Development of an in-process Pokayoke system utilizing accelerometer and logistic regression modeling for monitoring injection molding flash

Zhang, Julie Z.

doi:10.1007/s00170-013-5604-7

Cited by 17 publications

(6 citation statements)

References 16 publications

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“…In a similar context, among the systems operating in real time, [8] propose to insert an adaptative CNC machine parameters system trained with neural networks to obtain the surface roughness within a desired value in an in-process manner. Similarly, [22] implements a controller applied to an injection molded recycled plastic molding system. Data collection is performed by an accelerometer, while data is processed using a classifier based on logistic regression.…”

Section: Related Workmentioning

confidence: 99%

Poka Yoke Meets Deep Learning: a Proof of Concept for an Assembly Line Application

Martinelli

Gamberini²,

Lippi³

2021

Preprint

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In this paper we present the re-engineering process of an assembly line that features speed reducers and multipliers for agricultural applications. The product operates as an interface between an input torque, typically supplied by an agricultural vehicle, and an output torque, generally moving specific equipment placed on a trolley equipped with a tow hook. The "as-is'' (initial version) line was highly inefficient due to several critical issues, including the high age of the machines, a non-optimal arrangement of them in the shop floor, and the absence of controls and process standards. These critical issues were analysed with the tools offered by Lean Manufacturing, which made it possible to identify irregularities and operations that require effort (Mura), overload (Muri), and waste (Muda). The definition of the "to-be'' (new version) assembly line included actions to update the department layout, to modify the assembly process and to design the line feeding system in compliance with the well-known concepts of Golden Zone and Strike Zone. The whole process addressed, in particular, the problem of the incorrect assembly of the oil seals. The registered error was mainly caused by the difficulty in visually identifying the correct side of the assembled oil seal, and by the mental fatigue of operators at the end of the shift. The solution studied in this paper resulted in a Poka-Yoke solution, which, leveraging the modern technologies and methods of deep learning and computer vision, monitors the process flow of the operators through a camera, preventing its completion in the event of an assembly error.

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Section: Related Workmentioning

confidence: 99%

Poka Yoke Meets Deep Learning: a Proof of Concept for an Assembly Line Application

Martinelli

Gamberini²,

Lippi³

2021

Preprint

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“…Accelerometers were installed at the fixture of the inspection system to collect the dynamic trend of the mold movement during the filling stage. Data captured by Z direction of the accelerometer was in particular analyzed to extract feature signal, and logistics regression model was applied for determination of the state [6].…”

Section: Inspection Using Accelerometermentioning

confidence: 99%

In-Process Noise Detection System for Product Inspection by Using Acoustic Data

Baek

Kim

2019

IFIP Advances in Information and Communication Technology

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Objective quality inspection of products in manufacturing process is inseparable from sensor technologies. Inspection methods using analysis of vibration signals have advantages such as being non-destructive, accurate, fast for in-process application. This paper presents recent developments and applications of in-process product inspection which use vibration and acoustic data in various industry. In detail, the inspection system developed with accelerometer, laser vibrometer, laser ultrasonic sensor, acoustic emission sensor, and microphone are presented. An in-process noise detection system for car body parts inspection is introduced as a case study.

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“…Zhang [61] has presented a case study in which the monitoring of injection moulding flash was carried out using an inprocess Poka-Yoke system comprising an accelerometer and logistic regression modelling. The material used for carrying out the injection moulding process was a mixture of pure polystyrene and a small quantity of low-density polyethylene.…”

Section: Case Studies On Poka-yokementioning

confidence: 99%

Six Sigma through Poka-Yoke: a navigation through literature arena

Vinod

Devadasan

Sunil

et al. 2015

Int J Adv Manuf Technol

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From the middle of 1980s, Six Sigma concept emerged in Motorola. Because Six Sigma implementation facilitated the companies to achieve profitability, it was implemented subsequently in many parts of the world. Six Sigma is implemented by majority of the companies situated in the world by applying DMAIC (stands for define, measure, analyse, improve and control) phases. Meanwhile, there have been reports of the failures of implementing Six Sigma programmes presumably due to the failure to consider and apply the appropriate tools and techniques. In this regard, it is to be noticed that Poka-Yoke, which is a tool that can be used to achieve zerodefect manufacturing, has the potential to support the implementation of DMAIC phases of Six Sigma. On deriving this observation, the literature navigation reported in this paper was carried out to understand the essence of Six Sigma and PokaYoke as well as the trend of applying Poka-Yoke in DMAIC phases. In the literature survey conducted to carry out this literature navigation, the researches on applying Poka-Yoke concept in DMAIC phases were studied by referring to the relevant research papers. It has been pointed out that the Poka-Yoke solutions suggested in these research papers were not actually implemented in real-time scenario. Hence, this article ends with a suggestion that future researchers can make efforts to actually implement Poka-Yoke technique in DMAIC phases for achieving the goals of Six Sigma.

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Development of an in-process Pokayoke system utilizing accelerometer and logistic regression modeling for monitoring injection molding flash

Cited by 17 publications

References 16 publications

Poka Yoke Meets Deep Learning: a Proof of Concept for an Assembly Line Application

Poka Yoke Meets Deep Learning: a Proof of Concept for an Assembly Line Application

In-Process Noise Detection System for Product Inspection by Using Acoustic Data

Six Sigma through Poka-Yoke: a navigation through literature arena

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