Component Tolerances Research Articles

Analog Circuit Test Point Selection Incorporating Discretization-Based Fuzzification and Extended Fault Dictionary to Handle Component Tolerances

Analog circuit test point selection aims to find the least number of test points that can isolate all the fault modes (including the fault-free case). The fault dictionary, which uses the integer-valued codes to represent the diagnosability of a specific test point, is very popular and saves computation efforts. However, the classical fault dictionary has a limited ability to handle the component tolerances and continuous-valued monitoring variables. To solve the problem, the approach of clustering-based discretization (CBD) is used to abstract the information of data samples distribution. We also develop a new fault dictionary construction technique called extended fault dictionary (EFD). An element of EFD is a set containing either a single integer code or multiple integer codes. The fault isolation rules are redefined, and a novel entropy measure is created in line with CBD of the continuous values. The practical test point selection procedures are presented, which avoids the likelihood to include a redundant test point. Finally, two application studies of circuit test point election are presented, showing that the proposed method provides an effective implementation option for the engineering practice of circuit diagnosis.

Ultralow voltage imaging using a miniature electron beam column

Miniature columns that are fabricated from silicon using advanced micromachining processes are a relatively new class of electron beam column. The defining characteristics of these columns are a thermal field emitter source, low voltage operation (typically <3 keV), simple design (two lenses, no crossover), microfabricated lenses, and all electrostatic components. Current production versions of miniature columns achieve <10 nm resolution at 1 keV, and have demonstrated <7 nm resolution at higher beam energies [Spallas et al., J. Vac. Sci. Technol., B 24, 2892 (2006)]. The resolution of miniature columns is limited by competing requirements to minimize optical aberrations, relax physical or geometric constraints (e.g., working distance and electrode–electrode distance), and maintain manufacturable mechanical tolerances of the column components (e.g., alignment, diameter, and placement). In this paper, the authors investigate imaging using a miniature electron beam column in a commercial field emission electron microscope equipped with a retarding field lens. Using a retarding field allows high-resolution imaging at landing energies lower than are possible by reducing the beam voltage because the aberrations are lower. The authors show that for the same landing energy, the resolution is better when using the retarding field. High-resolution imaging at landing energies down to 50 eV and imaging is down to 10 eV are demonstrated. Imaging nonconductive samples at landing energies less than the stable unity yield energy is not expected to be beneficial, yet the authors show that in some cases the ability to image nonconducting samples improves. Finally, the authors image a gas shale sample to demonstrate energy dependent contrast.

Design of a wireless charging system with a phase‐controlled inverter under varying parameters

Class-D full bridge is the most common inverter topology at the primary side for wireless electric vehicles (EVs) charging systems. This study takes a novel topology of a phase-controlled inverter as the power amplifier and puts it in a context of the whole charging system. The proposed inverter topology regulates the charging power through adjusting the phase-shift angle among phases with a constant operating frequency, which alleviates the EMI filter design. For various wireless EVs chargers, the gaps between the primary side and the secondary side are changing, which results in various coupling factors k . The equivalent resistance of the EVs battery R battery is also changing during the charging process. Even resonant frequencies at two sides are variable because of the components tolerances and operating environments. This study presents design considerations of a wireless EVs charging system with the proposed technology under variable k , R battery , and resonant frequencies. Circuit parameters are designed and the system efficiency is derived. Industrial prototype of an EV charging system is manufactured with the proposed topology at 3.0 kW. Experiments show that these design considerations can reflect the system characteristics, and the proposed system is a good candidate to be used in wireless EV battery chargers.

Factors affecting the output pulse flatness of the linear transformer driver cavity systems with 5th harmonics

We describe the study we have undertaken to evaluate the effect of component tolerances in obtaining a voltage output flat top for a linear transformer driver (LTD) cavity containing 3rd and 5th harmonic bricks [A. A. Kim et al., in Proc. IEEE Pulsed Power and Plasma Science PPPS2013 (San Francisco, California, USA, 2013), pp. 1354--1356.] and for 30 cavity voltage adder. Our goal was to define the necessary component value precision in order to obtain a voltage output flat top with no more than $\ifmmode\pm\else\textpm\fi{}0.5%$ amplitude variation.

