Lean six sigma feasibility and implementation aspect in cast iron foundry

Barot, R.S.; Patel, Jay; Sharma, Balkrushna; Rathod, Bhargav; Solanki, Harshad; Patel, Yash

doi:10.1016/j.matpr.2020.01.087

Cited by 13 publications

(6 citation statements)

References 3 publications

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“…Ranade et al, (2021) used the DMAIC cycle in six sigma, in process quality improvement activities to control the generation of fluctuating parameters during operation (Ranade et al). R. S. Barot et al, (2020) suggested using six-sigma tool with DMAIC cycle to continuously improve process quality performing waste data analysis, find root cause and find ways to improve process. the process of eliminating waste products, eliminating unnecessary problems due to errors in waste generation (Talapatra and Gaine).…”

Section: Literature Reviewmentioning

confidence: 99%

Implement Industrial 4.0 into process improvement: A Case Study in Zero Defect Manufacturing

Que¹,

Huong²,

Hai³

et al. 2023

IRJASH

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This study describes in detail step-by-step implementation of quality improvement activities in mechanical processing plants. Describe how to implement quality 4.0 technologies in DMAIC (Define-Measure-Analysis-Improve-Control) phases. As well as using statistical formulas, experimental design in data analysis at each phase of DMAIC. This study proposes to use Quality 4.0 Technology in product quality improvement activities in mechanical product processing factories with the aim of becoming zero defect manufacturing. The results of the research found are repair rate reduced from 600 PPM monthly to 0 PPM, processing capacity increased from Cpk1.02 to Cpk2.56, reduce time for inspection product from 702 hours per year (calculated to save USD 2106 per year), reduce the amount of repair products by 196 products per year (calculated in terms of money is reduced by 917 USD per year) and reduce 1 roughing stage (calculated in terms of cost reduction about 171288 USD per year). The roughness dimension has reduced measurement time by about 364 hours per year (save 1092 USD per year). Processing digital signals from sensors in an oily environment is a big challenge for researchers. Improving the security of digital data is also a limitation of this study. This study proposes a model to apply statistical hypothesis testing methods to analyze real data collected from each machining stage and perform each job according to each corresponding DMAIC phase of the model. In addition, digital processing techniques and computer vision techniques are also deployed in the improve phase to complete the goal of improving the semiautomatic production stage to the automatic production stage.

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Section: Literature Reviewmentioning

confidence: 99%

Implement Industrial 4.0 into process improvement: A Case Study in Zero Defect Manufacturing

Que¹,

Huong²,

Hai³

et al. 2023

IRJASH

View full text Add to dashboard Cite

show abstract

“…As with any product processing process, when a product is generated, it reduces the stability of the process and increases the risk that the goods will not reach the customer and upset the customer, which means the profit. The company's profit will be reduced (Barot et al, 2020). The operation of the product processing process depends a lot on human skills and manipulations, during the operation of the machining parameters are not if the order is wrong, the risk of product waste (Ranade et al, 2021), using a combination of tools such as the critical to quality (CTQ), the voice of the customer (VOC) and Pareto Chart along with costs Six Sigma's DMAIC process in control of product processing and removal of non-value-added factors along with improving product quality (Srinivasan el al., 2014), implemented expert opinion survey on the link between six sigma implementation and operational performance is closely linked, six sigma project if the operation is successful, the productivity of the company will also increase accordingly (Boon Sin el al., 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Management and Production Engineering Review

Duc¹,

Bilík²

2022

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This paper proposes the application of the digital numerical control (DNC) technique to connect the smart meter to the inspection system and evaluate the total harmonic distortion (THD) value of power supply voltage in IEEE 519 standard by measuring the system. Experimental design by the Taguchi method is proposed to evaluate the compatibility factors to choose Urethane material as an alternative to SS400 material for roller fabrication at the machining center. Computer vision uses artificial intelligence (AI) technique to identify object iron color in distinguishing black for urethane material and white for SS400 material, color recognition results are evaluated by measuring system, system measurement is locked when the object of identification is white material SS400. Computer vision using AI technology is also used to recognize facial objects and control the layout of machining staff positions according to their respective skills. The results obtained after the study are that the surface scratches in the machining center are reduced from 100% to zero defects and the surface polishing process is eliminated, shortening production lead time, and reducing 2 employees. The total operating cost of the processing line decreased by 5785 USD per year. Minitab 18.0 software uses statistical model analysis, experimental design, and Taguchi technical analysis to evaluate the process and experimentally convert materials for roller production. MATLAB 2022a runs a computer vision model using artificial intelligence (AI) to recognize color objects to classify Urethane and SS400 materials and recognize the faces of people who control employee layout positions according to their respective skills.

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“…Combined with the simulation technique, VSM provides better solutions for different enterprises. However, in the foundry industry, related research mainly uses more traditional lean tools such as 5S, Jit and total preventive maintenance to explore the improvement of enterprise production conditions (Barot et al , 2020; Palange and Dhatrak, 2021; Matey et al , 2021). Few studies have applied VSM (Solanki et al , 2021; Zagloel, 2021) and these studies did not further explore the effectiveness and feasibility of application of simulation technique in casting process.…”

Section: Introductionmentioning

confidence: 99%

Research on optimization and simulation of sand casting production line based on VSM

Chao

Zhang

Yu³

et al. 2022

IJLSS

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Purpose This paper aims to provide feasible countermeasures for the application of lean manufacturing in automatic sand casting workshops to optimize production line balance. Design/methodology/approach A production line balance optimization model for sand casting workshops is proposed. The value stream mapping (VSM) approach is applied to diagnose the problems in the production process. An optimization scheme is established based on eliminate, combine, rearrange, simplify and increase theory and Kanban management method. Further, simulation is done to compare current VSM and future VSM. Findings After implementing the proposed model, findings indicated that the idle time of equipment was effectively reduced, the line of balance of the production line was increased by 44.7%, the production lead time was shortened by 60.3% and the production capacity was increased by 50.0%. Research limitations/implications Application of the optimization model in this study is limited to sand casting workshops that have realized automatic or semi-automatic production. Originality/value This paper provides an optimization model for the implementation of lean manufacturing in sand casting workshops and provides a reference case that reflects the actual application of lean manufacturing tools in a real situation.

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Lean six sigma feasibility and implementation aspect in cast iron foundry

Cited by 13 publications

References 3 publications

Implement Industrial 4.0 into process improvement: A Case Study in Zero Defect Manufacturing

Implement Industrial 4.0 into process improvement: A Case Study in Zero Defect Manufacturing

Management and Production Engineering Review

Research on optimization and simulation of sand casting production line based on VSM

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