Robust design of composites manufacturing processes with process simulation and optimisation methods

Due to the increasing variations in raw materials and manufacturing processes, composite manufacturing processes have more part-to-part variations compared with the metal manufacturing processes. To improve part quality consistency, tooling design optimisation is an imperative step for addressing the stochastic behaviour of composite manufacturing processes. This paper presents an optimisation approach for the typical composite manufacturing technique of resin transfer moulding (RTM), which minimises the sensitivity of the mould design to uncertain material properties by choosing appropriate locations of injection gates and vents. This paper proposes a stochastic simulation based approach for the RTM processes. Normal distribution and Weibull distribution were utilised as the statistical models for representing the permeability values for the main region and race-tracking, respectively. Based on the statistical properties of the permeability, a graph-based two-phase heuristic (GTPH) was adopted to minimise the flow dispersion value (a quantitative measure for part quality consistency) such that the process design is not sensitive to the material and process parameter variations.     

Modelling infant failure rate of electromechanical products with multilayered quality variations from manufacturing process

The optimisation of product infant failure rate is the most important and difficult task for continuous improvement in manufacturing; how to model the infant failure rate promptly and accurately of the complex electromechanical product in manufacturing is always a dilemma for manufacturers. Traditional methods of reliability analysis for the produced product usually rely on limited test data or field failures, the valuable information of quality variations from the manufacturing process has not been fully utilised. In this paper, a multilayered model structured by ‘part-level, component-level, system-level’ is presented to model the reliability in the form of infant failure rate by quantifying holistic quality variations from manufacturing process for electromechanical products. The mechanism through which the multilayered quality variations affect the infant failure rate is modelled analytically with a positive correlation structure. Furthermore, an integrated failure rate index is derived to model the reliability of electromechanical product in manufacturing by synthetically incorporating overall quality variations with Weibull distribution. A case study on a control board suffering from infant failures in batch production is performed. Results show that the proposed approach could be effective in assessing the infant failure rate and in diagnosing the effectiveness of quality control in manufacturing.     

Optimal concurrent product design and process planning based on the requirements of individual customers in one-of-a-kind production

One-of-a-kind production is a new manufacturing paradigm for producing customised products based on the requirements of individual customers while maintaining the quality and efficiency of mass production. This research addresses the issues in optimal concurrent product design and process planning based on the requirements of individual customers. In this work, a hybrid AND-OR graph is developed to model the variations of design configurations/parameters and manufacturing processes/parameters in a generic product family. Since different design configurations and parameters can be created from the same customer requirements, and each design can be further achieved through alternative manufacturing processes and parameters, co-evolutionary genetic programming and numerical optimisation are employed to identify the optimal product design configuration/parameters and manufacturing process/parameters. A case study is introduced to identify the optimal design configuration/parameters and manufacturing process/parameters of custom window products of an industrial company to demonstrate the effectiveness of the developed method.     

Optimal decision of an economic production quantity model for imperfect manufacturing under hybrid maintenance policy with shortages and partial backlogging

Considering the characteristics of the stochastic shift of the machine state and the uncertainty of the product quality of production, in this paper, we develop an optimisation decision of economic production quantity model for an imperfect manufacturing system under hybrid maintenance policy with shortages and partial backlogging. We assume that the production process is imperfect stemming from the machine reliability and the probability of out-of-control, a hybrid maintenance policy combined of emergency maintenance and preventive maintenance is executed during each production run. Three decision models based on the scenarios of machine breakdown and repair time are developed. The optimal production quantity and maintenance inspection number during each production run are solved with minimising the expected average cost of the system. Numerical examples are used to demonstrate the effectiveness and feasibility of the model. Sensitivity analysis is conducted to analyse the impacts of key parameters on the optimal decision. Some implications related to the effective and economical execution of maintenance policy for practitioners are derived.     

A bi-objective robust inspection planning model in a multi-stage serial production system

In this paper, we present a bi-objective mixed-integer linear programming (BOMILP) model for planning an inspection process used to detect nonconforming products and malfunctioning processors in a multi-stage serial production system. The model involves two inter-related decisions: (1) which quality characteristics need what kind of inspections (i.e. which-what decision) and (2) when the inspection of these characteristics should be performed (i.e. when decision). These decisions require a trade-off between the cost of manufacturing (i.e. production, inspection and scrap costs) and the customer satisfaction. Due to inevitable variations in manufacturing systems, a global robust BOMILP (RBOMILP) is developed to tackle the inherent uncertainty of the concerned parameters (i.e. production and inspection times, errors type I and II, misadjustment and dispersion of the process). In order to optimally solve the presented RBOMILP model, a meta-heuristic algorithm, namely differential evolution (DE) algorithm, is combined with the Taguchi and Monte Carlo methods. The proposed model and solution algorithm are validated through a real industrial case from a leading automotive industry in France.     

