturing; and clinical application. Implants are used to reconstruct bone damage or defects caused by trauma or disease. Traditionally, implants have been manually bent and shaped, either preoperatively or intraoperatively, with the help of anatomic solid models. The proposed workflow obviates the manual procedure and may result in more accurate and cost‐effective implants.

Design/methodology/approach

A patient‐specific implant was digitally designed to reconstruct a facial bone defect. Several test pieces were additive manufactured from stainless steel and titanium by direct metal laser sintering (DMLS) technology. An additive manufactured titanium EOS Titanium Ti64 ELI reconstruction plate was successfully implanted onto the patient's injured orbital wall.

Findings

This method enables exact fitting of implants to surrounding tissues. Creating implants before surgery improves accuracy, may reduce operation time and decrease patient morbidity, hence improving quality of surgery. By using AM methods it is possible to manufacture a volumetric net structure, which also allows cells and tissues to grow through it to and from surrounding tissues. The net is created from surface and its thickness and hole size are adjustable. The implant can be designed so that its mass is low and therefore sensitivity to hot and cold temperatures is reduced.

Originality/value

The paper describes a novel technique to create patient‐specific reconstruction implants for facial bony defects.

Details

Rapid Prototyping Journal, vol. 18 no. 3
Type: Research Article
ISSN: 1355-2546

Keywords

Available. Open Access. Open Access
Article
Publication date: 16 October 2017

Shenle Pan, Vaggelis Giannikas, Yufei Han, Etta Grover-Silva and Bin Qiao

The development of e-grocery allows people to purchase food online and benefit from home delivery service. Nevertheless, a high rate of failed deliveries due to the customer’s…

20117

Abstract

Purpose

The development of e-grocery allows people to purchase food online and benefit from home delivery service. Nevertheless, a high rate of failed deliveries due to the customer’s absence causes significant loss of logistics efficiency, especially for perishable food. The purpose of this paper is to propose an innovative approach to use customer-related data to optimize e-grocery home delivery. The approach estimates the absence probability of a customer by mining electricity consumption data, in order to improve the success rate of delivery and optimize transportation.

Design/methodology/approach

The methodological approach consists of two stages: a data mining stage that estimates absence probabilities, and an optimization stage to optimize transportation.

Findings

Computational experiments reveal that the proposed approach could reduce the total travel distance by 3-20 percent, and theoretically increase the success rate of first-round delivery approximately by18-26 percent.

Research limitations/implications

The proposed approach combines two attractive research streams on data mining and transportation planning to provide a solution for e-commerce logistics.

Practical implications

This study gives an insight to e-grocery retailers and carriers on how to use customer-related data to improve home delivery effectiveness and efficiency.

Social implications

The proposed approach can be used to reduce environmental footprint generated by freight distribution in a city, and to improve customers’ experience on online shopping.

Originality/value

Being an experimental study, this work demonstrates the effectiveness of data-driven innovative solutions to e-grocery home delivery problem. The paper also provides a methodological approach to this line of research.

Details

Industrial Management & Data Systems, vol. 117 no. 9
Type: Research Article
ISSN: 0263-5577

Keywords

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