Research on assembly quality evaluation based on Markov chain

Lu, Jiacai; Tang, Shuiyuan; Yan, Yan; Zhang, Faping; Shahid, Butt

doi:10.1109/ieem.2009.5373038

Cited by 4 publications

(5 citation statements)

References 3 publications

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“…To evaluate the final assembly quality of missile cabin, an assembly quality model was established by LU Jiping et al to explore the relationship between the assembly status and quality loss. Then the transfer matrix of assembly process was built for the missile cabin based on the Markov chain (Lu et al, 2009). As reviewed in literatures, although many scholars have turned several management methods to explore the mechanism in improving the reassembly quality, a proper quality control method oriented to remanufacturing parts with qualified quality attributes, but exceed dimension is not yet to be established.…”

Section: Literature Reviewmentioning

confidence: 99%

Reassembly classification selection method based on the Markov Chain

Liu

et al. 2018

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Purpose As the last link of product remanufacturing, reassembly process is of great importance in increasing the utilization of remanufactured parts as well as decreasing the production cost for remanufacturing enterprises. It is a common problem that a large amount of remanufactured part/reused part which past the dimension standard have been scrapped, which have increased the production cost of remanufacturing enterprises to a large extent. With the aim to improve the utilization of remanufacturing parts with qualified quality attributes but exceed dimension, the purpose of this paper is to put forward a reassembly classification selection method based on the Markov Chain. Design/methodology/approach To begin with, a classification standard of reassembly parts is proposed. With the thinking of traditional ABC analysis, a classification management method of reassembly parts for remanufactured engine is proposed. Then, a homogeneous Markov Chain of reassembly process is built after grading the matching dimension of reassembly parts with different variety. And the reassembly parts selection model is constructed based on the Markov Chain. Besides, the reassembly classification selection model and its flow chart are proposed by combining the researches above. Finally, the assembly process of remanufactured crankshaft is adopted as a representative example for illustrating the feasibility and the effectiveness of the method proposed. Findings The reassembly classification selection method based on the Markov Chain is an effective method in improving the utilization of remanufacturing parts/reused parts. The average utilization of remanufactured crankcase has increased from 35.7 to 80.1 per cent and the average utilization of reused crankcase has increased from 4.2 to 14 per cent as shown in the representative example. Originality/value The reassembly classification selection method based on the Markov Chain is of great importance in enhancing the economic benefit for remanufacturing enterprises by improving the utilization of remanufactured parts/reused parts.

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Section: Literature Reviewmentioning

confidence: 99%

Reassembly classification selection method based on the Markov Chain

Liu

et al. 2018

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“…10 Simplification for shape and assembly paths Int J Adv Manuf Technol 2 first indexes, 6 secondary indexes, and 22 third indexes to construct the assembly quality status [21]. The first indexes are divided into assembly process and external factor.…”

Section: DI Pimentioning

confidence: 99%

“…The current quantitative research works of product quality mainly focus on the design stage by using Taguchi quality loss function [19,20]. There is little work about assembly quality evaluation [21]. The quantitative evaluation of cabin's assembly quality is performed on different levels according to the factors affecting the assembly quality by comparing assembly parts status parameter with the designed parameter interval based on quality loss function [21].…”

Section: Introductionmentioning

confidence: 99%

A computer vision-based assistant system for the assembly of narrow cabin products

Liu

Wang

et al. 2014

Int J Adv Manuf Technol

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Due to the narrowness of space and the complexity of structure, the assembly of cabin parts has become one of the major bottlenecks in the whole manufacturing process. This paper presents a computer vision-based assistant system that integrates assembly planning, assembly training and guidance, assembly status inspection, and assembly quality evaluation to improve the assembly efficiency and quality of cabin products. By using a novel real-time 3D object registration approach, the mixed reality technology is applied to assembly training and guidance of cabin parts. A machine vision-based custom-made inspection system is designed for assembly surplus detection and gap measurement in half-enclosed cabin space. A cabin assembly quality model is proposed on the basis of Markov chain to quantitatively evaluate the cabin assembly quality. A case study is given to demonstrate the practical application of the proposed system.

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“…High aspect ratio aspheric workpieces are widely used in precision systems in aerospace, nuclear physics and other fields, such as rocket engines and spaceships [1,2]. The dimensional accuracy of the high aspect ratio aspheric workpiece is important since the geometric errors would greatly influence the performance reliability of the systems [3,4]. For example, in the rocket engine nozzles, the surface error and geometric error above the micrometer level will affect the stable flow state of the tail flame, resulting in the degradation of the engine performance [5].…”

Section: Introductionmentioning

confidence: 99%

Accurate Inner Profile Measurement of a High Aspect Ratio Aspheric Workpiece Using a Two-Probe Measuring System

Xiong

Zhang

et al. 2022

Applied Sciences

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This paper presents a novel method for inner profile measurement and geometric parameter evaluation, such as the radius of the bottom, steepness and straightness of the steep sidewall of a high aspect ratio aspheric workpiece, by utilizing a two-probe measuring system, which includes a lateral displacement gauge for the inner steep sidewall profile measurement and an axial displacement gauge for the inner deep underside profile measurement. To qualify the measurement accuracy, the systematic errors associated with the measurement procedure, including the miscalibration, misalignment and the roundness error of the gauge probes, as well as the slide motion error of the four-axis motion platform, are all evaluated and separated from the measurement results. A point cloud registration algorithm is employed to stitch the evaluated inner sidewall profile and the inner underside profile to form an entire inner profile of the workpiece. To verify the performance of the newly proposed method, the inner profile of a high aspect ratio aspheric workpiece, which has a tapered cone shape with a maximum inner radius of 40 mm, a maximum inner depth of 140 mm and a steep sidewall angle approaching 85°, is measured in experiments. The measurement result is compared with that of a coordinate measuring machine (CMM), and the comparison verifies the feasibility of the proposed measurement system.

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Research on assembly quality evaluation based on Markov chain

Cited by 4 publications

References 3 publications

Reassembly classification selection method based on the Markov Chain

Reassembly classification selection method based on the Markov Chain

A computer vision-based assistant system for the assembly of narrow cabin products

Accurate Inner Profile Measurement of a High Aspect Ratio Aspheric Workpiece Using a Two-Probe Measuring System

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