Techniques to improve the shear strength of impacted bone graft: the effect of particle size and washing of the graft.

BACKGROUND: When fresh morselized graft is compacted, as in impaction bone-grafting for revision hip surgery, fat and marrow fluid is either exuded or trapped in the voids between particles. We hypothesized that the presence of incompressible fluid damps and resists compressive forces during impaction and prevents the graft particles from moving into a closer formation, thus reducing the graft strength. In addition, viscous fluid such as fat may act as an interparticle lubricant, thus reducing the interlocking of the particles. METHODS: We performed mechanical shear testing in the laboratory with use of fresh-frozen human femoral-head allografts that had been passed through different orthopaedic bone mills to produce graft of differing particle-size distributions (grading). RESULTS: After compaction of fresh graft, fat and marrow fluid continued to escape on application of normal loads. Washed graft, however, had little lubricating fluid and better contact between the particles, increasing the shear resistance. On mechanical testing, washed graft was significantly (p < 0.001) more resistant to shearing forces than fresh graft was. This feature was consistent for different bone mills that produced graft of different particle-size distributions and shear strengths. CONCLUSIONS: Removal of fat and marrow fluid from milled human allograft by washing the graft allows the production of stronger compacted graft that is more resistant to shear, which is the usual mode of failure. Further research into the optimum grading of particle sizes from bone mills is required.

Techniques to improve the shear strength of impacted bone graft: The effect of particle size and washing of the graft

Background: When fresh morselized graft is compacted, as in impaction bone-grafting for revision hip surgery, fat and marrow fluid is either exuded or trapped in the voids between particles. We hypothesized that the presence of incompressible fluid damps and resists compressive forces during impaction and prevents the graft particles from moving into a closer formation, thus reducing the graft strength. In addition, viscous fluid such as fat may act as an interparticle lubricant, thus reducing the interlocking of the particles. Methods: We performed mechanical shear testing in the laboratory with use of fresh-frozen human femoral-head allografts that had been passed through different orthopaedic bone mills to produce graft of differing particle-size distributions (grading). Results: After compaction of fresh graft, fat and marrow fluid continued to escape on application of normal loads. Washed graft, however, had little lubricating fluid and better contact between the particles, increasing the shear resistance. On mechanical testing, washed graft was significantly (p < 0.001) more resistant to shearing forces than fresh graft was. This feature was consistent for different bone mills that produced graft of different particle-size distributions and shear strengths. Conclusions: Removal of fat and marrow fluid from milled human allograft by washing the graft allows the production of stronger compacted graft that is more resistant to shear, which is the usual mode of failure. Further research into the optimum grading of particle sizes from bone mills is required. Clinical Relevance: Understanding the mechanical properties of milled human allograft is important when impaction grafting is used for mechanical support. A simple means of improving the mechanical strength of graft produced by currently available bone mills, including an intraoperative washing technique, is described.

Data-based robust multiobjective optimization of interconnected processes: energy efficiency case study in papermaking.

Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.

Data-based robust multiobjective optimization of interconnected processes: Energy efficiency case study in papermaking

Reducing energy consumption is a major challenge for energy-intensive industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of optimized operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method. © 2006 IEEE.