A new statistical method for design and analyses of component tolerance

Tolerancing conducted by design engineers to meet customers’ needs is a prerequisite for producing high-quality products. Engineers use handbooks to conduct tolerancing. While use of statistical methods for tolerancing is not something new, engineers often use known distributions, including the normal distribution. Yet, if the statistical distribution of the given variable is unknown, a new statistical method will be employed to design tolerance. In this paper, we use generalized lambda distribution for design and analyses component tolerance. We use percentile method (PM) to estimate the distribution parameters. The findings indicated that, when the distribution of the component data is unknown, the proposed method can be used to expedite the design of component tolerance. Moreover, in the case of assembled sets, more extensive tolerance for each component with the same target performance can be utilized.

Screening of Bread Wheat Genotypes for Drought Tolerance Using Phenotypic and Proline Analyses.

Drought stress is one of the leading constraints to wheat (Triticum aestivum L.) production globally. Breeding for drought tolerance using novel genetic resources is an important mitigation strategy. This study aimed to determine the level of drought tolerance among diverse bread wheat genotypes using agronomic traits and proline analyses and to establish correlation of proline content and agronomic traits under drought-stress conditions in order to select promising wheat lines for breeding. Ninety-six diverse genotypes including 88 lines from the International Maize and Wheat Improvement Center (CIMMYT)'s heat and drought nurseries, and eight local checks were evaluated under greenhouse and field conditions during 2014/15 and 2015/16 making four testing environments. The following phenotypic traits were collected after stress imposed during the heading to anthesis period: the number of days to heading (DTH), days to maturity (DTM), productive tiller number (TN), plant height (PH), spike length (SL), spikelet per spike (SPS), kernels per spike (KPS), thousand kernel weight (TKW) and grain yield (GY) and proline content (PC). Analysis of variance, Pearson's correlation coefficient, principal component and stress tolerance index were calculated. Genotypes with high yield performance under stressed and optimum conditions maintained high values for yield components. Proline content significantly increased under stress, but weakly correlated with agronomic traits under both optimal and water limited conditions. The positive correlation observed between grain yield and proline content under-drought stress conditions provides evidence that proline accumulation might ultimately be considered as a tool for effective selection of drought tolerant genotypes. The study selected 12 genotypes with high grain yields under drought stressed conditions and favorable adaptive traits useful for breeding.

Synthesis of Low Sensitivity Broadband Matching Networks Using Clonal Selection Algorithm

This paper presents a new methodology for synthesis of broadband matching networks based on Clonal Selection Algorithms (CSA). This metaheuristic uses the hypermutation as only variation operator resulting in a gradual evolving of the network topology. A closed form expression for the transducer power gain (TPG) sensitivity with respect to the component values is employed, in such a way that the effects of the components tolerance on the matching network performance can easily be quantified. The evolvable function proposed is single-objective and eliminates the difficult task of assigning appropriate weights to parameters. The evaluation of the TPG sensitivity enables the designer to identify and remove irrelevant components of the circuit, simplifying it. The efficiency of the methodology is tested in two cases found in the literature: the traditional project of impedance matching for a simple RLC load proposed by Fano [1]; and a real synthesis of impedance matching network for a monopole whip antenna, proposed in [2]. The results are compared with other existing methods.

Analysis and Calibration of Sources of Electronic Error in PSD Sensor Response.