Optimisation design of attribute control charts for multi-station manufacturing system subjected to quality shifts

The qualities of products are a major concern in any production system; thus implementing efficient inspection policies is of great importance to reduce quality-related costs. This article addresses the problem of finding optimal inspection policies for the multi-station manufacturing system (MMS) subjected to quality shifts to minimise total quality-related cost. Each station of the MMS may stay at either in-control condition or out-of-control condition, which may lead to different nonconforming product rates. Markov chain method is used to calculate the steady-state probability distribution (SSPD). Based on the SSPD, the cost structure of this MMS is analysed. The economical optimisation model of attribute control charts (ACCs) is then established, in which the decision variables are the control chart parameters: sampling interval, sample size and control limit. The ACCs optimisation model is resolved by the proposed integrated algorithm combining heuristic rule and tabu search. This approach is verified through an application case taken from a mobile phone shell production company. The results of comparative analysis show that the proposed model is much more economical than both the current outgoing inspection strategy and the regular np control chart. The sensitivity analysis of four input parameters is also conducted.     

Research on resources optimisation deployment model and algorithm for collaborative manufacturing process

Agility is the competitive advantage in the global manufacturing environment. It is believed that agility can be realised by networked manufacturing resource optimisation deployment. However, this is a challenge to us now. To solve this question, logical manufacturing unit and logical manufacturing process are proposed to decompose and model the networked manufacturing task, and networked manufacturing resources are organised and modelled based on physical manufacturing unit. During the deployment of manufacturing resources to the task, many factors should be taken into consideration. Of these, manufacturing cost, time and quality are the most important factors. In this paper, before these factors are considered, networked manufacturing resources pre-deployment is carried out to find the candidate manufacturing resources on the basis of manufacturing abilities. Then, resources optimisation deployment is modelled as a multi-objectives optimisation. This optimisation problem is solved based on genetic algorithm after transforming the multi-objectives optimisation problem to a single objectives optimisation problem. Although we may not find the optimal solution for the problem by genetic algorithm, the better and feasible solution is produced. Thus, this algorithm is efficient and can be applicable to practical problem. At last, an illustrative example is presented to show the application of the proposed algorithm.     

Tactical production planning in a hybrid Make-to-Stock–Make-to-Order environment under supply,process and demand uncertainties: a robust optimisation model

In this paper, we consider a hybrid ‘Make-to-Stock–Make-to-Order’ environment to develop a novel optimisation model for medium-term production planning of a typical multi-product firm based on the competencies of the robust optimisation methodology. Three types of uncertainties: suppliers, processes and customers, are incorporated into the model to construct a robust practical model in an uncertain business environment. The modelling procedure is started with applying deterministic linear programming to develop a new multi-objective approach for the combination of multi-product multi-period production planning and aggregate production planning problems. Then, the proposed deterministic model is transformed into a robust optimisation framework and the solution procedure is designed according to the Lp-Metric methodology. Next, using the IBM ILOG CPLEX optimisation software, the proposed model is evaluated by applying the data collected from an industrial case study. Final results illustrate the applicability of the proposed model.     

Computer-based informatization of manufacturing engineering activities

The paper deals with the problem of optimization of a quality inspection process structure. The problem is identified with designing an optimal arrangement of inspection operations in a production process (linkages of the quality inspection process with manufacturing and accessory processes). Three mathematical models for the cases of specialized and versatile inspection stations are formulated. It is shown that the problem can be treated as a particular case of a standard assignment problem. An optimization procedure, grounded on Balas' algorithm, is proposed. A numerical example, based on real data, is given.     

融合生产信息的产品检验不确定度评定

为合理减小测量不确定度对产品合格判定的影响,研究融合生产信息的产品检验不确定度评定。建立融合加工信息和测量信息的贝叶斯信息融合模型,基于被测参数后验分布对测量结果及其不确定度进行重新估计,依据信息融合后的结果进行产品合格判定;提出利用质量控制信息计算贝叶斯先验参数的方法。仿真实验和实例分析结果表明,在产品检验不确定度评定中融入生产信息,可有效扩大产品检验合格区,合理减小合格判定的工作量。     

Applying an electromagnetism-like mechanism algorithm on parameter optimisation of a multi-pass milling process

Multi-pass milling is a common manufacturing process in practical production. Parameter optimisation is of great significance since the parameters largely affect the production time, quality, cost and some other process performance measures. However, the parameter optimisation of the multi-pass milling process is a nonlinear constrained optimisation problem. It is very difficult to obtain satisfactory results by the traditional optimisation methods. Therefore, in this paper, a new optimisation technique based on the electromagnetism-like mechanism (EM) algorithm is proposed to solve the parameter optimisation problem in a multi-pass milling process. The EM algorithm is a population based meta-heuristic algorithm for unconstrained optimisation problems. As the parameter optimisation problem is a constrained problem, the proposed approach handles the constraints of the problem by improving the charge calculation formula combined with the feasibility and dominance rules at the same time. This paper also puts forward flexible cutting strategies to simultaneously optimise the depth of cut for each pass, cutting speed and feed to improve solutions. A case study is presented to verify the effectiveness of the proposed approach. The results show that the proposed method is better than other algorithms and achieves significant improvement.     

