ADVISOR: A computer-aided material handling equipment selection system

This paper develops and describes a systematic computer-assisted methodology for the selection of material handling equipment (MHE). A microcomputer based system called ADVISOR models the material handling equipment selection process and employs information on 77 of the most common equipment types used in material transport, positioning, unit formation, and storage. The system identifies the appropriate equipment type for an application in two stages. At the primary stage, potential equipment types are identified through the use of physical requirements of the material handling activities specified by users. Eligible equipment are ranked according to their normalized accumulated rating and placed in a candidate equipment list. After this, a second stage involving an economic analysis for each eligible equipment is performed. The evaluation criteria provided by ADVISOR include present worth (PW).equivalent uniform annual cost (EUAC), return on investment (ROI), and payback period(PP) methods. Through ADVISOR, the expert-like MHE selection system recommends a ranked set of equipment based on user input data. An example problem is given in the paper to demonstrate the use of the system.     

Manufacturing process selection in engineering design. Part 2: a methodology for creating task-based process selection procedures

Part 1 (A.M. Lovatt, H.R. Shercliff, J. Mater Des., 19 (1998) 205–215) of this paper identified a need for a methodology to help to create selection procedures for specific manufacturing tasks, where the material and processing operation have, to some extent, already been defined. This paper develops such a methodology, and its aims are twofold: firstly to guide the designer through defining the requirements of the design and identifying the attributes of the relevant subset of processes which must be considered; secondly to lead the designer through an evaluation of a design considering both technical viability (including the product quality during processing and in the finished part), and economic viability. A critical development in this work is the incorporation of process modelling into the selection procedure, as a tool for capturing the coupling of material and process characteristics which govern whether the processing and performance targets can be met.     

Combinatorial process and plant design for agile manufacture

The concept of combinatorial process, equipment and plant design is introduced and developed for the specific examples of fluid separations and crystallisation. It is shown that traditional methods of process design may miss options that are identified using the combinatorial approach. New options may lead to novel types of processes and equipment. Application of this methodology is suggested in terms of scanning the multi-dimensional space describing the process, equipment and plant attributes. The new approach is particularly appropriate for the design of agile plants for families of products and where decisions have to be made as how best to re-configure an existing facility to manufacture a new product.     

Rapid tooling route selection and evaluation for sand and investment casting

Today, a number of direct routes using rapid prototyping (RP) processes (fused deposition modelling (FDM), laminated object manufacturing (LOM), stereolithography apparatus (SLA), selective laser sintering (SLS), etc.), as well as indirect routes (RP coupled with secondary or soft-tooling processes like RTV vacuum casting) are available for rapid fabrication of tooling for sand and investment casting processes. Each route is unique in terms of geometric, material, quality and cost characteristics; no comprehensive database of their capabilities has been reported, especially for metal casting applications. The problem of selecting the optimal rapid tooling (RT) route is a complex multi-criteria decision-making problem. This paper describes a systematic approach for RT route selection and planning. A database of RT process capabilities was generated through benchmarking experiments, covering 20 different widely used RT routes (both direct and indirect: two- and three-step processes) involving an impeller pattern. In this approach, RT process route selection involves translating the tooling requirements specified by the casting engineer into weighted tooling attributes using quality function deployment and analytic network process (QFD-ANP), which along with part specifications is used for RT route selection by calculating the overall process compatibility indices. The routes are ranked as per the value of the overall compatibility index. Once the optimal route is selected, process planning is carried out by retrieving a similar process plan using case-based reasoning (CBR). The methodology has been implemented in a software program using Visual C + + programming language in a Windows environment. The methodology is demonstrated and validated with an industrial example of a separator body casting. It has proved to be a robust evaluation and decision-making tool for selecting appropriate tooling route for a given casting based on customer requirements.     

Decision making in health care using robust parameter design with conditions‐based selection of regression estimators

Health care professionals often use regression methods to quantitatively describe the functional relationships between predictors and outcomes. However, little research investigates the appropriateness of tool application for response‐surface‐based design of experiments, the choice of regression estimators under different environments, and the impact of the determination of optimum conditions, or optimal factor‐level settings, to achieve desirable target outcomes. Robust parameter design (RPD) is an established methodology for determining optimum conditions for a process to achieve specified process targets while minimizing variability of the outcomes. Underlying assumptions for RPD modeling and process conditions should be taken into account when selecting a regression estimator for developing fitted models. If these assumptions are incorrect, then a direct use of estimates obtained has the potential to be problematic, and the results may be potentially catastrophic, particularly when applied to the health care field. Many approaches to RPD in existing literature use ordinary least squares to obtain response functions by assuming normality and moderate variability in the underlying process. Given that some biologic processes are often highly variable and inherently asymmetric, a conditions‐based approach for the selection of a regression estimate technique needs to be explored. This paper examines alternative approaches to regression estimation when the process data indicates that asymmetry or a high degree of process variability exists. The performance of select alternative regression methods is compared using Monte Carlo simulation and numerical analysis.     