In order to obtain very precise measurements of the position of agents located at a considerable distance using a sensor system based on position sensitive detectors (PSD), it is necessary to analyze and mitigate the factors that generate substantial errors in the system’s response. These sources of error can be divided into electronic and geometric factors. The former stem from the nature and construction of the PSD as well as the performance, tolerances and electronic response of the system, while the latter are related to the sensor’s optical system. Here, we focus solely on the electrical effects, since the study, analysis and correction of these are a prerequisite for subsequently addressing geometric errors. A simple calibration method is proposed, which considers PSD response, component tolerances, temperature variations, signal frequency used, signal to noise ratio (SNR), suboptimal operational amplifier parameters, and analog to digital converter (ADC) quantitation SNRQ, etc. Following an analysis of these effects and calibration of the sensor, it was possible to correct the errors, thus rendering the effects negligible, as reported in the results section.

Remote Laboratory for Measuring Linear Dimensions in the Process of E-Learning

Remote Laboratory for Measuring Linear Dimensions with remote access and control via Internet is developed and described in this paper. The system is realized at the University of Novi Sad, Faculty of Technical Sciences. This lab is dedicated to the students of secondary vocational schools and faculties with the aim to gain knowledge regarding linear dimension and mechanical component tolerance. Every user of this set-up is able to interact, observe and learn through the real measurement processes via communication infrastructure.

Process capability study of three dimensional printing as casting solution for non ferrous alloys

Purpose The purpose of this paper is to investigate the process capability of three-dimensional printing (3DP)-based casting solutions for non-ferrous alloy (NFA) components. Design/methodology/approach After selection and design of benchmark, prototypes for six different NFA materials were prepared by using 3DP (ZCast process)-based shell moulds. Coordinate measuring machine has been used for calculating the dimensional tolerances of the NFA components. Consistency with the tolerance grades of the castings has been checked as per IT grades. Findings The results of process capability investigation highlight that the 3DP process as a casting solution for NFA component lies in ±5sigma (s) limit, as regards to dimensional accuracy is concerned. Further, this process ensures rapid production of pre-series industrial prototypes for NFA. Final components prepared are also acceptable as per ISO standard UNI EN 20,286-I (1995). Originality/value This research work presents capability of the 3DP process supported with experimental data on basis of various process parameters for the tolerance grade of NFA castings. These statistics can help to enhance the application of 3DP-based NFA casting process in commercial foundry industry.

Statistical property feature extraction based on FRFT for fault diagnosis of analog circuits

Feature extraction plays an important role in the field of fault diagnosis of analog circuits. How to effectively extract fault features is crucial to diagnostic accuracy. The components tolerance and circuit nonlinearities of analog circuits can cause some part overlapping of primal signal among different component faults in time domain and frequency domain. Currently, the existing method aims at wavelet features, statistical property features, conventional frequency features and conventional time-domain features. There is no decoupling ability for the feature extraction methods mentioned above. To solve the problem, a new fault features extraction method is proposed. The diagnostic results are compared with those from other methods. Firstly, it is proposed to use the statistical property features of transformed signals by the fractional Fourier transform in the optimal fractional order domain as fault features, such as range, mean, standard deviation, skewness, kurtosis, entropy, median, the third central moment, and centroid. And then, KPCA is used to reduce the dimensionality of candidate features so as to obtain the optimal features. Next, normalization is applied to rescale input features. Finally, extracted features are trained by SVM to diagnose faulty components in analog circuits. The simulation results show that compared with traditional methods, the proposed method is quite efficient to improve diagnostic accuracy.

Assembly design in aeronautic field: From assembly jigs to tolerance analysis

Tolerance analysis represents the best way to solve assembly problems in order to improve the quality and to reduce the costs. It is a critical step to design and to build a product such as an aircraft and its importance is grown up in the past years. This work presents a method for the tolerance analysis of an assembly involving free-form surfaces with large dimensions. The assembly is a tail beam, a structural component of an aircraft that is constituted by five parts of large dimensions. The influence of the tolerances applied to the five components of the tail beam on the value of the gap at the interfaces among the parts has been deeply investigated. A set of control points have been distributed on the free-form surfaces of the tail beam; its number and its distribution have been opportunely designed. Moreover, the influence of the tolerances on the other requirements of the tail beam connected with the motion drive has been studied. Tolerance analysis has involved the choice of the assembly tools and sequence too. The assembly jigs are mated with the assembly components through pins that are inserted into tooling holes located on the assembly components. The fit conditions have been modeled and the tolerances of the tooling hole have been opportunely chosen. Each tolerance of the tail beam components has been modeled by means of a probability density function. Monte Carlo approach has been used to obtain the statistical distribution of the assembly requirements, once dimensions and geometry of the tail beam components have been perturbed inside the tolerance ranges. Monte Carlo simulation has been carried out by a well-known computer-aided tolerance software, eM-Tolmate of UGS®.

