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This paper seeks to address the production control problem of a failure‐prone manufacturing system producing a random fraction of defective items.

A fluid model with perfectly mixed good and defective parts has been proposed. This approach combines the descriptive capacities of continuous/discrete event simulation models with analytical models, experimental design, and regression analysis. The main objective of the paper is to extend the Bielecki and Kumar theory, appearing under the title “Optimality of zero‐inventory policies for unreliable manufacturing systems”, under which the machine considered produced only good quality items, to the case where the items produced are systematically a mixture of good as well as defective items.

The paper first shows that for constant demand rates and exponential failure and repair time distributions of the machine, the Bielecki‐Kumar theory, adequately revisited, provides new and coherent results. For the more complex situation where the machine exhibits non‐exponential failure and repair time distributions, a simulation‐based approach is then considered. The usefulness of the proposed models is illustrated through numerical examples and sensitivity analysis.

Although the decisions taken in response to demands for productivity have a direct impact on product quality, management quality and production management have been traditionally treated as independent research fields. In response to this need, this paper is considered as a preliminary work in the intersection between quality control and production control issues.

A study was conducted on a leading US‐based computer maker to examine its reverse logistics operations in the Asia‐Pacific region. This US company had set up a spare parts business unit in Singapore to take care of the Asia‐Pacific customers for its products, which were still under warranty or service contracts. Defective parts were sent to its US headquarters for refurbishment and repair, and subsequently return to the Asia‐Pacific region. The study revealed a number of interesting findings. These included: about 50 percent of the products returned to the USA cost less than half the reverse logistics costs; the current information technology systems supporting the reverse logistics operations are not used in assisting the company’s managers in making critical decisions but in data collection; and decision making on reverse logistics at each of the company’s Asia‐Pacific offices was inconsistent and lacked standardization. Recommendations were subsequently made to overcome some of the inefficiencies in managing the reverse supply chain.

Defects in fabric have been and continue to be a major source of seconds in finished garments. These defects persist despite several visual inspections and intensive efforts to remove defective parts during sewing operations. The increased use of automation in assembly steps will intensify the problem of detection and removal of fabric defects in cut‐parts. Describes a workstation utilizing machine vision which has been designed and constructed to detect and remove defective cut‐parts prior to the initiation of assembly operations. The workstation employs two vision systems — an area camera and a line camera — to inspect parts on a conveyor belt both statically and dynamically. The colour of the parts is also determined and the area and perimeter are measured to detect improperly cut parts. The acceptable parts are then stacked in a manner suitable for input to an automated sewing station. The workstation should permit placing into the assembly operations a set of defect‐free, properly‐cut and colour‐matched parts. It is estimated that this cut‐part inspection system will reduce defects in finished garments by approximately 50 per cent and should greatly simplify the labour‐intensive and costly fabric defect control systems currently in place in most apparel plants.

Examines the effectiveness of the line‐stop (on‐line) repair policy over traditional off‐line repair policy through two mathematical models developed based on total quality failure costs (TFC). The proposed models demonstrate that the TFC framework can be a valuable performance measure for evaluating the contribution of the line‐stop repair policy. The computational results also show that the line‐stop policy can bring substantial savings over the off‐line repair policy.

To avoid the high cost of poor quality (COPQ), there is a constant need for minimizing the formation of defects during manufacturing through defect detection and process parameters optimization. This research aims to develop, design and test a smart system that detects defects, categorizes them and uses this knowledge to enhance the quality of subsequent parts.

The proposed system integrates data collected from the deep learning module with the machine learning module to develop and improve two regression models. One determines if set process parameters would yield a defective product while the second model optimizes them. The deep learning model utilizes final product images to categorize the part as defective or not and determines the type of defect based on image analysis. The developed framework of the system was applied to the forging process to determine its feasibility during actual manufacturing.

Results reveal that implementation of such a smart process would lead to significant contributions in enhancing manufacturing processes through higher production rates of acceptable products and lower scrap rates or rework. The role of machine learning is evident due to numerous benefits which include improving the accuracy of the regression model prediction. This artificial intelligent system enhances itself by learning which process parameters could lead to a defective product and uses this knowledge to adjust the process parameters accordingly overriding any manual setting.

The proposed system was applied only to the forging process but could be extended to other manufacturing processes.

This paper studies how an artificial intelligent (AI) system can be developed and used to enhance the yield of good products.

Quality control and part inspection add no monetary value to a product, yet are essential processes for manufacturers who want to maintain product quality. Mass‐produced custom parts require processes that are able to perform high frequency of inspection, whilst providing rapid response to unanticipated changes in parameters such as throughputs, dimensions and tolerances. Frequent inspection of these parts significantly impacts inspection times involved. A method of reducing the impact of high‐frequency inspection on production rates is needed. This paper addresses these issues.

This paper involves the research, design, construction, assembly and implementation of an automated apparatus, used for the visual inspection of moving custom parts. Inspection occurred at user‐defined regions of interest (ROIs). Mechatronic Engineering principles are used to integrate sensor articulation, image acquisition and image‐processing systems. The apparatus is tested in a computer‐integrated manufacturing (CIM) cell for quantifying results.

Specified production rates are maintained whilst performing high frequencies of inspection, without stoppage of parts along the production line.

The limitations of these results lie in the fact that they are suited only to the speed of the CIM cell. Higher inspection rates may be achieved, and changes in the design may be required in order to make the apparatus more suitable to industrial applications.

The paper shows that it is possible to maintain high standards of quality control without significantly affecting production rates.

Current research does not focus on maintaining production rates whilst inspecting custom parts. The use of ROI inspection for moving custom parts is a relatively new concept.

Service parts are needed for maintenance of industrial systems as well as for consumer products. Their logistics has an inherent difficulty: common models for inventory management are invalid, as the demand process is different and demand data scarce. The paper discusses experiences gained in case studies of practical stock control techniques. New concepts aimed to reduce the problem of slow moving parts are described, for example: suppliers leasing service parts; standardisation of parts for the group of machines in a factory or over a complete sector of industry; a “broker” between suppliers and customers who makes the service parts inventories transparent and facilitates pooling of parts.

The use of robots to control for quality in manufacturing raises the issue of choice and its effect on the probability of accepting defective parts or rejecting good ones. The application of robots to the quality gauges is described and robot repeatability and errors in production processes are examined.

The authors propose to introduce industrial visual equipment to automate visual inspection systems. In addition to a conventional TV camera system, the recent development of solid‐state image‐sensor is expected to be fruitful. This paper describes up to date visual equipment and its usage and discusses some of the problems.

One of the major obstacles contributing to the cost, time and efficiency of improving the quality output of manufacturing systems is the propagation of defectives or errors through the system. Conventional individual control chart design does not address the problem of the interrelation of the processes adequately. Owing to the increasing complexity of manufacturing systems as well as the problems caused by the natural variability of the systems, trial‐and‐error methods are the most commonly used technique for the implementation of the control charts. Trial‐and‐error methods are very costly, time consuming and highly disruptive to the real system. Hence, a systematic and holistic computer‐based methodology is proposed in this paper to obtain a control chart configuration which improves productivity and quality, and reduces cost. Simulation is used as a platform to conduct the control chart system design because different scenarios can be tested off‐line so that statistical process control can be performed effectively without making costly mistakes and disturbing the real system.