A screening methodology for improved solvent selection using economic and environmental assessments

Solvents are used in many applications in chemical manufacturing. An ideal solvent exhibits superior solubility, economic, and environmental impact characteristics. There are many approaches developed for identifying the best solvents, including the use of the Hansen solubility parameters, activity coefficients, toxicity properties, flammability characteristics, and methods based on structure–activity relationships. In this paper, a general methodology is developed to perform solvent selection using solubility matching, economic evaluation and environmental assessments. We propose a five-step approach to perform this task: 1.Solubility matching by applying Hansen solubility parameters 2.Process-specific property constraints, such as boiling point and melting point 3.Economic assessment via process simulation to investigate solvent usage, energy consumption, equipment investment and other operating costs 4.Environmental impact assessment based on process simulation and emission estimation 5.Solvent ranking using economic and environmental objective functions A case study is evaluated using this methodology. This methodology provides a general and effective approach to solvent selection in the context of the entire process, balancing economic, environmental, and operational performances. Electronic Publication     

A methodology of integrating affective design with defining engineering specifications for product design

Affective design and the determination of engineering specifications are commonly conducted separately in early product design stage. Generally, designers and engineers are required to determine the settings of design attributes (for affective design) and engineering requirements (for engineering design), respectively, for new products. Some design attributes and some engineering requirements could be common. However, the settings of the design attributes and engineering requirements could be different because of the separation of the two processes. In previous studies, a methodology that considers the determination of the settings of the design attributes and engineering requirements simultaneously was not found. To bridge this gap, a methodology for considering affective design and the determination of engineering specifications of a new product simultaneously is proposed. The proposed methodology mainly involves generation of customer satisfaction models, formulation of a multi-objective optimisation model and its solving using a chaos-based NSGA-II. To illustrate and validate the proposed methodology, a case study of mobile phone design was conducted. A validation test was conducted and the test results showed that the customer satisfaction values obtained based on the proposed methodology were higher than those obtained based on the combined standalone quality function deployment and standalone affective design approach.     

Manufacturing process selection in engineering design. Part 1: the role of process selection

Whilst material selection methods are becoming mature, the selection of processes has been neglected — particularly after the preliminary design stages. This paper presents a view of current approaches to selection issues in engineering design with particular reference to process selection techniques. The need is highlighted for more focused selection techniques once a manufacturing task has been identified. A new methodology to aid the creation of task-based process selection procedures is outlined in Part 2 (A.M. Lovatt, H.R. Shercliff, J. Mater. Des., 19 (1998) 217–230).     

Functional characterisation of mechanical joints to facilitate its selection during the design of open architecture products

New trends in product design require the use of modularity as key feature aimed to improve functional performance and the generation of open architecture products. For mechanical systems, one of the challenges during early design stages of these products involves the proper selection of joining methods among their constructive components. A robust joint selection process must consider product requirements, life cycle analysis and eventual procedures for assembly and disassembly. However, the general approach towards a Design-for-Assembly (DFA)/Design-for-Disassembly (DFD) only considers design, manufacturing and in some cases final disposal stage. Additionally, most of the works found in the literature are merely focused on assembly operations, disregarding economic and environmental benefits from optimising disassembly complexity. Herein, a functional characterisation of mechanical joint methods for the assembly and disassembly activities that take place throughout the product life cycle is proposed, focusing on open architecture products. Additionally, a classification of joining methods, a joint complexity metric valuation and a selection process are proposed for the conceptual design stage. The approach integrates both DFA and DFD principles in a formal methodology. The proposed selection roadmap can be implemented to increase product sustainability positively regarding resources optimisation, operational time and costs in reuse, remanufacturing and recycling tasks.     