Improvement in quality and productivity of an assembled product: A riskless approach

In this paper, to show the importance of improvement on savings of costs in a riskless sense, the quality loss and manufacturing cost functions are employed to allocate the components tolerances of an assembled product simultaneous with taking appropriate improvement operations. The use of improvement operations reduces the variability of the process while increasing the production costs. To apply improvement operations in a riskless sense, which actually does not impose additional costs to the producers, we take advantages of the trade-off between improvement costs and tolerance costs. Therefore, a series of algorithms are proposed for simultaneous selection of tolerances and improvement operations so as to minimize the total cost. The use of our approach helps considerably save large amounts of computer time by pruning many unnecessary evaluations. It is shown that proper choices of both improvement operations and tolerances have an important impact to enhance productivity and quality.

An Evolutionary Method for Synthesizing Low-Sensitivity Lossless Matching Networks

Whereas the present practice of designing matching networks for antennas is limited to conventional topologies, requiring a significant amount of domain knowledge, evolutionary algorithms can be used for automatically identifying unconventional designs that are more effective than would otherwise be developed. In this work, an automatic method to design lossless matching networks driven by an evolutionary algorithm (EA) that considers the sensitivities of the network parameters during the synthesis process is presented. To this end, a closed-form expression for the transducer power gain (TPG) sensitivity with respect to the component values is employed in such a way that the effects of the components tolerance on the matching network performance can easily be quantified. A 3D data structure based on the adjacency matrix is conveniently used to represent any type of network topologies. The proposed EA employs a novel set of topology variation operators, tailored for changing the circuit topology, and an association step, with the aim of reducing the number of nodes of the matching circuit. The efficiency of the proposed EA is tested in the synthesis of an impedance matching network for a VHF monopole whip antenna. This study's results indicate a matching bandwidth improvement, a more uniformly distributed TPG along the operation frequency band and a more stable TPG regarding the components tolerance compared to the results obtained by previous approaches.

Make or buy analysis model based on tolerance allocation to minimize manufacturing cost and fuzzy quality loss

The specification of tolerances has a significant impact on the quality of product and final production cost. The company should carefully pay attention to the component or product tolerance so they can produce a good quality product at the lowest cost. Tolerance allocation has been widely used to solve problem in selecting particular process or supplier. But before merely getting into the selection process, the company must first make a plan to analyse whether the component must be made in house (make), to be purchased from a supplier (buy), or used the combination of both. This paper discusses an optimization model of process and supplier selection in order to minimize the manufacturing costs and the fuzzy quality loss. This model can also be used to determine the allocation of components to the selected processes or suppliers. Tolerance, process capability and production capacity are three important constraints that affect the decision. Fuzzy quality loss function is used in this paper to describe the semantic of the quality, in which the product quality level is divided into several grades. The implementation of the proposed model has been demonstrated by solving a numerical example problem that used a simple assembly product which consists of three components. The metaheuristic approach were implemented to OptQuest software from Oracle Crystal Ball in order to obtain the optimal solution of the numerical example.

