Determination of the Optimal Process Mean and the Upper Limit for a Canning Problem

Golhar, Damodar Y.; Pollock, Stephen M.

doi:10.1080/00224065.1988.11979105

Cited by 88 publications

(29 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This model is flexible enough to handle any of the situations described in Golhar [11]; Golhar and Pollock [12]; Boucher and Hafari [13] and Schmidt and Pfeifer [14]. Li [15] provides two models to optimise the setting of the process mean for unbalanced tolerance design.…”

Section: Literature Reviewmentioning

confidence: 99%

A general model for process-setting with an asymmetrical linear loss function

2005

Int J Adv Manuf Technol

View full text Add to dashboard Cite

In many industries, unbalanced tolerance design is a common occurrence. It occurs when the deviation of a quality characteristic in one direction is more harmful than the deviation in the opposite direction. The failure mode in the two directions is usually different. Taguchi redefines quality using Gauss's quadratic function as the loss that a product imparts to society from the time the product is shipped. However, using a quadratic loss function when the actual loss function is nonquadratic may yield incorrect input parameter levels. In certain situations, a linear loss function is more appropriate in industrial applications. Furthermore, automatic inspection and measurement technology is widely used by many industries; the nonconforming quality characteristic would be detected automatically. Thus, rather than use a quadratic loss function, we assume a truncated asymmetrical linear loss function to describe unbalanced tolerance design. The purpose of this paper is to find out the optimal setting of the process mean such that the expected quality loss is minimised. The results show that the process mean should be slightly offset from the target value.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A general model for process-setting with an asymmetrical linear loss function

2005

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…Pfeifer [17] presents a general canning problem model consisting of a piecewise linear profit function with two breakpoints. This model is flexible enough to handle any of the situations described in Golhar [18]; Golhar and Pollock [19]; Boucher and Hafari [20] and Schmidt and Pfeifer [21]. Gupta and Golhar [22] used one-sided tolerance to decide the quality of finished goods.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal target selection for unbalanced tolerance design

2004

The International Journal of Advanced Manufacturing Technology

View full text Add to dashboard Cite

In many manufacturing processes, unbalanced tolerance design is a common occurrence. It occurs when the deviation of a quality characteristic in one direction is more harmful than in the opposite direction. The failure mode in these two directions is usually different. Furthermore, automatic inspection and measurement technology are widely used by the industries. The nonconforming part will be detected automatically. Thus, a truncated asymmetrical quadratic loss function is assumed for the unbalanced tolerance design. Traditionally, the manufacturer would either choose the smaller tolerance as the tolerance for both sides, or would set the process mean at the middle of the tolerances. Both methods fail to minimise the expected quality loss. The purpose of this paper is to find out the optimal manufacturing setting such that the expected quality loss is minimised. The results show that the process mean should be shifted a little from the target value.Notation y quality characteristic l process mean of a quality characteristic r standard deviation of a quality characteristic L(y) quality loss function of a quality characteristic, y T target value of a quality characteristic D one side tolerance of a symmetrical specification A quality loss at the specification limits of a symmetrical quality loss function k coefficient of a symmetrical quality loss function k 1 coefficient of an asymmetrical quality loss function on the left-hand side k 2 coefficient of an asymmetrical quality loss function on the right-hand side R k asymmetrical ratio of the coefficient of the quality loss function, i.e. R k =k 1 /k 2 D 1 tolerance on the left-hand side of an unbalanced tolerance D 2 tolerance on the right-hand side of an unbalanced tolerance A 1 quality loss at the specification limit on the left hand side for an unbalanced tolerance design A 2 quality loss at the specification limit on the right hand side for an unbalanced tolerance design R A quality loss ratio at the specification limits, i.e. R A =A 1 /A 2 L(d, A 1 , A 2 ) expected quality loss as a function of d, A 1 and A 2 d standardised distance of the process mean apart from target value, i.e. d= (lÀT)/r l o optimal process mean that minimises expected quality loss d o optimal standardised distance of the process mean apart from target value, i.e. d o =(l o ÀT)/r USL upper specification limit LSL lower specification limit C p process capability ratio, i.e. C p = (USLÀ LSL)/6r R L quality loss ratio, i.e. R L =EL (0)/EL(d o ) AR A (actual value of R A )/(estimating value of R A ) AR k (actual value of R k )/(estimating value of R k ) d o * standardised distance of the process mean apart from target value computed by the actual values of R k and R A Err_d o error ratio of d o , i.e. Err_d o =(d o * Àd o )/d o

show abstract

“…The upper limit was optimised to reduce the loss incurred when customers received extra product but at the standard price. The concept was advanced by Golhar [7], Schmidt and Pfeifer [8] and Liu and Raghavachari [9]. Wen and Mergen [10] were the first to apply optimal mean setting to a feature manufacturing problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving profitability of optimal mean setting with multiple feature means for dual quality characteristics

Dodd

Scanlan

Marsh

et al. 2015

Int J Adv Manuf Technol

View full text Add to dashboard Cite

The setting of a process mean for a manufacturing process which frequently produces scrap and rework, can significantly affect profitability. Optimal mean setting is a methodology by which the process mean is adjusted to maximise profit. This paper studies the dynamics of the problem and investigates the possibility of applying different process means to each rework iteration, to further maximise profit. A proof is given confirming there is only one optimal mean that applies over all rework iterations in the single feature case. However, applying similar reasoning to a dual feature case led to the development of a new optimal mean setting methodology which outperformed the existing approach in terms of the maximum expected profit.

show abstract

Determination of the Optimal Process Mean and the Upper Limit for a Canning Problem

Cited by 88 publications

References 0 publications

A general model for process-setting with an asymmetrical linear loss function

A general model for process-setting with an asymmetrical linear loss function

Optimal target selection for unbalanced tolerance design

Improving profitability of optimal mean setting with multiple feature means for dual quality characteristics

Contact Info

Product

Resources

About