A quality function deployment-based model for materials selection

Materials play a key role during the entire product design and manufacturing phase as a wrongly selected material may often lead to premature product failure causing loss of revenue and repute of the concerned manufacturing organization. While selecting the most suitable material for a specific application, the designers often need a sound and systematic methodology to deal with this problem having multiple candidate alternative choices and conflicting objectives. Most of the previously applied methodologies for material selection have either adopted criteria weights estimated using subjective judgments of the designers or failed to give due emphasis on the voice of the customers to meet their requirements. In this paper, a maiden venture is taken to solve the material selection problems using a quality function deployment (QFD)-based approach that can integrate the voice of the customers for a product with its technical requirements. The applicability and solution accuracy of this QFD-based material selection model is demonstrated with the help of four illustrative examples. To ease out the materials selection decision-making process, a user-friendly software prototype in Visual BASIC 6.0 is also developed.     

60000m~3/h空分设备汽轮机直接空冷系统选型及优化介绍

结合国内外先进凝汽系统技术水平,根据昊华国泰40万t/a甲醇项目实际建设情况,确定60000 m3/h空分设备汽轮机配套直接空冷系统的流程和配置。介绍直接空冷系统的技术要求和设备部件要求,阐述60000 m3/h空分设备汽轮机直接空冷系统的选型优化。     

Robot selection using an integrated approach based on quality function deployment and fuzzy regression

The decision-makers have been experiencing difficulties in determining the most suitable robot alternative due to the increase in number of robots and the diversity in their application areas. A robust decision framework for robot selection should consider multiple and conflicting criteria and the dependencies among them. This paper introduces a decision model for robot selection based on quality function deployment (QFD) and fuzzy linear regression. The proposed approach benefits from the fact that QFD focuses on delivering value by taking into account the customer requirements and then by deploying this information throughout the development process, and applies this perspective to robot selection. Fuzzy linear regression is considered as an alternative decision aid for robot selection problems where imprecise relationships among system parameters exist. An example robot selection problem is presented to illustrate the proposed decision-making approach.     

Optimal supply contract selection

Selection of supply contracts is a critical decision faced by manufacturing firms in a variety of industries. Manufacturing firms often have the option of selecting from several types of supply contracts that include long-term, medium-term, and short-term contracts. While extant literature has stressed the importance of such contracts, few methodologies have been proposed for optimally selecting contracts under various business conditions. To this end, this paper proposes a methodology for optimal contract selection based on a mixed-integer programming approach. We present specific insights to manufacturing managers on choosing the right contracts in the presence of market price uncertainty, supplier discounts, investment costs, and supplier capacity restrictions.     

Training needs analysis—a human factors analysis tool

Human factors (HF) engineers have contributed to the reduction of human error, by improving user performance through the design of the user interface. However, there still remains a requirement for user training. This is especially true for complex, high risk processes employing new technology. Reduced manning and downskilling are putting increased pressures on users who, in many activities, still remain the last level of safety protection. In responding to this need, HF engineers now employ a range of tools and methods during the design process, which assist in identifying the training media and training design. Training needs analysis (TNA) offers a structured methodology to facilitate the information gathering process and identify any shortfall between current user performance and that demanded by new technology and designs. By the use of these methods and tools the potential for human error can be reduced, for the following reasons: (a) improved user performance (b) better designed and more efficient training systems. Existing TNA methods have been better suited to the study of existing equipment. This paper describes the training requirements process, and proposes a usable methodology for contractors conducting a TNA in the design of new systems/equipment. It is of particular use where information on the operation or maintenance may be limited.     

Solvent selection guide: a guide to the integration of environmental, health and safety criteria into the selection of solvents

 A practical, easy to use Solvent Selection Guide has been developed to provide a concise source of selected information and guidance to chemists and engineers to assist with their selection of solvents. It provides a relative ranking, and is limited by design to the inherent environmental, health and safety issues, in combination with general and specific process and facility issues associated with each solvent. These represent areas that are not always considered by R&D chemists and engineers during normal product or process development. The guide is intended to augment existing processes that mostly consider only technical, cost and regulatory aspects so that chemists and engineers may make more broadly considered solvent selections early in the chemical development process. The Guide currently includes a total of 35 solvents which were most commonly used in SmithKline Beecham (SB) Research and Development and manufacturing activities over the past three years, although the methodology used to develop the guide is readily applicable to other solvents. Detailed guidance is provided in a manual, and is summarized within charts which relatively rank and identify solvents and key issue areas, provide information on the new International Conference on Harmonization (ICH) guidelines for residual solvents in final pharmaceutical products, and supply data for co-solvent selection. The latter chart enables a chemist or engineer to choose solvents based on the ease of separation, which maximizes solvent utilization, recovery, and re-use. There is also a summary sheet for each solvent which reviews the scores and major issues for all the key categories used to develop the Environment & Safety Guide and provides essential solvent property data. Received: 5 November 1998 / Accepted: 11 February 1999     

