Computer Aided Tolerance assignment

Tolerances are basic to the production of every part. This is because perfect parts cannot be produced with existing processes and machines. The determination of tolerances for the individual parts of a functional assembly is critical, but not trivial. Numerous approaches are suggested in past literature for (analytical) tolerance allocation. With the advent of total automation, more attempts are being made to computerize manual design tasks. Tolerance design, assignment and allocation can also be fully automated if the assembly function can be estimated by the computer.

In the present paper, an attempt is made to computerize tolerance assignment. A simple example of a two piece assembly, viz., a fit, is used to demonstrate the developed methodology. A feature extraction is first performed from both detail and assembly drawings. Then, probable assembly interfaces are determined using a rule based procedure. Consequently, tolerances are assigned to the basic dimensions of each feature and to the assembly interfaces using a tolerance database and user interaction. More complex analysis for tolerance allocation is also under study.     

Support vector regression for determining the minimum zone sphericity

Several methods have been investigated to determine the deviation of manufactured spherical parts from ideal geometry. One of the most popular is the least squares technique, which is still widely employed in coordinate measuring machines used by industries. The least squares algorithm is optimal under the assumption that the data set is very large and has the inherent disadvantage of overestimating the minimum tolerance zone, resulting sometimes in the rejection of good parts. In addition, it requires that the data be distributed normally. The support vector regression approach alleviates the necessity for these assumptions. While most fitting algorithms in practice today require that the sampled data accurately represent the surface being inspected, support vector regression provides a generalization over the surface. We describe how the concepts of support vector regression can be applied to the determination of tolerance zones of nonlinear surfaces; to demonstrate the unique potential of support vector machine algorithms in the area of coordinate metrology. In specific, we address part quality inspection of spherical geometries.     

Assessing terrestrial wildlife populations in the Toronto and Region Area of Concern

Beneficial use impairments (BUIs) under the Great Lakes Water Quality Agreement identify environmental issues requiring remedial action within the Great Lakes Areas of Concern (AOCs). We conducted this study to support the assessment of the wildlife component of BUI 3: degradation of fish and wildlife populations. We compared bird and amphibian (frogs and toads) data from the Toronto and Region Conservation Authority’s Terrestrial Long-term Monitoring Program in the Toronto and Region AOC to an adjacent, but otherwise similar, reference watershed, Duffins Creek. Twelve of 13 targets were met within the AOC for forest bird, wetland bird, meadow bird and amphibian populations based on averages of mean annual values at sites within the AOC that were within two standard deviations of averages at sites in the Duffins Creek reference watershed between 2008 and 2017. Even though wildlife populations within the AOC were within the normal range of variability expected from a reference watershed, they were often at lower levels than within the Duffins Creek reference watershed. In addition, forest bird and amphibian populations were negatively affected by urbanization within the AOC and meadow bird indices declined. We conclude that while wildlife populations within the AOC currently meet targets for BUI 3, they continue to be negatively impacted by numerous stressors that are primarily related to past and ongoing urbanization. Thus, continued restoration of wildlife habitat and protection of existing habitat within the AOC is highly recommended.     

High-utility and diverse itemset mining

High-utility Itemset Mining (HUIM) finds patterns from a transaction database with their utility no less than a user-defined threshold. The utility of an itemset is defined as the sum of the utilities of its items. The utility notion enables a data analyst to associate a profit score with each item and thereof to a pattern. We extend the notion of high-utility with diversity to define a new pattern type called High-utility and Diverse pattern (HUD). The notion of diversity of a pattern captures the extent of the different categories covered by the selected items in the pattern. An application of diverse-pattern lies in the recommendation task where a system can recommend to a customer a set of items from a new class based on her previously bought items. Our notion of diversity is easy to compute and also captures the basic essence of a previously proposed diversity notion. The existing algorithm to compute frequent-diverse patterns is 2-phase, i.e., in the first phase, frequent patterns are computed, out of which diverse patterns are filtered out in the second phase. We, in this paper, give an integrated algorithm that efficiently computes high-utility and diverse patterns in a single phase. Our experimental study shows that our proposed algorithm is very efficient as compared to a 2-phase algorithm that extracts high-utility itemsets in the first phase and filters out the diverse itemsets in the second phase.

     

Valency of copper in 123 oxides

The valency of copper in freshly prepared as well asin situ high-temperature oxygen-treated 123 oxides was analysed by X-ray photoelectron spectroscopy. The results suggest the presence of Cu²⁺ and Cu³⁺ along with Cu⁺. This observation is supported by cyclic voltammograms of 123 oxides recorded in a formamide medium. The valence band of 1 23 oxides was probed using X-ray (MgK ) and ultraviolet (He-I) sources. It was observed that the Cu-O hybridized orbital in YBa₂Cu₃O_7–x responsible for conduction decreases with time in an ultrahigh vacuum and increases with oxygen treatment temperature.     

Chitosan-based magnetic molecularly imprinted polymer: synthesis and application in selective recognition of tricyclazole from rice and water samples

A tricyclazole selective chitosan/Fe₃O₄ magnetic molecularly imprinted polymer (MMIP) was synthesized using non-covalent binding polymerization involving methacrylic acid (MAA) as functional monomer, divinylbenzene (DVB-80) as crosslinker, 2,2'-azobisisobutyronitrile as initiator, acetonitrile/toluene (75:25, v/v) as porogenic solvent and tricyclazole as template. Surface morphology and magnetic characterization of the prepared imprinted and non-imprinted polymers were done using scanning electron microscopy, transmission electron microscopy, Fourier transform infrared spectrometry and vibrating sample magnetometry, respectively. The adsorption kinetic data fitted best in pseudo-second-order model. The adsorption equilibrium was achieved in 30 min and the maximum binding capacity was 4579.9 µg/g. The Freundlich isotherm model was found suitable for explaining the binding isotherm data (R² > 0.99). Negative values of thermodynamic parameters ∆G (Gibb’s free energy), ∆H (enthalpy), and ∆S (entropy) revealed exothermic and spontaneous nature of adsorption processes. It also revealed decreased randomness at the solid–liquid interface during sorption. The scatchard plot analysis suggested heterogeneity of binding sites on MMIPs. The molecular recognition selectivity of MMIPs towards tricyclazole was much higher, as compared to its structural analogues, tebuconazole (α = 28.58) and hexaconazole (α = 37.16). The MMIPs were successfully applied to separate and enrich tricyclazole from fortified samples of rice and water, with a recovery percentage of 89.4% and 90.9%, respectively. These reusable imprinted polymers possessing high selectivity and specificity can be utilized as an adsorbent for solid-phase extraction in sample preparation for tricyclazole residue analysis in complex environmental matrices.