Process capability improvement through DMAIC for aluminum alloy wheel machining

Sharma, G. V. S. S.; Rao, P. Srinivasa; Babu, B. Surendra

doi:10.1007/s40092-017-0220-z

Cited by 19 publications

(9 citation statements)

References 24 publications

(16 reference statements)

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“…Furthermore, some stages are worker-dependent. SIPOC (suppliers, inputs, process, outputs, customers) [39,40], Pareto chart [41], COPQ (Cost of poor quality) [42], brainstorming [43], FMECA (Failure mode, effects and criticality analysis) [24,44], CTQ (Critical to quality) matrix [45], control charts [46], design of experiments [4,47], cause and effect diagram [48], and process capability [49] are wellknown tools and techniques in the engineering field. The latter have been used during the PDCA cycle implementation aiming to bring an effective solution of a problem and to provide benefits for the industrial scenario.…”

Section: The Pdca Cycle Implementationmentioning

confidence: 99%

A Guideline for Implementing a Robust Optimization of a Complex Multi-Stage Manufacturing Process

et al. 2021

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In the industrial production scenario, the goal of engineering is focused on the continuous improvement of the process performance by maximizing the effectiveness of the manufacturing and the quality of the products. In order to address these aims, the advanced robust process optimization techniques have been designed, implemented, and applied to the manufacturing process of ultrasound (US) probes for medical imaging. The suggested guideline plays a key role for improving a complex multi-stage manufacturing process; it consists of statistical methods applied for improving the product quality, and for achieving a higher productivity, jointly with engineering techniques oriented to problem solving. Starting from the Six Sigma approach, the high definition of the production process was analyzed through a risk analysis, and thus providing a successful implementation of the PDCA (plan-do-check-act) methodology. Therefore, the multidisciplinary analysis is carried out by applying statistical models and by detecting the latent failures by means of NDT (non-destructive testing), i.e., scanning acoustic microscopy (SAM). The presented approach, driven by the statistical analysis, allows the engineers to distinguish the potential weak points of the complex manufacturing, in order to implement the corrective actions. Furthermore, in this paper we illustrate this approach by considering a pilot study, e.g., a process of US probes for medical imaging, by detailing all the guideline steps.

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Section: The Pdca Cycle Implementationmentioning

confidence: 99%

A Guideline for Implementing a Robust Optimization of a Complex Multi-Stage Manufacturing Process

et al. 2021

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“…Kualitas merupakan kepuasan pelanggan sepenuhnya (full customer satisfaction), sesuai dengan apa yang diharapkan. Perbaikan kualitas dengan identifikasi dan mencari faktor penyebab menggunakan ishikawa diagram, untuk tindakan korektif dengan PFMEA serta memperbaiki tingkat capability dengan pendekatan DMAIC (Sharma et al, 2018). Untuk meningkatkan kualitas produk, pendekatan DMAIC dalam six sigma dapat menurunkan defect (Gandhi at al., 2019).…”

Section: Kajian Pustakaunclassified

Analisis cacat painting komponen automotive dengan pendekatan DMAIC-FMEA

Supriyati¹,

Hasbullah

2020

OE Journal

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Increasing competition among competitors demands the automotive component painting industry to improve product quality, visual impairment dominates painting process product. High defects lead to quality degradation, set KPI targets have not been achieved, 1-year average defects are 5.4% from 4.2% targets. To improve the quality and to analyze product non-compliance, DMAIC and FMEA approaches are used. The use of Six Sigma can analyze defects in production. A number of improvements have been made to ensure that the objectives are met, from the analysis it was found that the highest percentage of defects in Line 1 was 6.86% and type of spot/dirty was 36.3%, DPMO value 7619 and sigma value 3.9. The analysis with FMEA based on the highest 8 RPN values is the priority of improvement, the main factor influencing spot /dirty from the machine. Regular maintenance and cleaning need to be emphasized, easily contaminated chemicals require special handling, alterations of hanger design by adding dishes should be done to prevent dirt from falling into product.

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“…Six Sigma can be seen as a systematic and organized, customer-oriented methodology that aims to improve the performance and quality of processes, products, and services using statistical techniques and the scientific method to analyze data and make decisions [17]. The generic problems of alloy wheel machining and subsequently details on the process improvement of the identified critical-to-quality machining characteristic of aluminum alloy wheel machining process [18]. Casting parameters are optimized in order to achieve better properties [19].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Aluminium Alloy Wheels Product Quality Improvement Through DMAIC Method in Casting Process: A Case Study of the Wheel Manufacturing Industry in Indonesia

Maryani¹,

Purba²,

Sunadi³

2021

JESA

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The global market demand for automotive wheels with alloy materials is 55%, which is quite high compared to other materials such as steel, magnesium, chromed, and carbon fiber. The high competition of the global alloy wheels market demands to be able to offer quality alloy wheels. The purpose of this research is to reduce the number of defects in the casting process step by using the Define Measure Analyze Improve Control (DMAIC) method. This study shows the systematic approach to find the root cause of major defects in aluminum castings using the defect diagnostic approach as well as cause and effect diagram. Quality improvement using quality tools, namely the Pareto diagram, fishbone diagram. The major defects for the rejections during production were identified as leak defects, porosity motive holes, and oval defects. In determining the proposed quality improvements using the FMEA tool. The results of data processing on the calculation of process capabilities and product performance show improvements after quality improvements in the casting process. Product performance from DPMO = 15.462, sigma level = 3.6 to DPMO = 8.186 and sigma level = 3.9. The effect of decreasing the percentage of defects could save production costs by IDR 413.350.000. Therefore, the application of the DMAIC method can provide a significant improvement in product quality and impact on production cost savings.

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Process capability improvement through DMAIC for aluminum alloy wheel machining

Cited by 19 publications

References 24 publications

A Guideline for Implementing a Robust Optimization of a Complex Multi-Stage Manufacturing Process

A Guideline for Implementing a Robust Optimization of a Complex Multi-Stage Manufacturing Process

Analisis cacat painting komponen automotive dengan pendekatan DMAIC-FMEA

Analysis of Aluminium Alloy Wheels Product Quality Improvement Through DMAIC Method in Casting Process: A Case Study of the Wheel Manufacturing Industry in Indonesia

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