The objective of this research study is to develop a set of expert systems that can aid metal manufacturing facilities in selecting binder jetting, direct metal laser sintering, or CNC machining based on viable products, processes, system parameters, and inherent sustainability aspects. For the purposes of this study, cost-effectiveness, energy, and auxiliary material usage efficiency were considered the key indicators of manufacturing process sustainability. The expert systems were developed using the knowledge automation software Exsys Corvid^®V6.1.3. The programs were verified by analyzing and comparing the sustainability impacts of binder jetting and CNC machining during the fabrication of a stainless steel 316L component. According to the results of this study, binder jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC machining, considering the fabrication of components feasible for each technology.

This research focuses on the benchmarking of electrical energy use at sawmills. Lean energy (a programmatic concept similar to lean manufacturing) is facilitated by this research, which furthers the understanding of energy intensity at sawmills, leading to reductions in operating costs and increases in profitability. A user-friendly interactive model, Baseline Electrical Energy Consumption in Wood Processing Sawmills (BEECWPS), was developed to enable the user to create an energy profile based on sawmill process dynamics and to provide information about product, process, and system parameters. These parameters were gathered by visiting six sawmill facilities in West Virginia. A detailed methodology was developed to collect the field data and determine an energy intensity. The proposed model has the capability of developing the energy benchmark for a facility under consideration, upon successful submission of required data, and will aid lean energy initiatives within the organization. Manufacturing engineers can use BEECWPS to effectively identify the effect of product, process, and system parameters on energy consumption and intensity to revise production system strategies.

The concept of ‘do it right the first time’ in the machining industry not only expects the best quality products but also at the best possible cost. The cost of machining depends on intelligent process planning and selection of machining parameters such as speed, feed, and depth of cut. The problem of machining parameter selection has received great attention by researchers and many techniques have been developed. A review of these techniques reveals that the selection of the machine and cutting tool is done before the process of cutting parameter selection and process sequencing, and often the selection is based on experience. The current research is an attempt to develop an integrated model (ExIMPro: Expert system based Integrated model for Machining Processes) which finds the sequence of operations with set of machines, tools, and other process parameters to minimise the cost of machining for a cylindrical part. This system consists of existing expert system Machining Parameter SELection (MPSEL) for machine and tool selection and a Microsoft Excel® and Visual Basic® based parameter selection model. The present model focuses on turning and cylindrical grinding operations but other processes can be incorporated with little modification to the software.