Developing a variables repetitive group sampling scheme by considering process yield and quality loss

Acceptance sampling is a useful tool for determining whether submitted lots should be accepted or rejected. With the current increase in outsourcing production processes and the high-quality levels required, it is very desirable to have an efficient and economic sampling scheme. This paper develops a variables repetitive group sampling (RGS) plan that accounts for the process yield (meeting the manufacturing specifications) and the quality loss (variation from the target). The plan parameters are determined by solving a nonlinear optimisation problem. This implies that the plan parameters minimise the average sample number required for inspection and fulfil the classical two-point conditions on the operating characteristic (OC) curve. Besides, this paper investigates the efficiency of the proposed plan and compares it with the existing variables single sampling plan. Tables of the plan parameters for the proposed variables RGS plan are provided and an application example is presented for illustration.     

Impacts of collaborative investment and inspection policies on the integrated inventory model with defective items

For an imperfect production system, to reduce quality-related costs, a manager may consider investing capital in quality improvement. In general, the investment expense in reducing the defective rate of items is often paid by the vendor. On the other hand, the buyer may inspect the product quality as the order is received which implies it incurs an inspection cost. In a supply chain integrated system, to accomplish global optimisation, the vendor and buyer can agree to jointly invest capital to improve the imperfect production processes, and the buyer can remove the inspection programme as the defective rate reaches a certain low-level. Hence, this paper investigates the impacts of collaborative investment and inspection policies on an integrated inventory model with defective items. The objective of this study is to seek the optimal order quantity, shipping times from the vendor to the buyer per production run, and the defective rate that minimise the joint total cost per unit time. An algorithm is developed to find the optimal solution. Several numerical examples are presented to demonstrate the proposed model and solution procedure, and then several management insights are obtained from the numerical examples.     

A multi-period scheduling method for trading-off between skilled-workers allocation and outsource service usage in dynamic CMS

In this paper, a new method is proposed for short-term period scheduling of dynamic cellular manufacturing systems in a dual resource constrained environment. The aim of this method is to find best production strategy of in-house manufacturing using worker assignment (both temporary and skilled workers) and outsourcing, while part demands are uncertain and can be varied periodically. For this purpose, a multi-period scheduling model has been proposed which is flexible enough to use in real industries. To solve the proposed problem, a number of metaheuristics are developed including Branch and Bound; a hybrid Tabu Search and Simulated Annealing algorithms and a hybrid Ant Colony Optimization and Simulated Annealing algorithms. A Taguchi method (L₂₇ orthogonal optimisation) is used to estimate parameters of the proposed method in order to solve experiments derived from the literature. For evaluating the system imbalance in dynamic market demands, a new measuring index is developed. Our findings indicate that the uncertain market demands affects the part allocating which may induce workstation-load variations that yield to cell-load variation accordingly. To solve this problem, two methods are offered. The results show that promoting staff and using freezing technique are promising ways to reduce system imbalance while confronting with the mentioned condition. The outcomes also show the superiority of the proposed hybrid method in providing solutions with better quality.     

Optimisation of burn-in time considering the hidden loss of quality deviations in the manufacturing process

The burn-in test time is an important parameter of the complex batch processing machine scheduling problem. The omission of the loss of quality deviations in manufacturing generates a non-comprehensive and imperfect result in the optimisation of burn-in time, which hinders the identification of proactive and economical optimisation strategies to prevent infant failure in manufacturing. To solve this problem, this study visualises and quantifies for the first time the hidden loss caused by quality deviations in manufacturing and uses it as a newly added constraint to optimise the burn-in time. Firstly, a quality loss model composed of visible yield loss and warranty costs related to measurable but undetectable reliability vulnerabilities is defined. Secondly, the loss effects of growing defects are measured during the burn-in test, and the optimal burn-in time expressed by the proposed quality loss model is traded off between the additional burn-in cost and the decreased quality loss for an acceptable low infant failure rate. Finally, the effectiveness of the proposed optimisation approach is demonstrated using actual data from a control board with a high infant failure rate. Results show that the proposed method can systematically combine the fundamental loss of quality deviations in the optimisation of burn-in time, which supplements the commonly used optimality criteria, with the upstream loss of quality deviations in the form of manufacturing defects.     