Product end-of-life options selection: grey relational analysis approach

Management of the product end-of-life (EOL) for a manufacturing enterprise is important. An improper EOL strategy can negatively affect the productivity and profitability and undermines the reputation of an enterprise because of the growing demands for extended producer responsibility. Producers need tools and methods to evaluate each EOL option since it is a complicated but must-be-accomplished task to achieve in order to solve the multiple-criteria problem that combines aspects such as eco-system quality, environmental impacts, human health issues, and economic factors etc. This paper presents an alternative decision-making process to generate an optimal solution from a list of EOL options under the uncertainty condition of incomplete information. Using Grey Relational Analysis, the multi-criteria weighted average is proposed to rank the product EOL options with respect to several criteria at the material level. It will guide the selection process and help a decision-maker solve the selection problem. The method is demonstrated with an example. Various EOL options are evaluated using the developed multicriteria methodology that takes account the environmental, economical and social factors.     

Facilities layout planning based on Fuzzy multiple criteria decision-making methodology

Space requirements for facilities and the activity relationships among these facilities are important factors in determining the design of a facility layout. A facility layout problem is an unstructured decision problem. One of the real difficulties in developing and using models for layout design is the natural vagueness associated with the inputs to the models. The personnel flow rate between different departments should be viewed as vague inputs. The analyst is typically uncertain about what this input should be, yet the formulation requires exact inputs. Similarly, arbitrary numerical ratings are assigned for the relationship chart. This paper presents a distinct methodology for the facility layout process using a fuzzy decision-making system for handling inexact, vague data. The selection routine for the placement of facilities (departments) in an open continual plane is developed by using a multifactor fuzzy inference system. It considers both qualitative and quantitative factors that influence the layout structure. A two-tier fuzzy inference system is proposed to compare the proposed layout methodology with that of a conventional selection routine with respect to personnel flow cost, dead space and the minimum required area of the layout. The suggested distinct methodology is coded in C⁺⁺ language and implemented in a personal computer. The experimental results for a test problem with six departments, 30 activities (moves) and four influencing factors are illustrated with encouraging results under a fuzzy multicriteria environment.     

A decision support system for selection of net-shape primary manufacturing processes

Developments in the capabilities of the manufacturing processes increased the number of processes that can produce a part within the requirements determined by its design and market research. The increased number of processes and unfamiliarity of manufacturing engineers to many new manufacturing processes forces the researchers to develop systematic process selection tools instead of depending on the accumulated human expertise only. In this paper, a net-shape primary manufacturing process selection decision support system (DSS), which is named PROSEL (PROcess SELection), is developed. The developed selection programme eliminates the unsuitable processes step by step by checking a part’s material, annual production quantity, specified shape, thickness and presents the most economical process as the most appropriate net-shape primary process after a final cost analysis. The developed DSS is written in Visual Studio and tested with a great deal of real-life examples. It can be concluded from the tests that the programme provides the same or better primary manufacturing process selection decisions than the practical usage, and it is a very useful support tool for net-shape primary process selections.     

Application of fuzzy logic in the selection of part orientation and probe orientation sequencing for prismatic parts

With an increase in complex designs and tighter tolerances, the Coordinate Measuring Machine inspection process has become increasingly more advanced. By inspection planning, design data can be transferred to an inspection system and an entire inspection operation can be carried out with a minimum of time and with reduced uncertainty. The current need is to automate this process completely so that the inspection plan can be generated directly from the design information. Two modules of inspection planning, i.e. selection of part orientation and probe orientation sequencing, have not been dealt with properly. Also, some important factors for the selection of part orientation have been neglected and proper weights have not been given to the probe-orientation sequencing criteria. An attempt was made to overcome these limitations. Both problems have been approached as the ranking of a number of alternatives based on multiple criteria, where each criterion has unequal importance. To get the optimum probe-orientation sequence and stable part orientation, fuzzy logic was applied. Fuzzy sets were obtained and combined using a suitable methodology. To explain and validate the proposed methodology, an example part was taken. As a practical case, an engine block was considered and the results presented.