Simulated Annealing Algorithm for Minimising the Surplus Parts in Selective Assembly-A Software Approach

Selective assembly is a precision assembly making process from wider tolerance components in which products are produced from randomly assembling the measured components from portioned bins. Very few papers were discussed in literature regarding cost aspects of manufacturing products in selective assembly techniques. Only procedural algorithms were used to calculate the cost of a product in these techniques. Moreover, tolerance cost curves and selection of tolerance for a component with alternative process were also not considered in most of the papers found in literature. The best combination of bins to make less scrap was computed only based on component's tolerance and not by considering the original dimension of the components. None of the paper used simulated annealing algorithm to compute the best bin combinations to make more number of assemblies in turn to reduce surplus parts. In this paper, these problems were removed using Power Builder, an optional programming language with Sybase SQL Anywhere as a database and Matlab software. Shaft housing assembly is considered as an example product to illustrate the effectiveness of the proposed method. Tolerance cost curves can be viewed within no time for individual process or together with a good number of processes. Moreover, for the same process and tolerance value, the best nominal dimension of the product can also be selected in this work and for the given tolerance of a component with alternative processes; the minimum cost process can be visualized and calculated.

パラレルリンクロボットの構成要素の幾何公差に基づく公差設計に関する研究

パラレルリンクロボットの構成要素の幾何公差に基づく公差設計に関する研究

Development of an intelligent pressure measuring technique for bellows using radial basis function neural network

This paper presents the implementation of an intelligent pressure transmitter to measure pressure using a bellow sensor. In industrial applications, the deflection of the bellow due to applied pressure must be translated into an efficient electrical readout for monitoring, transmission and control. An inductive pick-up is used to convert the deflection of the bellow into the change in self-inductance of a coil. The signal conditioning circuit designed for the bellow sensor with the inductive coil is an inductance to voltage conversion circuit (ITVCC). The stray inductances, component tolerances and ambient factors introduce errors in the output of the ITVCC. The voltage–pressure relation exhibits a considerable nonlinearity and limits the measurement to local operations. In this aspect, we propose an artificial neural network (ANN) using a radial basis function to estimate and compensate the nonlinearity of the ITVCC. The intelligence of ANN modeling is incorporated into an embedded plug-in-module (EPIM). The output voltage of the EPIM is converted into a 4–20mA current signal for further processing. The performance of the proposed technique is experimentally verified. The nonlinearity expressed as maximum deviation from the desired response is within ±0.6% of full scale reading. The design aspects, simulation analysis and experimental results of the technique are reported.

Editorial

Traditionally, tolerance allocation and scheduling have been dealt with separately in the literature. The aim of tolerance allocation is to minimize the tolerance cost. When scheduling the sequence of product operations, the goal is to minimize the makespan, mean flow time, machine idle time, and machine idle time cost. Calculations of manufacturing costs derived separately using tolerance allocation and scheduling separately will not be accurate. Hence, in this work, component tolerance was allocated by minimizing both the manufacturing cost (sum of the tolerance and quality loss cost) and the machine idle time cost, considering the product sequence. A genetic algorithm (GA) was developed for allocating the tolerance of the components and determining the best product sequence of the scheduling. To illustrate the effectiveness of the proposed method, the results are compared with those obtained with existing wheel mounting assembly discussed in the literature.

Comparative Analysis of Chosen Tolerance Stackup Methods and Development of an Improved Tolerance Analysis Method

The perception of Tolerance Analysis (TA)/Tolerance Stackup is imperative for every Design and Manufacturing Engineer because Tolerance is the criterion that should be compromised between the cost and function of a product. The literatures relevant to 15 methods of TA which are being used to determine Assembly Tolerance from Component Tolerances are collected and critically analyzed to gain an insight into the existing methods. Out of these methods, four major methods viz., Simulation Based Stack-Up Analysis, Second Order Tolerance Analysis, OpTol - Spatial Tolerance Analysis and Tolerance Analysis of 2D and 3D Assemblies are chosen for further study and comparative analysis. Based on the analysis and based on the identified merits and demerits of these methods, a framework for a new TA Method is developed. Based on the developed framework, a new TA Method using Artificial Neural Network (ANN) is developed and trained which can predict the value of Assembly Tolerance for the known Component Tolerances.