Stochastic lot sizing for maximisation of shareholder wealth in make-to-order manufacturing

Current research in production planning focuses mainly on optimising operational objectives, taking little consideration of the primary principle of corporate governance and investor interests. Such approaches often overlook the critical roles of the cost structure and financial position of a firm, rendering the optimisation results unreliable. This paper studies stochastic lot sizing optimisation in make-to-order manufacturing, with an aim to maximise the full investor interests, well known as shareholder wealth. It presents a relatively simple yet reliable lead time model based on probability theory and stochastic processes. Moreover, the impacts of macroeconomic factors are examined to seek potential drivers for shareholder wealth. Theoretical optimality properties are proved to validate the effectiveness of the proposed model in dealing with batch production planning. Numerical examples and analytical results are presented to illustrate the significance of considering such economic and financial constraints and shareholder wealth. These results highlight that the proposed model can help improve shareholder wealth, and that it is a useful tool for examining the potential challenges and opportunities of shareholder wealth creation in production planning.     

A genetic algorithm and queuing theory based methodology for facilities layout problem

Facilities layout, being a significant contributor to manufacturing performance, has been studied many times over the past few decades. Existing studies are mainly based on material handling cost and have neglected several critical variations inherent in a manufacturing system. The static nature of available models has reduced the quality of the estimates of performance and led to not achieving an optimal layout. Using a queuing network model, an established tool to quantify the variations of a system and operational performance factors including work-in-process (WIP) and utilisation, can significantly help decision makers in solving a facilities layout problem. The queuing model utilised in this paper is our extension to the existing models through incorporating concurrently several operational features: availability of raw material, alternate routing of parts, effectiveness of a maintenance facility, quality of products, availability of processing tools and material handling equipment. On the other hand, a queuing model is not an optimisation tool in itself. A genetic algorithm, an effective search process for exploring a large search space, has been selected and implemented to solve the layout problem modelled with queuing theory. This combination provides a unique opportunity to consider the stochastic variations while achieving a good layout. A layout problem with unequal area facilities is considered in this paper. A good layout solution is the one which minimises the following four parameters: WIP cost, material handling cost, deviation cost, and relocation cost. Observations from experimental analysis are also reported in this paper. Our proposed methodology demonstrates that it has a potential to integrate several related decision-making problems in a unified framework.     

数字化检测在MES中的应用研究

讨论了机械制造企业中三维数字化检测的相关技术,进行了基于DMIS标准的数字化测量研究.通过三维检测技术与生产车间制造执行系统(MES)的结合,完成了基于三维轻量化模型的检验工艺管理、检验任务的执行与管理、产品质量数据和状态的可视化管理,以及产品的质量审理和追溯.这些研究实现了机械制造企业检验的自动化和无纸化,达到了对检...     

Pilot production and manufacturing start-up: The case of Volvo S80

This paper is based on the experiences of the Volvo Car Corporation and examines pilot production and manufacturing start-up- processes that are here combined under the rubric final verification of product and production system. The purpose of the paper is to discuss results from the pilot production and the manufacturing start-up of the Volvo S80 model in order to contribute to an understanding of how the process of ramping up production capacity and improving quality is affected by various types of disturbances - disturbances that affect final verification throughput time and efficiency. In addition, drawing on the results of the case study, the paper contributes a framework of concepts for studying and analysing the final verification processes. A case study methodology was employed, using a combination of quantitative and qualitative methods for data collection and analysis. The results show that disturbances that affect performance during the final verification are felt in four areas (materials supply, machinery/equipment, personnel and product concept). It is proposed that, in order to succeed with a rapid and efficient start-up, that production organizations should focus on identifying the sources of disturbances, as early as possible during pilot production, in order to gain control of these during start-up. The means for gaining improved disturbance control is to apply certain principles for designing, planning and controlling the pilot production and the manufacturing start-up phase.     

Long-term predictive opportunistic replacement optimisation for a small multi-component system using partial condition monitoring data to date

The advanced condition monitoring tools and sensors have changed the decision making on maintenance in modern manufacturing. To face the change, an integrated ‘prognostics-replacement’ framework is proposed to optimise the replacement decision from component-level layer into production system-level layer by using condition monitoring data in this paper. Some special situations such as no failure or suspension histories of many of same or similar components for prognosis, etc., are considered. A novel degradation prediction model is introduced and the failure risk of a component is estimated based on its degradation level and service time. A total current-term cost rate function is defined to determine the replacement clusters and time for performing replacement from an integrated and economic view. A conservative window is used to adjust the replacement time and overcome the prognostic results varying at different inspection time in a long task. To optimise the replacement clusters effectively, a random-keys genetic algorithm (GA) based on convex set theory is developed. The proposed framework is validated by different small systems. Two commonly adopted replacement policies are compared. Sensitive analysis is conducted and the results show the outperformance of our proposed framework.