PCB assembly line assignment:a genetic algorithm approach

Purpose – This paper sets out to study a production-planning problem for printed circuit board (PCB) assembly. A PCB assembly company may have a number of assembly lines for production of several product types in large volume. Design/methodology/approach – Pure integer linear programming models are formulated for assigning the product types to assembly lines, which is the line assignment problem, with the objective of minimizing the total production cost. In this approach, unrealistic assignment, which was suffered by previous researchers, is avoided by incorporating several constraints into the model. In this paper, a genetic algorithm is developed to solve the line assignment problem. Findings – The procedure of the genetic algorithm to the problem and a numerical example for illustrating the models are provided. It is also proved that the algorithm is effective and efficient in dealing with the problem. Originality/value – This paper studies the line assignment problem arising in a PCB manufacturing company in which the production volume is high.

Estimation of ocular volume from axial length

Background/aims - To determine which biometric parameters provide optimum predictive power for ocular volume. Methods - Sixty-seven adult subjects were scanned with a Siemens 3-T MRI scanner. Mean spherical error (MSE) (D) was measured with a Shin-Nippon autorefractor and a Zeiss IOLMaster used to measure (mm) axial length (AL), anterior chamber depth (ACD) and corneal radius (CR). Total ocular volume (TOV) was calculated from T2-weighted MRIs (voxel size 1.0 mm3) using an automatic voxel counting and shading algorithm. Each MR slice was subsequently edited manually in the axial, sagittal and coronal plane, the latter enabling location of the posterior pole of the crystalline lens and partitioning of TOV into anterior (AV) and posterior volume (PV) regions. Results - Mean values (±SD) for MSE (D), AL (mm), ACD (mm) and CR (mm) were −2.62±3.83, 24.51±1.47, 3.55±0.34 and 7.75±0.28, respectively. Mean values (±SD) for TOV, AV and PV (mm3) were 8168.21±1141.86, 1099.40±139.24 and 7068.82±1134.05, respectively. TOV showed significant correlation with MSE, AL, PV (all p<0.001), CR (p=0.043) and ACD (p=0.024). Bar CR, the correlations were shown to be wholly attributable to variation in PV. Multiple linear regression indicated that the combination of AL and CR provided optimum R2 values of 79.4% for TOV. Conclusion - Clinically useful estimations of ocular volume can be obtained from measurement of AL and CR.

High accuracy mobile robot positioning using external large volume metrology instruments

A method of accurately controlling the position of a mobile robot using an external large volume metrology (LVM) instrument is presented in this article. By utilising an LVM instrument such as a laser tracker or indoor GPS (iGPS) in mobile robot navigation, many of the most difficult problems in mobile robot navigation can be simplified or avoided. Using the real-time position information from the laser tracker, a very simple navigation algorithm, and a low cost robot, 5mm repeatability was achieved over a volume of 30m radius. A surface digitisation scan of a wind turbine blade section was also demonstrated, illustrating possible applications of the method for manufacturing processes. Further, iGPS guidance of a small KUKA omni-directional robot has been demonstrated, and a full scale prototype system is being developed in cooperation with KUKA Robotics, UK. © 2011 Taylor & Francis.

A Bayesian neural network algorithm identifies patients in whom post-EVAR surveillance may be unnecessary

Lifelong surveillance is not cost-effective after endovascular aneurysm repair (EVAR), but is required to detect aortic complications which are fatal if untreated (type 1/3 endoleak, sac expansion, device migration). Aneurysm morphology determines the probability of aortic complications and therefore the need for surveillance, but existing analyses have proven incapable of identifying patients at sufficiently low risk to justify abandoning surveillance. This study aimed to improve the prediction of aortic complications, through the application of machine-learning techniques. Patients undergoing EVAR at 2 centres were studied from 2004–2010. Aneurysm morphology had previously been studied to derive the SGVI Score for predicting aortic complications. Bayesian Neural Networks were designed using the same data, to dichotomise patients into groups at low- or high-risk of aortic complications. Network training was performed only on patients treated at centre 1. External validation was performed by assessing network performance independently of network training, on patients treated at centre 2. Discrimination was assessed by Kaplan-Meier analysis to compare aortic complications in predicted low-risk versus predicted high-risk patients. 761 patients aged 75 +/− 7 years underwent EVAR in 2 centres. Mean follow-up was 36+/− 20 months. Neural networks were created incorporating neck angu- lation/length/diameter/volume; AAA diameter/area/volume/length/tortuosity; and common iliac tortuosity/diameter. A 19-feature network predicted aor- tic complications with excellent discrimination and external validation (5-year freedom from aortic complications in predicted low-risk vs predicted high-risk patients: 97.9% vs. 63%; p < 0.0001). A Bayesian Neural-Network algorithm can identify patients in whom it may be safe to abandon surveillance after EVAR. This proposal requires prospective study.

High accuracy mobile robot positioning using an external large volume metrology instrument

A method of accurately controlling the position of a mobile robot using an external Large Volume Metrology (LVM) instrument is presented in this paper. Utilizing a LVM instrument such as the laser tracker in mobile robot navigation, many of the most difficult problems in mobile robot navigation can be simplified or avoided. Using the real- Time position information from the laser tracker, a very simple navigation algorithm, and a low cost robot, 5mm repeatability was achieved over a volume of 30m radius. A surface digitization scan of a wind turbine blade section was also demonstrated, illustrating possible applications of the method for manufacturing processes. © Springer-Verlag Berlin Heidelberg 2010.